Engineering Asset Management 2011

The landscape of maintenance and asset management has been reshaped as key technology enablers that are making a significant impact on everyday applications. The growing maturing of web-based and semantic maintenance, the ubiquity of mobile and situated computing, and the lowered costs and increased capabilities of wireless sensing and identification technologies are among the enabling technologies having the most significant impact. They are recognized as the key constituents of eMaintenance, the technological framework that empowers organizations to streamline their asset management services and data delivery across the maintenance operations chain. This paper takes a look at these key, contributing technologies, alongside their adoption prospects and current hurdles preventing the wider penetration of eMaintenance in industry.

The number of offshore wind turbines installed in the seas around Britain’s coasts is likely to increase from just fewer than 150 to 7,500 over the next 10 years with the potential cost of £10 billion. Operation and Maintenance activities are estimated to comprise 35 % of the cost of electricity. However, the development of appropriate and efficient maintenance strategies is currently lacking in the wind industry. The current reliability and failure modes of offshore wind turbines are known and have been used to develop preventive and corrective maintenance strategies which have done little to improve reliability. Unplanned maintenance levels can be reduced by increasing the reliability of the gearbox and individual gears through the analysis of lubricants. In addition, the failure of one minor component can cause escalated damage to a major component, which can increase repair and or replacement costs. A Reliability Centered Maintenance (RCM) approach offers considerable benefit to the management of wind turbine operations, since it includes an appreciation of the impact of faults on operations. Due to the high costs involved in performing maintenance and the even higher costs associated with failures and subsequent downtime and repair, it is critical that the impacts are considered when maintenance is planned. The paper will provide an overview of the application of RCM and on line e-condition monitoring to wind turbine maintenance management. Finally, the paper will discuss the development of a complete sensor-based processing unit that can continuously monitor the wind turbine’s lubricated systems and provide, via wireless technology, real-time data enabling onshore staff the ability to predict degradation, anticipate problems, and take remedial action before damage and failure occurs.

Derailment is a business risk for rail operators. However, most of the derailments are due to a combination of several causes. This paper presents a derailment study, focusing on Australia, that categorizes a comprehensive set of factors causing derailment and current mitigation techniques. Finally, a risk gap is analysed and integrated model is proposed. Further, the opportunities to incorporate remote monitoring and data analysis technologies to ensure an eMaintenance management of derailment are discussed. A case study on eMaintenance of rail lubrication, which is highly related to derailment, is also demonstrated in this paper.

Achieving business excellence within industries which utilize complex technical systems requires effective and efficient maintenance. Maintenance is an important enabler of business performance. An effective maintenance strategy creates additional values in an organization’s value-generating process. Establishing an effective and efficient maintenance process is highly dependent on supporting Information and Communication Technology (ICT) infrastructure for information logistics that facilitates maintenance decision support. eMaintenance solutions facilitate effective and efficient management and control of maintenance activities through an enhanced utilization of computing; like estimation of Remaining Useful Life (RUL); reduction of No-Fault Found (NFF); and prediction of fault. eMaintenance solutions enable a seamless integration and fusion of information services provided by information intensive systems with embedded components in order to manage increasing and extensively distributed real-time data collected via different data sources (e.g., sensors). Since, the emerging ‘Internet of Things’ is expecting to dramatically change information systems with inherent embedded components, eMaintenance solutions need to be adapted to this new context to fulfill the overall business requirements on an effective and efficient decision-making process; for e.g.,: real-time analysis based on real-time data and context-aware information logistics. However, development and establishment of proper eMaintenance solutions can be facilitated through utilization of an appropriate framework which deals with real-world challenges. This paper explores some of the issues and challenges pertaining to eMaintenance in industrial applications.

Increased environmental awareness in industry combined with the globalized market economy has created an increase in demand for sustainable and efficient resource utilization. In this context, maintenance plays a critical role by linking business objectives to the strategic and operational activities aimed at retaining system availability performance, cost-efficiency, and sustainability. Performing maintenance effectively and efficiently requires corresponding infrastructure for decision-support provided through eMaintenance solutions. A proper eMaintenance solution needs to provide services for data acquisition, data processing, data aggregation, data analysis, data visualization, context-sensing, etc. For Quality of Service (QoS) in eMaintenance solutions, the performance of both system-of-interest, enabling systems, and related processes have to be measured and managed. However, the QoS has to be considered on all aggregation levels and must encompass the aspects of Content Quality (CQ), Data Quality (DQ), and Information Quality (IQ). Hence, the purpose of this paper is to study and describe some aspects of DQ in eMaintenance related to the process industry in northern Europe.

Availability simulation modeling is a tool used by industry to assist maintenance decision-making. However, it can be time-consuming to conduct because of poor data quality and the complexity of real systems. In this paper, an availability simulation modeling approach that is suitable to the needs of industry is developed which balances the need for (a) an accurate reflection of system availability performance, and (b) efficiency in development. The approach was tested using data from a gold processing plant and considered 802 assets. The approach is used to construct two reliability block diagrams (RBD), a high level RBD at equipment level and a lower level at component level. The component level model indicates that the roasting system availability is at 94 %, within 3 % of the actual plant downtime; compared to 83 % for the equipment level model. These values are inclusive of equipment failures, as well as, corrective, and preventive maintenance tasks. The component model predicts that 50 % of system unscheduled downtime is caused by 14 failure modes. Potential production revenue savings of 460 h per year exists if these failure modes are addressed. The results suggest that the component-level approach is recommended although the time to construct the model was 8 weeks compared with 5 weeks for the equipment level model. This paper makes suggestions for how to improve the efficiency of development using production time-delay data. Usually maintenance work orders are used for process plant availability simulation studies. The alternative data set was found to improve modeling efficiency by approximately 80 %.

