Advances in Reliability and Maintainability Methods and Engineering Applications

The reality of selecting an acceptable ballast water treatment technology is a daunting task for end-users, due to availability of numerous treatment options and their efficacy in given ship-types and ballast voyages. Six treatment systems have been selected from the two generic treatment technology groups (physical solid liquid separation and disinfection), and are considered as the decision-making alternatives in the proposed model. The proposed model involves the application of the Technique for Order Performance by Similarity to the Ideal Solution (TOPSIS), in the decision-making analysis. The TOPSIS technique has been applied to obtain the performance ratings of the decision alternatives using linguistic terms parameterised with triangular fuzzy numbers. A sensitivity study is also conducted to identify the effects of changes in input data, and test the suitability of the developed model in decision-making analysis of ballast water treatment systems.

This work presents a two-phase sampling approach to address reliability-based optimization problems in structural engineering. The constrained optimization problem is converted into a sampling problem, which is then solved using Markov chain Monte Carlo methods. First, an exploration phase generates uniformly distributed feasible designs. Thereafter, an exploitation phase is carried out to obtain a set of close-to-optimal designs. The approach is general in the sense that it is not limited to a particular type of system behavior and, in addition, it can handle constrained and unconstrained formulations as well as discrete–continuous design spaces. Three numerical examples involving structural dynamical systems under stochastic excitation are presented to illustrate the capabilities of the approach.

Comprehensive and effective assessment of motion accuracy reliability for industrial robot registers a crucial and lasting challenge. In order to ensure the precision performance of industrial robots, this study systematically investigates the reliability modeling and analysis. For kinematic accuracy reliability, a novel computational framework is proposed to comprehensively evaluate the reliability for kinematic positioning and trajectory accuracy of industrial robots, in which the motion error correlation quantification methods are developed. In terms of dynamics accuracy reliability, the rotational sparse grid method and the advanced mixed-degree cubature formula are inferred to evaluate statistical moments of industrial robots’ joint torque subject to multidimensional correlations among uncertain parameters. The computational performance of proposed methods is significantly improved compared to the traditional competitive approaches. The engineering practicability and proficiency of the proposed methods are verified by a series of industrial robot examples.

A wireless body area network (WBAN) is a network of low-power devices including smart sensors situated in, on, or around the human body to monitor the physiological and motion information for healthcare, military, sports, security, firefighting, as well as other applications and purposes. Reliability is one of the major changes to address for delivering the desired quality of services of WBANs. In this chapter, a critical review of WBAN reliability-related literature is conducted, covering reliability modeling, analysis, and designs. A reliability model is also presented for WBANs subject to the probabilistic function dependence and associated probabilistic isolation and competing behaviors. The model is demonstrated through a case study on the reliability analysis of a WBAN patient monitoring system.

Component importance analysis measures the effect on system reliability of components’ reliabilities, enables the analyst to rank each component’s contribution to the system failure, and identifies the system’s weak components. Thus the system reliability can be improved by upgrading the weak components. Component importance analysis is commonly used in the design of a system from the reliability point of view. However, although dependencies exist among the failure behavior of systems in practice, and the dependent failures are known as a risk factor for degradation of system reliability, it is difficult to evaluate the component importance measures in the presence of failure dependencies analytically. In this chapter, we consider the Markov chain-based component-wise sensitivity analysis, which can evaluate the component importance measures without any system structure function. In particular, three types of component importance measures are derived from the viewpoints of both steady-state availability and reliability. Also, numerical examples illustrate the component importance analysis with the proposed approach.

For products subjected to many times of load action during service, product life is dominated by load and its capability against load, referred to as strength. This chapter introduces load-strength interference analysis based failure rate modelling method, develops component and system failure rate models, and illustrates the causal relation between failure rate curve shape and load/strength characteristics. For the majority of mechanical components and systems, service load can be described as a random process, material property degrades during load actions, and the dynamic load-strength relationship makes the failure rate change continuously. As failure occurs on load exceeding strength, failure rate models are developed by analyzing the competition behavior between load and strength. By such failure rate models, the effects of load uncertainty, strength uncertainty and strength degradation pattern on failure rate curve shape are demonstrated. Meanwhile, the three stages of the bathtub curve are interpreted in terms of stochastic load-strength competition behavior, the roller coaster type failure rate curve is attributed to the strength diversity of the products in a population.

