Advances in Condition Monitoring of Machinery in Non-Stationary Operations

Condition monitoring of rotating machinery is generally performed in

stationary

conditions or quasi-stationary conditions. Assuming linearity of the system its dynamic behaviour can be simulated in the frequency domain. Simulated results are then compared to measured results and the comparison allows to apply model based diagnostic procedures. As soon as the system presents strong non-linearity, simulation must be performed in the time domain, including also iterative procedures, which may become cumbersome. When the dynamic behaviour of linear or non-linear systems in non-stationary conditions is simulated, the time domain integration must be necessarily used. Accuracy of simulated results gets weaker; comparison with measured results for diagnostic purposes becomes difficult or ineffective; model based diagnostic approach seems not applicable. Monitoring of machines in “strong”

non

-

stationary

conditions is generally performed only by means of accurate signal analysis, without modelling the machine or the process. In this paper the simulation of some typical behaviour of rotating machines in weak or strong non-stationary conditions in the time domain is presented and discussed. Some experimental results are also presented and compared to simulations. Further examples of systems with strong non-linearity, that in stationary conditions exhibit non-stationary vibrations, are also given.

Due to the periodical motions of most machinery in steady state operation, many diagnosis techniques are based on frequency analysis. This is often performed through Fourier transform. Some extensions of these techniques to the more general case of non stationary operation have been proposed. They are based on signal processing advances such as time–frequency representations and adaptive filtering. The technique proposed in this paper is based on the observation that, when under non stationary operation, the vibrations of a machine are still tightly related to the speed variations. It is thus suggested to decompose the vibration signal over a set of time-varying frequency sine waves synchronized with the speed variations, instead of fixed frequency sine waves. This set of time-varying frequency sine waves is shown to be an orthonormal basis of the subspace it spans in the case of linear frequency variations. An insight to the improvement such decomposition can provide for spectral analysis, cyclostationary analysis and time–frequency representation is given. Some application examples are presented over both simulated signals and real-life signals.

Maintenance activities are commonly organized into scheduled and unscheduled actions. Scheduled maintenance is undertaken during pre-programmed inspections. Maintenance operations try to minimize the risk of deterioration based on a priori knowledge of failure mechanisms and their timing. However, in complex systems it is not always possible to schedule maintenance actions to mitigate all undesired effects, and SMART systems, which monitor selected parameters, propose actions to correct any deviation in normal behavior. Maintenance decisions must be made on the basis of accepted risk. Performed or not performed scheduled tasks as well as deferred corrective actions can have positive or negative consequences for the company, technicians and machines. These three risks should be properly assessed and prioritized as a function of the goals to be achieved. This paper focuses on how best practices in risk assessment for human safety can be successfully transferred to risk assessment for asset integrity.

Bearings are one of the more widely used elements in rotating machinery, reason why they have focused the attention of many researches in the last decades. The aim is to obtain a methodology that allows a reliable diagnosis of this kind of elements without dismounting them from the machine, and detecting the failure in incipient stages before a critical failure occurs. This manuscript develops and improvement of a technique showed in [

1

] of automated diagnosis of bearings through vibration signals, using the coefficients of the Multirresolution Analysis (MRA) and Multilayer Perceptron (MLP) neural network (NN). Data were obtained from a quasi-real industrial machine, where bearings were supporting axial and radial loads while rotating at different speeds. This technique offered very good results when diagnosing healthy and faulty bearings, nevertheless the reliability decreased when distinguishing between different kinds of failures. The novel technique showed in the present work, increases the success rates obtained using the same data: not only allows detecting early faults but also their location with high accuracy. The methodology exposed in this work is based on the use of the relative energy of the Wavelet Packets Transform (WPT), and NN, concretely, the RBF.

The importance of predictive maintenance optimization has been recognized over the past decades. A relevant aspect in the process of machinery noise control is the proper identification of noise sources. Microphone-array-based methods are known as alternatives for noise source identification in machines. In this work, the “Beamforming” technique is used to visualize the directionality pattern of the noise emitted by a rotating machine and a study is presented to compare the performance of machine condition detection using different architectures of classifiers based on Artificial Neural Networks. Sound maps from a rotating machine are used as inputs to classifiers for two-class (normal or fault) recognition. The classifier is trained with a subset of the experimental data for known machine conditions and is tested using the remaining data set. The procedure is illustrated using data from experimental sound maps of a rotating machine. The effectiveness of the classifiers and the network training is improved through the use of the Karhunen-Loève transform on the sound map data.