This paper focuses on consider the optimization of maintenance plans for a system of assets with failure/degradation interaction between the assets. The asset system under consideration in this paper has

M

identical non-critical machines feeding their output to a critical machine. A common repair team performs maintenance on all the machines. All the machines deteriorate over time independently. In addition to the independent degradation, the performance of the non-critical machine also affects the degradation of the critical machine. We develop a mathematical model to represent the interactions and performance of the asset system. We also provide a simulation-based numerical solution to optimize the maintenance plan for the system outlining the maintenance intervals for each of the machines.

Correlation between the quality degradation of a product and maintenance of a machine is often established by the production engineers. To asses this correlation, some assumptions are made. In most cases it is assumed that the quality of the product degrades after a fixed number of operation cycles of the production machine. Therefore, maintenance of the production machine is only performed after this number of cycles is accomplished. This kind of assumptions is often not valid in modern industry since high variability of products, tolerances of machines/components, reliability variations of these components, extensive/smooth usage, etc., make this degradation quite dynamic in time. As a result, the quality of the product could get degraded in a fast way if this variability is high or in a slow way if this variability is low. Both cases will lead to low benefit because of lost production in the former case or redundant maintenance in the latter one. In this paper, we propose a solution to this problem by maximizing the benefit using online monitoring of product’s quality degradation and maintenance cost evolution. A Condition Based Maintenance framework for industry developed in Prognostics for Optimal Maintenance (POM) project [

1

] and described in [

2

] is applied to two industrial use cases in order to deploy and validate the proposed technique.

Linear assets are engineering infrastructure, such as pipelines, railway lines, and electricity cables, which span long distances and can be divided into different segments. Optimal management of such assets is critical for asset owners as they normally involve significant capital investment. Currently, Time Based Preventive Maintenance (TBPM) strategies are commonly used in industry to improve the reliability of such assets, as they are easy to implement compared with reliability or risk-based preventive maintenance strategies. Linear assets are normally of large scale and thus their preventive maintenance is costly. Their owners and maintainers are always seeking to optimize their TBPM outcomes in terms of minimizing total expected costs over a long term involving multiple maintenance cycles. These costs include repair costs, preventive maintenance costs, and production losses. A TBPM strategy defines when Preventive Maintenance (PM) starts, how frequently the PM is conducted and which segments of a linear asset are operated on in each PM action. A number of factors such as required minimal mission time, customer satisfaction, human resources, and acceptable risk levels need to be considered when planning such a strategy. However, in current practice, TBPM decisions are often made based on decision makers’ expertise or industrial historical practice, and lack a systematic analysis of the effects of these factors. To address this issue, here we investigate the characteristics of TBPM of linear assets, and develop an effective multiple criteria decision making approach for determining an optimal TBPM strategy. We develop a recursive optimization equation which makes it possible to evaluate the effect of different maintenance options for linear assets, such as the best partitioning of the asset into segments and the maintenance cost per segment.

Road asset managers are overwhelmed with a high volume of raw data which they need to process and utilize in supporting their decision making. This paper presents a method that processes road-crash data of a whole road network and exposes hidden value inherent in the data by deploying the clustering data mining method. The goal of this method is to partition the road network into a set of groups (classes) based on common data and characterize the crash types to produce a crash profile for each cluster. By comparing similar road classes with differing crash types and rates, insight can be gained into these differences that are caused by the particular characteristics of their roads. These differences can be used as evidence in knowledge development and decision support.

A novel

m

-ary tree-based approach is presented to solve asset management decisions which are combinatorial in nature. The approach introduces a new dynamic constraint-based control mechanism which is capable of excluding infeasible solutions from the solution space. The approach also provides a solution to the challenges with ordering of assets decisions.

This paper presents the derivation of the probability distribution of maintenance cost of a repairable system modeled as an alternative renewal process and subjected to an age-based replacement policy. The key idea of the paper is to formulate a renewal equation for computing the characteristic function of the maintenance cost incurred in a fixed time interval. Then, the Fourier transform of the characteristic function leads to the complete probability distribution of cost. This approach also enables the derivation of probability distributions of the down time and the number of failures in a given time period. The distribution of the cost can be used to evaluate the value at risk (VaR) and other measures needed for the financial planning of a maintenance program.

Sustainable management of community buildings is a challenging task in Australia. Maintenance and renewal of building assets is a prominent issue as a large number of buildings owned by local councils were built in 1970s and become aged and deteriorated. Each building consists of a massive load of building components which adds complexity into their management. Limited asset management models in favor of buildings’ decision making have further widened the gap in finding a reliable decision-making model for building maintenance and renewals. On the other hand, a majority of asset management models available are unable to cope with the uncertainty associated with the data collection which makes the results inconsistent and subjective. This paper presents a useful tool minimizing aforementioned problems and making asset planner’s life easier to prioritize maintenance actions. The model is a multi-criteria decision-making model (MCDM) which is combined with two analytical tools, i.e., analytical hierarchical process (AHP) and fuzzy inference system (FIS). The model is based on a four level hierarchical structure, which includes a goal, aspects, criteria, and attribute factors representing the level one to level four in the hierarchy respectively. Decision Criticality Index (DCI) has been introduced in order to understand the importance of the decision. The concept which is used in the traffic light system is adapted to categorize maintenance options according to color codes depending on DCI value range and the duration of maintenance plan. Example calculations based on case study and hypothetical data have been demonstrated throughout the paper to showcase and validate the model.