The nonhomogeneous Poisson process (NHPP) has become a useful approach for modeling failure patterns of recurrent failure data revealed by minimal repairs from an individual repairable system. This work investigates complex repairable artillery systems that include several failure modes. We propose a superposed log-linear process (S-LLP) based on a mixture of nonhomogeneous Poisson processes in a minimal repair model. This allows for a bathtub-shaped failure intensity that models artillery data better than currently used methods. The method of maximum likelihood is used to estimate model parameters and construct confidence intervals for the cumulative intensity of the S-LLP. Additionally, for multiple repairable systems presenting system-to-system variability, we apply the mixed-effects models to recurrent failure data with bathtub-shaped failure intensity, based on the superposed Poisson process models including S-LLP. The mixed-effects models explicitly involve between-system variation through random-effects, along with a common baseline for all the systems through fixed-effects. Details on estimation of the parameters of the mixed-effects superposed Poisson process models and construction of their confidence intervals are examined in this work. An applicative example of multiple artillery systems shows prominent proof of the mixed-effects superposed Poisson process models for the purpose of reliability analysis.

Signature theory, as an important part of reliability theory, provides an efficient tool for modeling and analyzing various properties of reliability systems. By now, signature theory has become exhaustive for binary-state systems, but for multi-state systems which are commonly encountered in practice, there are still a lot of issues to examine. In this work, we review important research works that have been carried out on signatures of systems with a special focus on signature concepts, their properties, computational methods, and some multi-state signatures for multi-state systems. We also summarize work that we have done recently on multi-state signatures, including their definitions, properties, transformation formulas and module structures. Finally, we present a number of examples to illustrate various notations and associated results described here.

Aircraft actuation system receives commands from the flight control computer and drives the plane surface to realize the aircraft flight attitude and flight trajectory control. The actuation system has a significant influence on the overall aircraft flight control performance and safety. This chapter presents the essential reliability characteristics of the redundancy aircraft actuation systems, and creates a reliability evaluation method for non-similar redundancy actuation systems. Section 1 summarizes the aircraft actuation system, and explains the interface between the flight control system(FCS) and the actuation system. Some typical aircraft hydraulic actuation system constructions in current commercial aircraft are provided in Sect. 2. Furthermore, Sect. 3 analyzes the architecture and characteristics of A380 aircraft actuation system, and provides the comprehensive reliability definition and the reliability calculation method. Afterward, the reliability of actuation system based on performance degradation is described. Finally, the integrated reliability evaluation case is provided for example of a HA/EHA system, in which HA operates actively and EHA follows under normal operating conditions.

System availability is a fundamental measure to evaluate the reliability performance of capital goods. Traditional approaches to availability management, such as reliability-redundancy allocation, preventive maintenance, and spare parts logistics, usually focus on a particular phase of system life. This paper discusses a holistic lifetime approach to sustaining system availability in an integrated product-service framework. Our approach seamlessly incorporates reliability, redundancy, maintenance, repairable inventory, and installed base information into a unified availability measure. A superimposed renewal process is adopted to characterize spare part demands considering the effect of installed base and proactive replacements. Extensive simulations are conducted to analyze the spares demand profile in terms of maintenance time, lifetime distribution, inventory lead time, and repair and renewing capacity. The study reveals that: (1) system availability is jointly determined by ten performance drivers across the product design, manufacturing, and after-sales market; (2) Poisson spare parts demand assumption is valid provided the item lifetime is much longer than the inventory replenishment time; and (3) installed base information provides a causal approach for spares demand forecasting during the new product introduction phase.