Vibration-based condition monitoring (VCM) has gained tremendous successes in the detection and differentiation of faults associated with rotating machines, through the installation of various numbers of vibration transducers at individual bearing pedestals of the monitored machine. This chapter however exposes the future potentials of the use of the higher order spectra (HOS) i.e., the bispectrum and the trispectrum for rotating machines faults diagnosis (FD). The aim of this is to achieve a significant reduction in the number of vibration transducers required at each bearing pedestal, without necessarily compromising valuable information required for the diagnosis. Four cases (healthy, shaft misalignment, cracked shaft and shaft rub) were simulated on an experimental rig with two rigidly coupled shafts supported by four ball bearings. Only four accelerometers (one at each bearing pedestal) were used for this study. The HOS results were compared for the different conditions of the rig. The observations and findings are presented in the chapter.

Standard bearing fault detection features are shown to be ineffective for estimating bearings remaining useful life (RUL). In this paper we propose a new approach estimating bearing RUL based on features describing the statistical complexity of the envelope of the generated vibrations and a set of Gaussian process (GP) models. The proposed approach is shown to be sensitive to incipient condition deterioration which allows timely and sufficiently accurate estimates of the RUL. The proposed approach was evaluated on the data set comprising of 17 bearing runs with natural fault evolution.

This paper presents a novel test rig, developed to analyse the behaviour of rolling element bearings subjected to highly varying loads. The design is optimised to measure the bearing behaviour, free from dynamics of the surrounding structure. In the current study, the test rig is used to evaluate the stiffness of a deep groove ball bearing under different operational conditions. The bearing behaviour is measured using the modal analysis technique. Then, an analytical model of the test structure is fitted on the data to estimate the bearing stiffness. The stiffness estimation is validated using a dummy bearing with a known stiffness. Finally, the stiffness of a mounted ball bearing is estimated. The paper evaluates the effect of a radial static load on the bearing stiffness. Stationary and operational conditions are compared as well. A clear difference between the stiffness of a rotating and non-rotating bearing is observed.

Many investigation methods used to identify the most common faults in rotating machines do not consider the non-linear behaviour of oil-film journal bearings with an adequate care. This chapter shows the results of a parametric analysis performed to study the sensitivity of non-linear effects in the oil-film forces to changes of some parameters of the synchronous (1X) filtered orbit of the journal. This study is focused on the influence on non-linear forces caused by changes of the maximum amplitude and circularity of the journal orbit as well as by changes of the inclination angle of the major principal axis of the 1X elliptical orbit. Moreover, also the effects of the shaft rotational speed, bearing load and the average journal position have been taken into account. A procedure to perform this sensitivity analysis for different types of journal bearing is described. Then, the results obtained by the analysis of the behaviour of a two-lobe elliptical oil-film journal bearing are shown and discussed.

In the field of rolling element bearing diagnostics, envelope analysis has gained in the last years a leading role among the different digital signal processing techniques. The original constraint of constant operating speed has been relaxed thanks to the combination of this technique with the computed order tracking, able to resample signals at constant angular increments. In this way, the field of application of this technique has been extended to cases in which small speed fluctuations occur, maintaining high effectiveness and efficiency. In order to make this algorithm suitable to all industrial applications, the constraint on speed has to be removed completely. In fact, in many applications, the coincidence of high bearing loads, and therefore high diagnostic capability, with acceleration-deceleration phases represents a further incentive in this direction. This chapter presents a procedure for the application of envelope analysis to speed transients. The effect of load variation on the proposed technique will be also qualitatively addressed.

In this work, a novel thresholding method is proposed to improve the accuracy in segmentation process on thermal images. Characteristics of the thermal distribution around convex Regions of Interest (ROI) are the core of this method, used as input markers for a segmentation process based on watershed transform. This method based on data variability reduces the classification error by about 10 % and reduces the number of features by about 80 % from the set of 360 elements. Moreover, the proposed method provides some tracks for fault localization, demonstrated for a bearing unbalance test rig.