Most products are affected by multiple stresses simultaneously, so it is necessary to study the optimal method for accelerated degradation testing (ADT) with multiple stresses. This method is proposed for constant stress ADT (CSADT). First, uniform orthogonal test theory is used to determine the combined mode of different stresses. Then stochastic process is used to model the perform degradation of products. Under the constraint of the total experimental cost, the optimum problem is established with the objective that minimizing the asymptotic variance of the estimation of the reliability of the

p

th quantile of product’s lifetime under use condition. Optimal test variables are given, including: levels of each stress, total sample size and testing time, and sample size and testing time at each stress combination. Finally, simulation examples are presented to illustrate the proposed method.

This paper presents a group maintenance scheduling case study for a water distribution network. This water pipeline network presents the challenge of maintaining aging pipelines with the associated increases in annual maintenance costs. The case study focuses on developing an effective pipeline replacement planning for the water utility. Replacement planning involves large capital commitment and can be difficult as it needs to balance various replacement needs under limited budgets. A Maintenance Grouping Optimization (MGO) model based on a modified genetic algorithm was utilized to develop an optimum group maintenance schedule over a 20 year cycle. An adjacent geographical distribution of pipelines was used as a grouping criterion to control the searching space of the MGO model through a Judgment Matrix. Based on the optimum group maintenance schedule, the total cost was effectively reduced compared with the schedules without grouping maintenance jobs. This optimum result can be used as a guidance to optimize the current maintenance plan for the water utility.

In this paper we develop and discuss a prognostics model to estimate the Mean Residual Life of Rail Wagon Bearings within certain confidence intervals. The prognostics model is constructed using a Proportional Hazards Model approach informed by imperfect data from a bearing acoustic monitoring system and related failure database. We have been able to predict failure within a defined maintenance planning window from the receipt of the latest acoustic condition monitoring information. We use the model to decide whether to replace a bearing or leave it until collection of the next condition monitoring indicators. The model is tested on a limited number of cases and demonstrates good predictive capability. Opportunities to improve the performance of the model are identified.

Acoustic Emission (AE) Method is widely used in research for machinery diagnostics, and wavelet transform has been implemented in many applications in the condition monitoring of machinery. In contrast to previous applications, this paper examines whether the acoustic signal can be used effectively to detect the fault in rolling element bearing using discrete wavelet transform (DWT). A novel mother function for DWT was extracted through acoustic signal measured by the fatigue crack growth test. A commonly encountered fault was simulated. The results suggest that DWT applied using the novel mother function is an effective for the detection of fault and may provide a powerful tool to indicate the faults in rolling element bearing.

The acoustic emission (AE) technique is a well-known non-destructive test which is widely applied in research and industry. It is mainly used in monitoring the onset of the cracking process in materials to predict and prevent the factures that attract the attention of researchers to apply condition monitoring techniques into detecting the early stage of cracks. The object of this study is to obtain the features of AE signals caused by crack growth. The envelope analysis with discrete wavelet transform (DWT) is used to find the characteristic of AE signal. To estimate the features, the crack length was divided into three parts. From the raw signals and transferred signals, the power spectrum which was processed by the envelope analysis with DWT was generated. Through this experiment, the envelope analysis with DWT is a better way to analyze the original AE signals. In this paper, the results of fatigue crack growth can contribute to a database of crack detection.

There are many facets of a prognostics and health management system. Facets include data collection systems that monitor machine parameters; signal processing facilities that sort, analyze, and extract features from collected data; pattern matching algorithms that work to identify machine degradation modes; database systems that organize, trend, compare, and report information; communications that synchronize prognostic system information with business functions including plant operations; and finally visualization features that allow interested personnel the ability to view data, reports, and information from within the intranet or across the Internet. A prognostic system includes all of these facets, with details of each varying to match specific needs of specific machinery. To profitably commercialize a prognostic system, a generic yet flexible framework is adopted which allows customization of individual facets. Customization of one facet does not materially impact another. This chapter describes the framework, customization process, and choices of commercial system components.

Dense phase pneumatic conveying is critically dependent on the physical properties of the materials to be conveyed. However, many materials, such as alumina and coarse fly ash, which are highly abrasive, do not have dense phase conveying capacity. Bypass pneumatic conveying systems provide a dense phase capability to non-dense phase capable bulk materials. These systems also provide the capacity of lower the conveying velocity and therefore lower pipeline wear and lower power consumption occurs. The objectives of this work were to study the energy consumption and wear of bypass pneumatic transport systems. Pneumatic conveying of alumina experiments were carried out in a 79 mm diameter main pipe with a 27 mm inner diameter bypass pipe with orifice plate flute arrangement. High-speed camera visualizations were employed to present flow regimes in a horizontal pipe. The experimental result showed the conveying velocity of bypass system is much lower than that of conventional pipelines; thus, specific energy consumption in the conveying process is reduced. The service life of the bypass line has also been estimated.

Distributed industrial systems present increasing risk as assets age. For systems such as pipelines and utility corridors, the cost of inspection to mitigate this risk needs to be controlled without compromising reliability. Major elements of remote inspection cost and effectiveness are the number of personnel, where they are located, inspection quality control, and travel time. One approach to reduce such costs is to use robotic systems for information gathering and preliminary feature extraction to detect anomalies and identify faults and their location. A combination of aerial and terrestrial robots can be deployed to cover the territory of interest and collect information necessary to extract features of interest in a timely manner. A system conceptual design is reviewed, and specific elements for a robotic mission to monitor the integrity of a pipeline and characteristics of a mine tailings structure are presented and discussed, with options for condition indicator data collection and feature extraction. Strategies are discussed for ensuring that the robotic inspection system itself has high reliability. Preliminary development and testing results for two prototype robotic systems are presented.