This chapter proposes a modified failure mode effect analysis (FMEA) approach using Artificial Neural Networks (ANNs) to evaluate and predict the operational risks of container terminals. It effectively integrates two established methods in one framework to realise complex risk analysis from a whole system perspective, including fuzzy rule based Bayesian networks (FRBN) for risk analysis of particular hazards in ports and fuzzy evidential reasoning (FER) for safety evaluation of ports in a systematic way. During this process, ANNs are integrated with FRBN and FER respectively to create two sub-models. The first sub-model is FRBN-ANN that incorporates Bayesian networks (BNs) with ANNs to facilitate risk prediction of each identified hazard in a container port. The second sub-model is FER-ANN, which uses ANNs to simulate the FER method to ease the aggregation of all the hazards to obtain the safety level of the port. Finally, the two sub-models are combined into a single safety model, which can help simplify risk prediction, and realise real-time safety evaluation of ports at hazard or whole system levels. The Levenberg–Marquardt (trainlm) back-propagation algorithm trial and error approach was used to determine the optimal ANN architecture. The proposed ANN model produced small deviations that indicate high predictive accuracy with satisfactory determination coefficients (i.e., the regression) for forecasting operational risks of container ports. It provides an effective risk prediction tool for complex port safety systems, and significantly simplifies the port safety analysis and prediction in a feasible, versatile, and accurate manner. It, through the black box approach of ANN, provides a mathematically unsophisticated solution and hence aids the visualisation of risk analysis outcomes without the need of the end users to understand the complicated computing process of the risk inference. It makes significant contributions to port safety analysis and management in practice.

This chapter reviewed several optimization problems to determine its optimal relevant decision variables minimizing the expected warranty costs during certain intervals, such as life cycle, warranty period or post-warranty maintenance period. The decision variables of our interest include the length of warranty period, inter-PM interval and length of post-warranty maintenance period. All of the warranty models presented in this chapter are based on the renewable minimal repair-replacement (MRR) warranty under which both repair time and failure time are considered at the same time upon the system failure. Furthermore, the warranty conditions under the MRR warranty is somewhat similar to the ones regulated under the lemon law which aims to protect the buyers of the defective motor vehicles. The warranty model applicable to the lemon law is also presented in this chapter.

This chapter reviews the state-of-the-art methods and procedures for the reliability and maintainability of floating offshore wind turbines. First, a new failure identification and critical failure determination schedule is introduced, according to which failure prevention actions are determined. Subsequently, failure rate and reliability analysis models are reviewed, in particular, the assessment of failure rates of floating offshore wind turbines based on the onshore counterpart data. Finally, an opportunistic maintenance model is described for better scheduling of the maintenance crew, allowing limited preventive maintenance after corrective maintenance. Overall, methods and procedures introduced in this chapter contribute to failure and risk management, reliability improvements, and maintenance strategy planning of floating offshore wind turbines and can apply to other complex systems.

In this chapter, we consider one shot systems, long-term repairable storage systems, which are in storage and can be used at an unknown time point once, whose failure only can be detected by inspection. Due to the system failure can incur a loss of life and economic damage, inspections and maintenance should be carried out to maintain a high level of storage reliability. However, since these inspections are usually costly, inspection times should be optimized to achieve a balance between undetected failure costs and inspection costs. Therefore, it is necessary to suggest appropriate optimization criteria and inspection policies according to the system structure and function characteristics of one shot system. In recent years, performance evaluation and inspection optimization problems have attracted many researchers’ attention. This study summarizes the existing literatures related to the reliability and inspection optimization models of one shot systems. Firstly, this paper reviews the recent advances in storage reliability modeling for evaluating the performance of one shot systems. On this basis, the inspection optimization models of one shot systems with various structures are established and the key ideas of optimization methods in each optimization inspection problem are summarized. In summary, this contribution provides a survey on optimization methods for the inspection policy of one shot systems, with emphasis on the optimization methods under the different scales of systems, such as single-unit and multi-unit, as the target system. In addition, a qualitative comparison is performed to provide some general guidelines for the range of applicability of the approaches discussed in this contribution.