The task of identifying a faulty roller element bearing has been so far faced through the use of envelope analysis. As it is well known the main issue linked to such approach is related to the definition of the optimal band-pass filter which can enhance the defect characteristics when the vibration signal is affected by severe noise. The Kurtogram has overcome this limit by letting the optimal band-pass filter be selected in a semi-automatic way, that is by exploiting the potentials of the Spectral Kurtosis. This paper aims at presenting an alternative algorithm which is able to cope with faults characterised by an impulsive-periodic nature. It is well known that faults characterised by periodic-impulsive nature are identifiable by means of cepstral analysis while damages inducing modulation effects are usually assessed via envelope processing. The presented algorithm combine two instruments, since it is based on the Fourier spectrum of the cepstrum squared envelope. Such spectrum allows to isolate the modulation effect by centring the modulating frequency around the DC component. In this paper the algorithm is applied to both synthesized data reproducing typical damaged rolling bearing signals and experimental data. Results achieved by exploiting the proposed algorithm are compared to the ones obtained by applying conventional envelope analysis based on Spectral Kurtosis.

Conventional vibration-based condition monitoring (VCM) of rotating machines with a multiple bearing system, such as Turbo-generator (TG) sets, is data intensive. Since a number of sensors are required at each bearing location, the task of diagnosing faults on such systems may be impossible for even an experienced analyst. Hence, the current study aims to develop a simplified fault diagnosis (FD) method that uses just a single vibration and a single temperature sensor on each bearing. Initial trials on an experimental rotating rig indicate that supplementing vibration data with temperature measurements gave improved FD when compared with FD using vibration data alone. Observations made from the initial trials are presented in this paper.

A comparative analysis has been performed as an effort to obtain a better idea of how the fault is appearing over a rolling bearing. In this paper are presented the results of this analysis between three methods of detecting faults on bearings: infrared thermography, vibration analysis and air-borne sound. Those methods are applied on a specific rolling bearing and developed on an experimental set up. The conducted experiment depicted that this comparison is feasible as the results of each method are relevant.

Manufacturing of mechanical components with a large lot size is usually an automated process. The knowledge of the in situ

condition

of the tool can help to tailor its exchange cycles in order to reduce costs by using it to its full capacity, without compromising workpiece quality. We aim to monitor the

condition of a lathe’s tool

cutting tip by use of

acoustic emission

(

AE

)

technology

in order to increase its service life. Unlike previous research approaches that verified the potential of AE to monitor tool wear, we concentrate on identifying and overcoming application challenges of automated tool wear control that impede a broad and economic use of tool condition monitoring (TCM) in industry.

Wind turbine condition monitoring is essential task in the process of maintaining machine operation at the optimal level. It is related to ensuring the profitability of investment and the provision of security in the environment of the turbine. However, operational conditions of turbine associated with non-stationary nature of the stimulus which is the wind, impede the correct diagnosis of the machine. In addition, a multitude of parameters adversely affects the clarity of predictions and setting alarm thresholds. In the article, the authors evaluate the impact of generator output power and rotational speed on selected vibration-based feature value. The study was performed for wind turbine planetary gearbox during fault development of the ring. It was possible due to historical data consisting peak-to-peak (P2P) values together with corresponding values of rotational speed and generator output power. For the purpose of the experiment the method that bases on calculation of arithmetic mean of the data in the segments corresponding to the chosen ranges of both rotational speed and generator output power is presented. Results are given in the form of three-dimensional charts, which allow assessing the impact of parameters on the studied feature. The paper shows that for machinery operating under varying regime proposed representation might serve as a valuable method for fault detection. Additionally, authors highlight the importance of analysis of vibration-based features as a function of two variables (rotational speed and power/load).

One of the standard approaches widely used in the field of localized gear tooth fault diagnosis is the creation of residual signals i.e. signals obtained after removing the deterministic frequency components from a Time Synchronously Averaged vibration signals. Most of the time these components are removed based on the knowledge of the characteristic gearbox frequencies. Sometimes however such information is not available. AR modeling, a type of time series modeling, has been found to be capable of faithfully estimating the deterministic content of the signal allowing meaningful residual signals to be created. An improvement to the classic AR modeling approach is proposed in this text. The method is applied to experimental data taken from a gearbox in both healthy and faulty condition. The improvement derived from the new method is quantified through a comparison with results obtained by applying Time Synchronous Averaging and the classic AR modeling method to the experimental data.

In this paper a torsional model of planetary gear set test bench is developed. This bench is composed by two identical planetary gears connected by a common shaft. A tri axial accelerometer is mounted in both rings. The mechanism leading to modulation sidebands is modelled. Time histories are characterized by a periodic fluctuation. Spectra showed sidebands around the mesh frequency and its harmonics. Simulation is achieved to demonstrate amplitude modulation and rich sidebands that agree well with the experimental results.