Nowadays, energy has become a key issue in all sectors of industry. Analytical tools in the prognostics and health management (PHM) area are needed to transform energy and related data into actionable information. The IMS Center has developed corresponding solutions for energy generation, storage, and usage applications. The Watchdog Agent® toolbox techniques are applied in a systematic way in each application to address the development of advanced predictive tools for near-continuous uptime of energy generating assets; mobility readiness and safety for next-generation electric vehicles via the Smart Battery Agent; and the application of low-cost, nonintrusive predictive solutions using equipment energy consumption. In this paper, methodologies and case studies in the three categories are presented.

Electronic products are continually developed as well as redesigned to improve performance and enhance reliability. These products are required to meet reliability requirements of the customer. Traditionally, reliability of products has been ensured by physical testing, which can be time consuming and result in design change iterations delaying product introduction or delivery. To reduce time to market, improve reliability, and product development costs, simulation techniques that can replicate physical tests can be used. Simulation must include consideration of product response to anticipated use and test conditions, as well as identification of failure mechanisms and failure sites including estimated time to failure. In this paper, virtual qualification using simulation assisted reliability assessment (SARA

®

) approach is introduced. Failure mechanisms of electronic products are discussed and methods for estimating time to failure are provided. Finally, a case study on the life assessment of a commercially available flash drive is presented. The predicted results are compared to experimental test results to determine the accuracy of the approach.

Continuous monitoring of diesel engine performance is critical for early detection of fault developments in the engine before they materialize and become a functional failure. Instantaneous crank angular speed (IAS) analysis is one of a few non-intrusive condition monitoring techniques that can be utilized for such tasks. In this experimental study, IAS analysis was employed to estimate the loading condition of a 4-stroke 4-cylinder diesel engine in a laboratory condition. It was shown that IAS analysis can provide useful information about engine speed variation caused by the changing piston momentum and crankshaft acceleration during the engine combustion process. It was also found that the major order component of the IAS spectrum directly associated with the engine firing frequency (at twice the mean shaft revolution speed) can be utilized to estimate the engine loading condition regardless of whether the engine is operating at normal running conditions or in a simulated faulty injector case. The amplitude of this order component follows a clear exponential curve as the loading condition changes. A mathematical relationship was established for the estimation of the engine power output based on the amplitude of the major order component of the measured IAS spectrum.

There are many difficulties in statistical analysis of multi-stress accelerated life testing, such as establishing the accelerated model and solving pluralism likelihood equations. With a focus on these difficulties, the Grey-SVM based life and reliability prediction method for multi-stress accelerated life testing is proposed, with the accelerated stress level and the reliability as SVM inputs, and the corresponding Grey AGO processing failure data as outputs. Simulation and case study shows that the method has high prediction accuracy and with less amount of training samples than neural network.

Dynamic fault tree analysis has been widely applied to the reliability and safety analysis of various fault tolerant systems. Modern equipment has become larger, more complex and more intelligent; the reliability of this equipment not only impacts the safe operation of personnel and property, but also has important social and economic significance. Based on the improved dynamic fault tree analysis method, this paper analyzes the reliability of oil pump units within a pump station. This paper gives three criteria for ranking the basic events in a Binary Decision Diagram and a comprehensive dynamic fault tree. This method is then put into practice in the reliability analysis of pump units. The result indicates that the improved method used for reliability analysis of oil pump units in a pump station has a simple modeling process, and low computational complexity.

Traditional reliability assessment methods predominantly based on probability and statistical theories need a large sample size to get the general, overall estimates for the identical units. However, the sample is often insufficient for large machinery, traditional reliability assessment methods became ineffective. More importantly, traditional reliability assessment cannot reflect the individuality of the running equipment. To overcome these deficiencies, a new approach based on running condition information to assess the operation reliability of large machinery is proposed. First, according to “short board effect”, the critical and weak component of the equipment is determined. Second, based on quantitative damage diagnosis, a mapping function called reliability membership function is built between equipment damage severity and a new reliability index which is defined as membership reliability. Third, quantitative damage diagnosis is employed to achieve the damage severity feature of the key component. Finally, by substituting the feature into the function, the value and change trend of the membership reliability is calculated and the equipment’s operation reliability assessment is gained. The application to the reliability assessment of the bearing for large machinery demonstrated the proposed method is reasonable and effective. Moreover, the proposed approach provides a new way to the operation reliability assessment for large machinery.

Achieving fleet-wide management and decision-making support raises challenges that still have to be addressed. Toward this end, new methodologies, methods and tools are required to identify the related health indicators, support continuous monitoring, enable predictive diagnosis, and provide suitable prognosis facilities in order to carry out proactive health management. In this paper, an approach to proactive fleet health monitoring and management is proposed. The underlying modeling methods are introduced as well as health monitoring facilities for proactively reacting to system performance drift.

Development of successful service concepts is a prerequisite for success. Developing business through service excellence is one way of surviving competition while improving customer service and loyalty. Service value is created in a network context, and the structure and dynamics of the value network and customer expectations influence the complexity of service delivery. This needs to be taken into account when considering the management of risks. The traditional emphasis on of risk management has been on protecting the system, and its users, from the failures in the system. When considering the performance of a system in its larger commercial and political environment, uncertainty may provide opportunities as well as threats. The key research question in this paper is how to develop new service business and manage risks in service value networks taking into account both threats and opportunities related to the services’ value creation.