Reliability of a repairable system is usually modeled by a failure point process with system age as underlying variable. The system may undergo a reliability improvement process due to possible technology upgrades as well as manufacturing or/and maintenance quality improvement. New methods are needed to evaluate their influence on the reliability. This chapter aims to address this issue through introducing a maintenance experience measure and concept of system technology age. They are used as the underlying variables to analyze the influence of the maintenance and manufacturing quality on the reliability, respectively. A signal-to-noise-ratio-based cluster analysis approach is also proposed to identify the change point of a function. The proposed approach can be used to determine whether or not the system undergoes a reliability growth. These concepts, approach and their appropriateness are illustrated through analyzing a real-world example that deals with a fleet of air conditioning systems of jet airplanes. The results show that the maintenance quality is poor and the fleet may undergo a reliability growth due to manufacturing quality improvement rather than due to technology upgrade.

This chapter reviews the definition of warranty, introduces its different types, discusses possible causes of warranty claims, and then provides an introductory overview of the approaches to modelling warranty claims. When only warranty claim related data are available, statistical models are suggested to model the frequency of warranty claims. This approach is referred to as the black-box approach in the chapter. When the physical structure and the failure mechanism are known, both statistical models and physical models can be applied in modelling the frequency of warranty claims. This approach is referred to as the white-box approach. The chapter suggests that models that can reflect the real-world claim patterns should be the focus studied by researchers in the future.

In today’s competitive environment of Industry 4.0, cyber-physical systems (CPS) of various advanced manufacturing paradigms have brought new challenges to maintenance managements. Efficient prognostics and health management (PHM) policies, which can integrate both individual machine deteriorations and different manufacturing paradigms, are urgently needed. Newly proposed PHM methodologies are systematically reviewed in this chapter: as the decision basis, an operating load based forecasting algorithm is proposed for machine health prognosis; at the machine level, a dynamic multi-attribute maintenance model is studied for diverse machines in CPS; at the system level, novel opportunistic maintenance policies are developed for complex flow-line production, mass customization and reconfigurable manufacturing systems, respectively. This framework of PHM methodologies has been validated in industrial implementations.

Residual life estimation plays a significant role in scheduling maintenance activities for high-reliability products. In the literature, most of the existing studies dealt with this issue by considering only one-dimensional performance characteristic. However, it may be unreasonable since a product can have multiple performance characteristics. Generally, these performance characteristics are dependent due to the common influences from the environments. Moreover, the nonlinearity of the product’s degradation process should also be taken into account. In this chapter, degradation models based on nonlinear Wiener process is presented to address the issue under univariate and multivariate situations. Based on the proposed method, a closed-form of the probability density function (PDF) of the product’s residual life can be approximately obtained. Numerical examples concerning fatigue cracks demonstrate the validity of the proposed method.

Interest and associated research for reliability and health prediction and maintenance of infrastructure and industrial products have increased continuously. The study of reliability and health prognosis has become an indispensable field in the overall design and evaluation of systems, industrial products and engineering projects. Previously, the common approaches and mathematical models to describe the condition of products were usually based on the statistical lifetime distribution of the target production. The lifetime distribution is obtained based on the observation and analysis of large quantities of components. However, when it comes to a single component, it can only quantify whether the component is functioning or not, rather than the detailed working condition or deterioration behavior. Therefore, degradation models are introduced to quantify the health conditions of the component based on time dependent observations. Alternatively, on the basis of the degradation model, by introducing the degradation threshold of product failure, the reliability model and the remaining useful life of the product and the corresponding maintenance strategy can also be derived. In practice, the evaluation of the degradation behavior of the system often needs to introduce multiple degradation processes while modeling, and these degradation processes are not always independent of each other. Due to factors inherent in the system or from the external environment, these degradation processes often affect each other and show some commonalities. Examples of such degradation include LED lighting systems (Sari et al. in Qual Reliab Eng Int 25:1067–1084, 2009), operating data of heavy-duty machine tools (Mi et al. in Reliab Eng Syst Saf 174:71–81, 2018), fatigue cracks of two terminals of an electronic device (Rodríguez-Picón et al. in Appl Stoch Model Bus Ind 35:504–521, 2019), etc. In this chapter, we will introduce various degradation models, as well as modeling approaches and reliability analysis to study dependent processes, such as dependent Markov chains, shared shock exposure models, joint distribution functions of degradation paths, and dependent random effects stochastic processes.