Nowadays, many industrial types of machinery rely on different types of gears to transmit rotational torque. Gearbox faults are one of the major reasons for breakdown of industrial machinery. Therefore, gearbox diagnosing is one of the most important topics in machine condition monitoring. A number of signal processing techniques are described for the vibrodiagnostics of gearboxes, but there are also different limitations for vibration based gear diagnostic methods. For some specific requirements (e.g. time-triggered signal acquisition), not all of described techniques can be always applied in industrial reality. This paper introduces a novel, easy to use method of gearbox health vibromonitoring based on Empirical Mode Decomposition (

EMD

) and a time-domain analysis of vibration signal parts. Six sets of data collected from gearboxes are used to validate the proposed method. The experimental results demonstrate that the gear tooth defect can be detected and evaluated at an early stage of development when both Empirical Mode Decomposition and statistical analysis technique are used.

In the paper a problem of diagnostic data classification is discussed. The classic condition monitoring approach requires two examples of machines: one in a good and one in a bad condition. From the industrial perspective such a requirement is often very difficult to fulfill, especially in the case of machines with an unique design. To overcome it, we proposed to use the Artificial Immune System (AIS) based approach to classify multidimensional diagnostic data. AIS allows to recognize a change of the machine condition based on a training phase using the dataset related to a good condition. To validate the proposed procedure and assess efficiency of the condition recognition, an extra data set from another machine (of the same type) in a bad condition was used. In the paper several key issues related to the selection of parameters have been discussed.

There is a need to treat a gearbox as a subsystem which consist of several elements like gears, bearings and shafts incorporated into a box. The gearbox is incorporated into a system of a drive, for example an electric motor, and a driven machine. When a system is in operation the mention elements interacts each other. When preparing condition method one has for disposal results of the research directed to evaluation of isolated faults like a tooth crack, tooth breakage, pitting, scuffing, misalignment and so on. It is taken in advance that only one of the mentioned faults occurs in the system. The research is done at the condition of constant load or constant rotation speed. There is also done research under condition of different constant levels of the load and rotation speed. The scenario of degradation process of gearboxes is that only one fault occurs and developed in the system. In real gearbox systems many different scenarios of degradation process may occur. The presented paper will show gearbox condition monitoring procedures, which is equivalent to different scenarios. There are given some steps, which ought be considered when gearbox condition monitoring procedures are developed. Having in mined these steps one can control the gearbox degradation process extending a gearbox live and reducing maintenance cost, creating, what is called, the failure prevention technology. For presenting this paper stimulates us also the papers presented in the MSSP “Special Issue” on the “Condition monitoring of machines in non-stationary operations” where some authors have tendency of treating the machine as the collection of separate not connected elements in the same way as it was stated before.

This paper proposes a model-based technique using a combination of cyclostationary and autoregressive signal modelling in order to detect wear in a multistage planetary gear of lifting cranes. The first-order cyclostationarity is exploited by the analysis of the Time Synchronous Average part (TSA) of the angular resampled vibration signal. Then an autoregressive model (AR) is applied to the TSA part in order to extract a residual signal containing pertinent fault signatures. The paper also explores the efficiency of a number of methods commonly used in vibration monitoring. Condition monitoring indicators are then extracted from different treated signals. In the experimental part, all these techniques are applied to a test bench data of a lifting winch. The goal is to trend the evolution of the extracted features during the test. This study reveals that the proposed procedure using this combination enhances the ability to detect and diagnose mechanical wear of winch planetary gears.

The correct diagnosis of faulty components in rotating machines requires a pre-knowledge of the characteristics of the system being monitored and the identification of the frequencies of interest. In gearboxes, the number of gear stages and the number of teeth for each gear are required to calculate the gear mesh frequencies and monitor these frequencies and their sidebands. It is not always possible to have this information available, especially in old equipment. In this chapter a fresh approach is presented to deduce such crucial information from the measured vibration signal. The approach focuses on fine tuning harmonic/sideband cursors to capture different gear mesh families. The approach is illustrated on a signal taken from a wind turbine gearbox, which poses the extra challenge of the variable speed within the measurement record. Results show the possibility of identifying the number of teeth for the first two stages with much more confidence than the planetary stage, where a trial and error approach was used to decide on the most likely combination for the ring, sun and planetary gears. This chapter sets a good practice example for understanding the system characteristics by detailed analysis of the vibration signal using finely tuned harmonic and sideband cursors.