Asset Management is a concept that has a very wide range of uses and different levels of maturity across diverse industrial sectors and regions. Because of this, there is lots exists a large amount of variation in the concepts and the terminology used. In discussing the concept of asset management, the asset management professionals tend to adhere to their own definitions. This situation is an impediment to bringing the concept further, and can mostly be attributed to the fact that framing an improvement in one specific approach or terminology may render it virtually inapplicable in another context. In other words, quite comparable approaches and concepts have to be invented over and over again. To prevent the chaos and in order to provide the basis for a unified resolution, a system level reference model for translating improvements into other realms would be very helpful. In this paper, the outline of such a reference model is developed.

Sustainability has attracted the attention of many researchers and organizations. There is a Europe-wide interest in measuring sustainability performance. A standardized framework to determine sustainability performance of dispersed production clusters, however, has yet to be found. Existing initiatives either focus on partial aspects of sustainability or do not support sustainability considerations for complex value networks but rather focus on the organizational level. This paper discusses some problems related to existing initiatives and introduces

SustainValue

, a project that responds to the known shortcomings and that aims at the creation of a governing framework for sustainability performance.

In this paper, we present an analytical model for flexible asset management, which is a new tool for company decision-making. The model reveals a significant negative correlation between the cycle times of operational working capital and the return on investment. Conventional research on working capital management has mostly focused on manufacturing industries. We show that working capital should be managed actively also in unconventional environments like service industries. The focus is on the industrial maintenance service providers, which still remain somewhat unexplored in academic literature. The importance of working capital management is actually emphasized in this industry, due to its light fixed assets and good profitability. Interestingly, there are some major differences between large enterprises and small and medium size enterprises in the industrial maintenance service sector. These can be explained through economies of scale and the fact that large maintenance service enterprises often focus on providing services mostly for their former host companies.

The offshore wind energy sector is growing fast and a lot of effort is put to the business and technology development. Today most current offshore turbines are based on onshore turbine designs and the turbine technology development continues incrementally. At the same time offshore wind marked demands for technological breakthroughs and new concepts increases. The main focus in offshore wind turbine design is reliability and cost efficiency. Most important decisions concerning safety and dependability are made in the early phases of design process that has implications in the operational phase. The later the decisions are made and mistakes realized the more they cost. RAMS+I (Reliability, Availability, Maintainability, Safety and Inspectability) issues should be taken into account systematically from the beginning of design process as a risk mitigating measure. Wind turbines in cold climates like in Nordic countries may be exposed to icing conditions or temperatures outside the design limits of standard wind turbines. In this paper we outline a model for managing RAMS+I factors in the conceptual design phase of offshore wind turbine. The model is based on the product development process, concurrent design ideas and the Stage-Gate® model. The model concentrates mostly on technical decisions made in the early development phases. Service aspects, such as maintenance concepts, are not in the focus of this paper. In some level service-based factors still have to be involved into technical decisions made. The objective of the presented model is to clarify the fuzzy front end of wind turbine innovation process from the RAMS+I point of view as a risk reduction measure at the operational phase.

Remote services are services enabled by information and communication components and therefore do not require the physical presence of a service technician at the service object to provide a task. The impact of remote service on the capital goods industry has been increasingly significant over the recent years. Still many companies struggle with developing and implementing successful business models for remote service. This leads to a lot of unaccomplished benefits for the customer as well as for the companies themselves. A survey throughout companies in the industrial machine and plant production sector was conducted in order to determine what successful companies do differently from those that cannot effectively implement remote service business models. The study presented in this chapter identifies key success factors of companies that effectively implemented remote services for their products. In order to identify the successful companies a scale for measuring remote service success was developed. Only by the use of this scale further findings regarding the success factors were possible. Key findings include the fact that successful companies actively market their remote service to their customers. Generally they try to approach their remote service business from the operating company’s perspective.

The importance of maintaining close contact with customers and utilizing customer-based information has been emphasized in the industrial service and product-service systems (PSS) literature. A profound understanding of the customer’s business and production environment is needed for successful PSS development. The conventional methods for gathering information about customers (surveys, feedback and interviews) typically result in incremental improvements and information about existing products and services. The focus of this paper is on how the information and ideas from customer contacts can be better captured to enable radical improvements. A framework for capturing the customer ideas is presented. The framework is based on customer observation methodology, entrepreneurial opportunity recognition model, front end of innovation literature as well as the experiences of a case study and interviews.

Asset Management has grown over the last few years and has begun to use many terminologies and concepts across different applications. This has had notable negative effects on the further growth of the discipline, as the present conditions only contribute to complexity and chaos rather than leading the way toward a commonly acceptable approach. In this context, a common reference model becomes extremely useful that can provide the necessary elements which can be capitalized for learning and development efforts for wider applications. The issue here is to get the principles intact that has comparable and defining features across many contexts. In this respect some initial efforts were invested to develop an approach for a unified asset management reference model. This paper brings further work on this reference model into perspective by discussing the application of the reference model across different asset types. The purpose here is to communicate the potential of such a unified approach as a valuable foundation to build on.

Cloud storage and computing continues to present itself as a solution to the data storage and computational needs required by Asset Management applications. Indeed, a fleet of assets that are properly monitored using sensory networks will create terabytes and even petabytes of data and information. The largest computer and technology suppliers are investing billions of dollars in the evolution of cloud computing technology. To take advantage of cloud computing for Asset Health Management and prognostics it is necessary to understand cloud technology and its challenges. This paper introduces cloud concepts and relates them to the needs of Asset Management.