The proactive health management is a new medical mode which is becoming an important issue for national health management. Proactive health management is very similar to reliability, the health state of an individual or a group of people is the key issue. Thus, it is significant to focus on the related researches in both theory and applications. In the chapter, a new Markov process is developed for describing the evolution process of health states via considering the health state itself and the invention events, and based on the Markov model, the formulas for several related measure indexes in health states are derived. Meanwhile, some analyses on the sensitivity of parameters appeared in the model and some numerical examples to illustrate the related measure indexes are presented. This research may shed light on further studies in proactive health management.

A Kalman filter-based framework is proposed for the prognostics and health management of DC electric motors by treating them as a system. The control signals of the motor are used to estimate the current health and predict the remaining useful life (RUL) of the motor and its components, such as bearings and permanent magnets. The framework consists of an online health diagnosis to estimate the health status of the motor and each component, and an offline failure prognosis to predict the RULs. The approach is demonstrated with the aid of two real examples: the reaction wheel motor for advanced attitude control of satellites and the driving motors in a quadcopter to lift and control flight operations. In each example, the motors were subjected to accelerated degradation tests, motor control data were collected for each cycle, and RULs were predicted against failure thresholds critical to motor performance. The results showed that the framework can be used to effectively predict the RUL of a degraded motor, thereby enabling failure prevention and proactive maintenance scheduling.

This paper presents a case study on using statistical method for detecting impending bearing failures using in-situ field data. We first explore the relationships between a few variables of interest using a matrix plot. By focusing on variables with consistent profile, we analyze the change in these multivariate data over time and propose a way to pinpoint impending failure. Due to the way data are generated and the inherent large variation, a Gaussian mixture model (GMM) is proposed and methods analogous to multivariate SPC are then applied to detect “out-of-control” signal. In particular, a phase I analysis using variances corresponding to the within and between sorties variations so that the correct control limits can be determined. From the actual failure and known conditions from field data, it was found that the proposed method is able to signal impending failure before it occurred.

Prognostics and Health Management (PHM) supports users with an integrated view of the health of any technical asset, and it consists of many different tasks based on data that are usually obtained from multisensory systems. The effective implementation of PHM does not, however, end with predicting remaining useful life (RUL). PHM has untapped potential to go beyond failure prediction and support of optimal maintenance actions and scheduling, along with logistics decisions. Both data captured by reliability systems and standard production data are generally used separately for different purposes. For higher effectiveness, these data have to be integrated in a combined approach. This can be achieved with the help of Digital Twin analytics that can support effective data use for parallel or combined purposes, such as classifying states, predicting failures or enhancing production efficiency. Furthermore, these seemingly independent concepts can be integrated into the same data collection approach. Previous studies have demonstrated that the afore-mentioned combined solution to classification and prediction challenges is yet only a standard approach to PHM, one that makes it possible to predict RUL, degradation track and optimal time to intervention. Consequently, a new solution is proposed, one that takes into consideration the possibility of intelligent and sustainable production in combination with online predictive maintenance and continuous process optimisation. The prediction of degradation and remaining useful life with the use of multisource data integration facilitates production process optimisation to gain additional use time. This, in turn, brings about incomparably greater financial effects than is the case with the traditional approach to PHM.

This chapter provides a systematic overview of the introduction and evolution of reliability systems engineering (RSE) in China, and the latest RSE development, including model-based RSE (MBRSE) and Reliability Digital Twin (RDT), are emphatically introduced. The chapter summarizes the establishment of the system architecture and conceptual models of MBRSE, fundamental theory and methodology of MBRSE with a V-model as the core of this approach, development of the MBRSE platform and RDT and the effectiveness of their implementations. Finally, the prospective trends in the development of RSE in China are outlined.