The detection of cracks in gears may be considered as among the most complicated operations in the diagnosis of this type of machines. This paper presents the crack signature in the vibration signal through a numerical model. Then, a comparison of phase analysis is conducted between the phase estimated by the Hilbert method and the proposed technique Estimation of Signal Parameters via Rotational Invariant Technique (ESPRIT) by using a sliding window. This comparison was made on signals coming from both a numerical model of a cracked tooth and a multiplicative signal modulated in frequency. The proposed method gives very interesting results despite the existence of the amplitude modulation generated by the transmission error of the gear model.

There are different statistical fault detection indicators applied in the time domain to detect crack propagation in the gear tooth root. ‘TALAF’ and ‘THIKAT’ are two newly presented indicators which have been designed and recommended to improve the performance of ball bearing fault detection after a certain stage of degradation. This chapter studies the performance of these two new indicators, together with the RMS, kurtosis and crest factor indicators, in the context of detecting faults in the gear tooth root. The chapter also presents an investigation of the performance of these indicators in the presence of three levels of random background noise. Gear mesh stiffness calculations and dynamic simulation have been performed using Matlab™ to obtain the residual gear centre point displacement signals for different crack propagation cases. The simulations indicate that the RMS and kurtosis perform well for crack depths up to approximately 50 % of the tooth root thickness. Kurtosis and THIKAT show the most sensitive performance with an increasing noise level.

The use of the cepstrum for removing components from a signal which manifest themselves as periodic spectral components has previously been described. These include discrete frequency components with uniform spacing such as families of harmonics and modulation sidebands, but also narrow band noise peaks coming from slight random modulation of almost periodic signals, such as higher harmonics of blade pass frequencies. The removal is effected by applying a notch “lifter” to the real cepstrum of the signal, thus removing the targeted components from the log amplitude spectrum, and then combining the modified amplitude spectrum with the original phase spectrum. Not much attention was previously paid to the type of notch lifter, but two different situations occurring in conjunction with analysis of signals from wind turbines showed that different lifters have advantages in different situations. This chapter describes two different approaches, illustrating them with the two examples of application.

In this paper a new method of failure detection in rotating machinery is presented. It is based on a vibration time series analysis. A pure vibration signal is decomposed via the short-time Fourier transform (STFT) and new time series for each frequency bin are processed using novel approach called local maxima method. We search for local maxima because they appear in the signal if local damage in bearings or gearbox exists. Due to random character of obtained time series, each maximum occurrence must be checked for its significance. If there are time points for which the average number of local maxima is significantly higher than for the others, then the machine is suspected of being damaged. For healthy condition machinery, the vector of average number of maxima for each time point should not have outliers. The main attention is concentrated on the proper choice of required local maxima significance. The method is illustrated by analysis of very noisy both real and simulated signals. Also possible generalizations of this method are presented.

In the framework of monitoring of rotating machinery, this paper proposes a simple signal processing tool to reconstruct the Instantaneous Angular Speed (IAS) variations caused by the presence of spalled bearing. This tool is applied to signals obtained on a specific test bench. Associated with an angular sampling, the analysis of these variations can identify the length of the defect whatever the mode of operation, particularly in non-stationary operating conditions in rotation speed.

In rotating machinery, actions of the moving parts take place at specific angular positions rather than at specific times. For this reason, having a geometrical reference, such as the one provided by an encoder, and studying the Instantaneous Angular Speed (IAS) variations can provide a large amount of information about the health status of the machine. In fact, from the variation of the IAS during the machine loads’ cycle it is possible to identify defects and faults. The current work focuses on the estimation of the IAS through the Elapsed Time (ET) method, using a counter in order to measure the time elapsed between the pulses of an encoder. Both IAS and vibration measurement are conducted on an asynchronous four poles electrical motor driven by 50 Hz line current, without load. The study compares the order analysis of both signals. The bearing’s Fundamental Train Frequency is detected using IAS estimation.