Data collection and analysis for machinery condition monitoring has been completely revolutionized due to the advances of the personal computer (PC). Processors run at GHz speeds, terabyte hard drives at very low cost, and Ethernet networking links systems across the globe. This technology has enabled engineers to perform all types of machinery analysis from thermography to vibration analysis to oil analysis and structural analysis. Engineers are connecting accelerometers, displacement probes, tachometers, cameras, microphones, thermocouples, strain gauges, and a lot more sensors to take the measurements they want. And the result of all this technology is an overwhelming mountain of data that engineers are left to sort through to understand their machinery. This challenge is further complicated by dedicated data collection systems which record just one type of sensory data, such as vibration. Even so, data collection technologies now record data in high fidelity, using 24 bit analog to digital converters, and stream more of it to disk than ever before.

This paper proposes the use of the visualization techniques through Augmented Reality (AR) in order to manage maintenance data visualization. Using this method the AR visualization will insert the human in the predictive maintenance loop. It will facilitate the users understanding (in this case the maintenance operator) and visualization of the information from a predictive system and also represent a way to provide a safe interaction for the operator.

This paper addresses modeling of age-dependent failure rates from incomplete data that includes interval-censored failure ages. Two estimators for cumulative failure rates are presented: a simple non-parametric estimator and a maximum-likelihood method based on the gamma distribution and the non-homogeneous Poisson process. The maximum-likelihood fit of familiar parametric models (e.g., the power law) to the available field data from an aircraft component was far from satisfactory, so a special three-parameter model function had to be worked out. The maximum-likelihood estimate obtained is then used for repeated random generation of different data sets akin to the field data. This way the effect of data set size, censoring rate, and randomness on the non-parametric estimate can be analyzed to get practical appraisals.

Asset lifecycle management is information intensive. The variety of asset lifecycle processes generate, process, and analyze enormous amounts of information on daily basis. However, as the volume of information increases so does the risks posed to its quality. In engineering asset management, the issue of information quality has organizational, technical, and human dimensions. In technical terms, this issue has its roots in multiplicity of data acquisition techniques, tools, systems, and methodologies, processing of the data thus captured within an assortment of disparate systems, and lack of integration and interoperability. As a result, the information requirements of asset management processes as well as information stakeholders are not properly fulfilled. This paper proposes a novel approach to resolving the issue of information quality. It takes a product perspective of information and applies six-sigma methodology to information quality management. In doing so, it not only assesses the maturity level of the quality of information, but also provides for the continuous improvement of information quality in asset management information systems.

Issues such as data integration and system interoperability are becoming more crucial in asset lifecycle management (ALM) information models. Improving the availability and exploitation of maintenance data is beneficial for both: predicting malfunctions and failures of the assets; and providing useful feedback of the beginning of life (BOL) to engineers for improving the next generations of the assets. The aim of this work is to combine an ontology information model for semantic maintenance with an IT system architecture in order to provide benefits and new services for the maintenance of the SISTRE system (Supervised industrial system for pallets transfer). The use of the ontology model is combined with description logics (DLs) which allow to reason on classes and instances. The information system is executable, dynamic, and flexible. The use of DLs is tested and validated through implementation in SISTRE which also demonstrates how the user may extend the developed model for facilitating better his needs and at the same time maintain data integration and interoperability among the variations of the initial model.

The problem of optimal planning for inspection of assets in engineering asset management is formulated in the case that the inspections take place at the beginning of the planning period. It is shown that the problem has the form of a 0-1 knapsack problem. The formulation is applicable to systems consisting of multiple assets. The concept of risk, as usually defined in asset management, arises from the formulation in a natural way and plays an important part in identifying optimal inspection plans. Two special cases are considered. In the first special case, where the inspection budget is large, the optimal inspection plan involves inspecting all assts whose risk is sufficiently large. In the second special case, where the costs and expected benefits associated with each asset are the same and the conditional probability of failure of each asset given that it has been inspected is negligible, the optimal inspection plan involves prioritization of assets on the basis of risk alone.

E-maintenance systems are considered as platforms integrating various systems in the maintenance scope, but these platforms provide only the services provided by their integrated systems. S-maintenance platform is built in the aim to provide dynamic services thanks to its core components, especially its knowledge base. This paper focus the exploitation of the s-maintenance architecture’s components to define two new processes that we called self management and self learning processes. These processes allow the automatic acquirement and integration of knowledge in the knowledge base and the dynamic evolution of the platform behavior.

This case study describes how the process of developing an asset management plan, adapted from the infrastructure industry, is used to translate organizational objectives to tangible operational, reliability, contractual, and regulatory objectives at the asset level. In this case, the assets are gas-turbine or reciprocating compressors critical to the organizational performance of a gas transmission company. The key to this process is the establishment of ‘level of service’ (LOS) criteria against which asset performance and the performance of the processes that support their operation are assessed. These LOS criteria, their measures and targets, are explicitly linked to the organization’s business plan and aligned with key policies. There are specific challenges to selecting performance measures associated with compressors in gas transmission lines and these are discussed. The process allows for performance comparison of the 68 compressors, supporting risk identification, operational improvements, and knowledge sharing. This view of a single asset class across the business units ‘horizontal asset management’ also identifies common risks and opportunities for technical improvement. The business case for investment in technical projects such as prognostics is enhanced as success can be leveraged across a number of critical units in the business and performance improvement assessed against common measures.