This contribution describes the application of differential geometry and nonlinear systems analysis to the estimation of friction effects in a class of mechanical systems. The proposed methodology, that has been developed for the more general problem of fault detection and diagnosis, relies on adaptive filters designed with a nonlinear geometric approach to obtain the disturbance de-coupling property. The classical model of an inverted pendulum on a cart is considered as an application example, in order to highlight the complete design procedure, including the mathematical aspects of the disturbance de-coupling method as well as the feasibility and the efficiency of the approach. Thanks to accurate estimation, friction effects can also be compensated by means of a controller designed to inject the on-line estimate of friction force to the control action calculated by classical linear state feedback. This strategy, which belongs to the class of so-called Active Fault-Tolerant Control Schemes, allows to maintain existing controllers and enhance their performance by introducing an adaptive estimator of unmodeled friction forces.

Empirical Mode Decomposition (EMD) is one of the techniques that proved its efficiency for an early detection of defects in many mechanical applications like bearings and gears. The EMD methodology decomposes the original times series data into intrinsic mode functions (IMF), by using the Hilbert-Huang transform. In this study, EMD is applied to acoustic emission signals. The acoustic emission signal is heterodynined around a central high frequency in order to obtain an audible signal. Scalar statistical parameters such as Kurtosis and THIKAT are then used in this study. These statistical descriptors are calculated for each IMF. The technique is validated by experiments on a test bench with a very small crack (40 μm) on the outer race of a ball bearing. It is found that the application of time descriptors to different IMF decomposition levels gives good results for early detection of defects in comparison with the original time signal.

The signal processing techniques developed for the diagnostics of mechanical components operating in stationary conditions are often not applicable or are affected by a loss of effectiveness when applied to signals measured in transient conditions. In this chapter, an original signal processing tool is developed exploiting some data-adaptive techniques such as Empirical Mode Decomposition, Minimum Entropy Deconvolution and the analytical approach of the Hilbert transform. The tool has been developed to detect localized faults on bearings of traction systems of high speed trains and it is more effective to detect a fault in non-stationary conditions than signal processing tools based on envelope analysis or spectral kurtosis, which represent until now the landmark for bearings diagnostics.

Empirical mode decomposition (EMD) is a self-adaptive data driven technique for analyzing nonlinear and non-stationary signals and decompose them into some elementary Intrinsic Mode Functions (IMFs). Although EMD method has been applied in various applications successfully, this method has some drawbacks, i.e. lack of a mathematical base, no robust stopping criterion for sifting process, mode mixing and border effect problem. Under the practical point of view, the most relevant is possibly the sifting stop criterion. Although sifting as many times as possible is needed to decompose the signal, too many sifting steps will reduce the physical meaning of IMFs. To preserve the natural amplitude variations of the oscillations, sifting must be limited to as few steps as possible. The proposed criteria so far are: Cauchy-type convergence, three-threshold, energy difference tracking, resolution factor, bandwidths, and orthogonality criterion. There is not a thorough study yet regarding the fault diagnosis application, to determine the effects of stopping criteria on the fault detection performance. In this chapter, the influence of different criteria to this purpose is investigated.

Vibration signals can be successfully captured and analyzed for quality control at the end of the production line. Various signal processing techniques and their applications are presented in this paper. These applications demonstrate the importance of selecting proper signal processing tools in order to extract the most reliable information from the signals. The presented applications regards tooth fault detection in helical gears and the detection of assembly faults in diesel engines by means of cold test technology.

Vibration signals can be successfully captured and analyzed for quality control at the end of the production line. Various signal processing techniques and their applications are presented in this paper. These applications demonstrate the importance of selecting proper signal processing tools in order to extract the most reliable information from the signals. The presented applications regards ball bearings and threshing process in harvesting machines.

A reliable evaluation of technical condition of machinery working under non-stationary conditions requires a rigorous tracking of operational parameters. Therefore, modern condition monitoring systems (CMS) enable reading and registering of process parameters (e.g. speed, load, pressure, etc.) in parallel with acquisition of vibroacoustic signals. Although few tries have been undertaken to develop state-free analysis of vibration signals, currently installed systems still do rely on state-preclassified data. The paper shows how the process, referential data might be automatically transformed into proposition of optimal machine operational states in terms of their number and their range. As indicated by the title, the paper shows common pitfalls coming from implementation of popular clustering approach. The proposed algorithm illustrates is verified on real data from a pitch-controlled wind turbine.

In this paper we suggest two simple approximate methods to estimate the unknown terms of incomplete spectral or correlation matrices, when the cross-spectra or cross-correlations available from multiple measurements do not cover all pairs of transducer locations. The proposed techniques may be applied whenever the available data includes the auto-spectra at all measurement locations, as well as selected cross-spectra which implicates all measurement locations. The suggested formulae can also be used for checking the consistency between the spectral or correlation functions pertaining to measurement matrices, in cases of suspicious data. After presenting the proposed formulations, we discuss their merits and limitations. Then we illustrate their use on a realistic simulation of a multi-supported tube subjected to turbulence excitation from cross-flow.

Condition monitoring is useful to describe the machine state under current operating regimes, especially, when non-stationary operating conditions appears. Nevertheless, in actual applications the faulty data are not always available. This paper proposes a novel methodology for condition monitoring using dynamic features and one-class classifiers. The dynamic features set comprises the spectral sub-band centroids and linear frequency cepstral coefficients computed from time–frequency representations. A one-class classification stage is carried out to validate the performance of the dynamic features and commonly used statistical features as descriptors of the machine state. Proposed methodology is evaluated by using a test rig, which is composed by outliers (unbalance and misalignment) and target objects (undamaged state). The data set is obtained under variable speed conditions including start-up and coast-down. The attained results outperform other state-of-the-art extracted parameters and the methodology is robust to large speed fluctuations in the machine.

Noise and vibration performance plays an important role in the development of rotating components, such as engines, drivelines, transmission systems, compressors and pumps. The presence of torsional vibrations and other specific phenomena require the dynamic behaviour of systems and components to be designed accurately in order to avoid comfort and durability related problems. This paper provides an overview of the instrumentation and challenges related to torsional vibration testing. The accuracy and performance of various measurement techniques are investigated by measurements on a Fiat Punto 1.4 l engine. The potential sources of error are discussed for each technique.

Systems and techniques for fast damage detection play a fundamental role in the development of effective Structural Health Monitoring systems. Modal-based damage detection algorithms are well-known techniques for structural health assessment but they need reliable and accurate automated modal identification and tracking procedures in order to be effective. In this chapter, the performance of a recently developed algorithm for automated output-only modal parameter estimation is assessed. An extensive validation of the algorithm for continuous monitoring applications is carried out based on simulated data. Different levels of damping are considered. The numerical study demonstrates that the algorithm provides fairly robust, accurate and precise estimates of the modal parameters, including damping ratios.

This paper describes the principles of operation of equipment design which may be used in the field of application of vibration technology for efficient separation of free-flowing dry materials on the thickness differences of the solids present in the mixture. An initial prototype equipment is constructed to simulate actual operating conditions for monitoring the condition of machinery and for selecting a suitable design of equipment characterized by properly selected types of components. The inventive aspect of process design includes development of completely new separation method and the arrangement of the equipment design for application in operation processes depending essentially on characteristics of oscillation in a mechanical system. Through mathematical modelling, a better understanding of the processes motivated by the rectangular acceleration of translational vibrational motion and steps in exact solution of transcendental equations of particle movement are presented. Design methods using analytical procedures is profitable in general for theoretical prediction. Technical feasibility of some types of suitable designs are used for the purpose of studying the effects of rectangular acceleration of vibration on particle size separation which may be attractive from engineering perspective.

In this paper a back-to-back test bench for experimental characterization of planetary gear transmissions is described. Some considerations related to the design of this kind of devices as well as the compromises and general features of the proposed design are presented. Particular attention is given to the instrumentation layout and the alternatives foreseen for measurement. In the last chapter, some results are presented in order to show the capabilities of the bench configuration.

This paper describes the design aspects for a new gear test rig aimed at adjusting the influence of shaft compliance on gear meshing stiffness, while allowing the operator to impose gear misalignments. Static and dynamic testing is possible for the most important gear-related physical quantities: Transmission Error, relative displacements, tooth root strain, transmitted torque. The discussed test rig has a mechanical power circulation arrangement, where two sides can be identified. One side is dedicated to testing a cylindrical gear pair; the other side is needed for retaining a torque preload in the system by means of a second gear pair. Relative misalignment can be imposed between the test gears in the five possible degrees of freedom (three parallel misalignments, two angular misalignments). Shaft compliance can be adjusted by setting the axial position of the gears before fastening them to the shafts.