The management of engineered assets like cooling tower structures constructed from reinforced concrete, and pipe racks fabricated in steel, generally receive less maintenance emphasis than plant equipment such as instrumentation, pumps, switch gear or vehicles. This seems more apparent in mining and mineral processing firms where production output is the dominant concern. Coupled with the usually longer life span of civil structures and static mechanical components, this tendency often leads to limited knowledge about the condition of assets like cooling towers, substation buildings, and pipe racks which, in principle, cannot run to failure. The paper is based on an ongoing case study of a large industrial complex. Three sources of data are being explored to provide insight regarding the condition assessment of engineering structures in practice. The perplexing initial findings suggest that despite advances in condition monitoring technologies, more often than not, visual inspection prevails as the assessment method and thus confines budget provisions for maintenance activities.

Pneumatic conveying is a frequently used method of material transport particularly for in-plant transport over relatively short distances. This is primarily to exploit the degree of flexibility it offers in terms of pipeline routing as well as dust minimization. Approximately 80 % of industrial systems are traditionally dilute phase system which uses relatively large amount of air to achieve high particle velocities to stay away from trouble, such as blocking the pipeline. However, for many applications higher velocities lead to excessive levels wear of pipelines, bends, and fittings. To combat these problems, many innovative bends have been designed. These designs have solved the problem of wear in the bends, but often introduce the wear problem in the area immediately after the bend due to the changed flow conditions. Wear in pneumatic conveying is a very complex problem and at present there is limited understanding of the wear mechanisms responsible for the severe wear in certain areas of a pneumatic conveying pipeline. The ability to determine the wear mechanisms in these areas holds the key for determining the service life of pneumatic conveying pipelines in industry. Even though the fly can be conveyed at low velocity dense phase mode, wear of pipeline conveying fly ash remained a critical issue for many power plant operators. In this paper the wear mechanisms in a fly ash conveying pipeline has been analyzed. Wear samples from fly ash conveying pipeline have been collected and analyzed for dominant wear mechanisms in the critical wear areas. Analysis of the worn pipeline showed continuous wear channels along the bottom of the pipeline consistent with the abrasive wear by larger particles. The other severe wear areas are the sections after the special bends used to reduce bend wear. Scanning electron microscope (SEM) analysis of the surfaces revealed that both erosive wear and abrasive wear mechanisms are present in these areas. Formation of a surface layer similar to transfer film in alumina conveying pipelines have been recognized in this analysis. These layers seem to be removed through brittle manners such as cracking and spalling. The wear mechanisms and the wear debris seen on the surface are consistent with wear by larger particles.

RFID technology enables tracking of individual assets throughout the supply chain and real-time inventory management. However, the potential benefits cannot be realized until +99 % read rate is achieved. The radio frequency interference, signal fading, and harsh environment often result in not detected items, and subsequently incorrect asset management decisions. This paper presents a novel RFID-based smart freezer with a new inventory management scheme for extremely low temperature environments. The proposed solution utilizes a distributed inventory control (DIC) scheme, systematic selection of antenna configuration, and antenna power control in order to addresses those challenges. The performance of the DIC scheme is guaranteed using a Lyapunov-based analysis and verified in simulations and experiments on industrial freezer test bed operating at −100 °F. The proposed RFID antenna configuration design methodology coupled with locally asymptotically stable distributed power control (LASDPC) ensures a 99 % read rate of items while minimizing the required number of RFID antennas in the confined cold chain environments with non-RF friendly materials.

This paper addresses the need of developing sustainable strategies by taking advantage of e-platforms bringing advanced asset management solutions. Even though most asset management systems are cost oriented, it is also clear that new factors are gaining weight when considering strategies and operation, like safety or environmental issues. In this sense, LCA methodology enables to assist an effective integration of the environmental considerations in the decision-making process, and this paper presents different applications developed in this address. The paper also shows an e-platform based on current maintenance technologies already developed, together with strategies to select the most cost-efficient path in the search for a maintenance ‘excellence’ strategy. ICT technologies impact both the economical and environmental cost of the manufacturing processes or the factory as a whole. An integrated strategic decision tool for sustainability must be able to simulate and assess cost variations in processes and business models and also incorporate novel metrics, business concepts, and paradigms in order to estimate adequately the interactions between factory, production systems, and processes.

Aiming at inner ring single fault and inner ring, outer ring compound fault of rolling bearing, wavelet analysis and nonlinear redundant lifting wavelet packet analysis are introduced in this paper to process the fault vibration signals in order to realize fault diagnosis of bearing. Conclusions can be drawn from the results that spectrum analysis is of limitation for fault feature extraction, while wavelet analysis and nonlinear redundant lifting wavelet packet analysis are more effective in extracting fault feature information and the latter has more advantage in compound fault analysis.

Gathering system is not only an important oilfield production facility system for gathering and transporting oil and gas but also is a key section to realize the function of oil and water separation. However, the high complex failure coupling relation among separate production process sections, personnel operation, and equipment leads to which is a complex high potential hazard and failure relational degree within subsystems production system, make it difficult to analyze failure mode. Therefore, it is urgent to demand a higher level effective operational failure analysis model to simplify the complex gathering system to resolve the realistic safety problem. A failure mode analysis of gathering system based on a novel MFM-HAZOP deep knowledge model is proposed. By decomposing goals, functions and components, a graphical MFM model of gathering system is established. Combined with HAZOP analysis technology, this paper sets up MFM-HAZOP model to perfect studying failure mode of gathering system and accurately analyzes reason of failures, which is the base of gathering system safety decision to guarantee the gathering system safely operating.

Centrifugal pumps are a widely used machine found in industries such as water, sewerage, oil, and gas. As a result, it is vital that these pumps are monitored, diagnosed, maintained, or replaced prior to the pump failing to reduce downtime, material, and labour costs. Most companies employ a run-to-fail method or a time-based maintenance strategy to service their pumps, instead of condition based maintenance or a predictive maintenance strategy. This paper reviews the state of art in diagnostics and prognostics pertaining to centrifugal pumps. Attention is given to detailing the methods of application, detection of fault modes and results used by researchers in the main areas of diagnostics and prognostics.

In this paper, a condition-based monitoring (CBM) model that uses the Mahalanobis-Taguchi System (MTS) for fault detection, isolation, and prognostics is presented. The proposed scheme fuses data from multiple sensors into a single system level performance metric using Mahalanobis Distance (MD) and generates fault clusters based on MD values. MD thresholds derived from the clustering analysis are used for fault detection and isolation. When a fault is detected, the prognostics scheme, which monitors the progression of the MD values over time, is initiated. Then, using a linear approximation, time to failure is estimated. The performance of the scheme has been validated via experiments performed on a mono-block centrifugal water pump testbed. The pump has been instrumented with vibration, pressure, temperature and flow sensors and experiments involving healthy and various types of faulty operating conditions have been performed. The experiments show that the proposed approach renders satisfactory results for centrifugal water pump fault detection, isolation and prognostics.

Analog circuits have been widely used in diverse fields such as avionics, telecommunications, healthcare, and more. Detection and identification of soft faults in analog circuits subjected to component variation within standard tolerance range is critical for the development of reliable electronic systems, and thus forms the primary focus of this paper. In this paper, we have experimentally demonstrated a reliable and accurate (99 %) fault diagnostic framework consisting of a sweep signal generator, spectral estimator and a least squares-support vector machine. The proposed method is completely automated and can be extended for testing other analog circuits whose performances are mainly determined by their frequency characteristics.

In this paper, a novel technique for denoising gearbox vibration has been proposed. We first convert the vibration signal into a two-dimensional matrix such that each row of the resulting matrix contains exactly one revolution of the gear. This matrix is subsequently denoised using two-dimensional wavelet thresholding method. We apply our proposed method to an experimental data set to investigate the improvement in denoising performance. The experimental data is generated using a test rig on which different damage levels are simulated. The experimental results show that the impulses in the vibration signal can be detected easily from the denoised signal even for slight localized tooth damage. The proposed method is compared to time synchronous averaging and the combination of the time synchronous averaging and the one-dimensional wavelet denoising. The kurtosis value of the denoised signal is used for comparing the denoising performance of these three methods. The comparison study shows that the proposed method outperforms both competing methods, especially in early stages of the fault.

The future natural gas network will deliver multiple services and the gas in the network will be a mix of various gasses like hydrogen and biogas. In our study we use asset management techniques and processes to create a flexible, future-proof infrastructure. Ultimately, we will develop a framework in which technical and institutional flexibility are considered. Our first modeling efforts led to a representation of such a changing gas grid, including its physical and operational performance. We used this model to execute some initial Monte Carlo simulations. This first exploratory model allowed us to demonstrate the approach and to explore the value of flexibility in gas infrastructure asset management.

The introduction of innovative contracts caused a shift in the roles and responsibilities between asset owner, asset manager, and service providers. This shift requires better integration of decisions on strategic, tactical, and operational level. In this paper we discuss the impacts of social technical trends on the asset management of public infrastructure networks. We identify trends in literature and illustrate some of the trade-offs for road maintenance. We conclude by identifying the effect of innovative contracting and other trends on the implementation of asset management systems and proposing a new direction for research into more dynamic contracting.

An important feature in managing civil infrastructures is the growing use of outsourcing in the delivery of maintenance. Evidence of this is shown in a strong increase in the application of performance-based contracts. The expectations of the principals are high: a smaller organization, better service, lower costs, more innovation, and more flexibility. But there are also risks connected to performance-based outsourcing of maintenance: the use of the wrong performance requirements, strategic behavior of the contractor, and a lack of knowledge and experience of the principal. The main question for the authorities that want to outsource their maintenance is: how do we achieve as much as possible of the expected advantages while limiting the possible disadvantages to a minimum? Based on case study and theoretical exploration this paper answers that question by investigating the strategies that the English Highways Agency and the Dutch Rijkswaterstaat use, when outsourcing the maintenance of their existing road infrastructures and what the effects of their strategies are. Lessons are drawn from the case studies and they are of most interest to other road authorities that consider, or already have chosen, outsourcing the delivery of maintenance as the way forward.

Buildings are complex infrastructure assets and optimization of maintenance and rehabilitation actions require a well-considered asset management model. Community Buildings as one of the major investments of the local government in Australia have a large proportion of demand, expectation, and consideration among the local council’s assets. The following paper introduces a building asset management (BAM) framework and a building element hierarchy which facilitates the building asset management and inspection strategies. Data gathering and preparation will be discussed followed by the calibration of a probabilistic deterioration prediction approach based on the Markov process. The Markov transition matrices have been derived for building elements based on the condition data sourced from local councils. The transition matrices for superstructure elements are presented, reviewed, and compared.

Cloud computing has forced companies to a paradigm shift on how services are managed and delivered, and this includes how prognostics and health management (PHM) applications are implemented. Currently, companies are constrained in developing fleet-wide or distributed monitoring systems due to limitations in computing resources, difficulty to achieve scaled application deployment, among others. This paper addresses these issues through the iFactory Cloud Service Platform which integrates a cloud infrastructure (Servo-lution) and PHM analytics. A tool condition monitoring (TCM) module has been developed as a service application and has been successfully demonstrated on a horizontal machining tool test-bed.