This paper describes the instrumentation aspects for a new parallel cylindrical gear test rig aimed at adjusting the influence of shaft compliance on gear meshing stiffness, while allowing the operator to impose gear misalignments. Static and dynamic testing is possible for the most important gear-related physical quantities. Transmission Error can be measured in a wide frequency range by high-resolution analogue encoders, low-cost digital encoders and accelerometers attached to the test gears. The latter are also used to obtain dynamic relative displacements between the gears in 6° of freedom. Tooth root strain is measured by a linear pattern of strain gauges, either wired in a quarter-bridge configuration or with direct measurement, to capture axial strain distribution. Signals from rotating sensors are fed out to the stationary acquisition system by means of slip rings. Transmitted torque is measured using a rotating torque flange with contactless signal transmission to its stator.

The aim of this paper is to describe the architecture of a distributed control system designed to handle several heliostats in a solar concentration field. The system foresees the use of solar power to achieve the necessary temperature to activate a dry reforming process in order to convert landfill gasses into hydrogen that will be exploited for energy production. Between the manifold solar concentration technologies the Central Tower Heliostat Field was adopted. This paper includes a first section regarding the most common geometry of solar receivers and concentrators and a set of equations for the main sun tracking techniques. The chosen hardware components and the software solutions will be illustrated as well.

The efficiency of water supply networks is an important issue. In order to reduce water losses, policies of leak reduction are essential. The paper deals with a preliminary study on the use of vibration monitoring tools for the detection of leaks in water service pipelines. The long-term project is the development of a system for automatically detecting burst leaks occurring in service pipes. Preliminary experimental tests were performed on both a test rig and an actual service pipe of the water distribution system. Three main objectives were achieved: firstly, the effectiveness of vibration monitoring for leak detection purposes was assessed providing a positive response; then, a prototypal detection procedure was studied, implemented and tested on the preliminary experimental data; finally, the specifications for a prototypal acquisition equipment were also determined. This paper illustrates the experimental campaign and its main results.

Due to fatigue cracks appearing in atypical places at construction of bucket-wheel excavator’s body, it was supposed that they are a consequence of transient vibrations associated with impulse loadings. This work presents a procedure for identification of such phenomenon. Operating loadings as well as vibration of bucket-wheel excavators’ structures are strongly random and nonstationary, what makes any analysis difficult. Moreover they reveal continuous changes in the structure of the frequency spectrum. For that reason the procedure was based on statistical measures and relativity of power spectral densities of vibration in succeeding time periods. As a result it was found that indeed transient vibration accompany impulse loading, but it isn’t full correlation, and it also happens for quite low impulses.

This paper assesses the application of different processing techniques on acceleration signals extracted from faulty heavy duty wheels. Heavy duty wheels are used in applications as automatic vehicles and are mainly composed of a polyurethane tread glued to a cast iron hub. The adhesive application between tread and hub is the most critical assembly phase, since it is completely made by an operator and a contamination of the link area may happen. Furthermore the presence of rust on the hub surface can contribute to worsen the adherence interface, reducing the operating life. Several wheels with different types of faults have been manufactured ‘ad hoc’ with anomalies similar to the ones that can really be originated. Synchronous average is calculated over the wheel rotation in order to highlight the phenomena that have the wheel rotation as periodicity (e.g. the contact between defect and test bench drum). Successively, cyclostationary theory is applied to extract information from the frequency/order domain of the processed signals. Eventually, well-suited indicators/coefficients are applied to the processed signals, objectifying the anomaly presence and defining pass-fail reference values based on the non-statistical Tukey’s method.

S6-high rolling mill is an advanced mode to work the steel: it allows the use of very small work rolls laterally guided by individually adjustable side support rolls, which are supported by two rows of roller bearings mounted in cassettes. In this paper the vibrations generated in a S6-high cold rolling mill are analyzed with the aim to investigate the problem of skid marks generation. Such marks are regular, parallel marking across the width of strip metal that not only significantly affects the mill performance, but also reduces surface quality of the strip steel. The defects of the strip are the consequence of insurgence of vibrations, generically denominated ‘chatter’. The analyzed rolling mill has six rolls able to roll steel strip coming directly from hot rolling mill train. The purpose of the present work is to identify the reason of the excitation in order to limit the problem. A solution based on empirical observations, vibration analysis and considerations of a model is proposed.

Paper presents the approach of testing of heavy duty gearboxes dedicated to mining machinery. Main focus is on non-stationary operational conditions and advanced monitoring of vibration, process parameters and other signals connected with gearbox operation such as temperatures, pressure and flow in cooling system. Famur’s Group advanced test rig is presented with description of its technical parameters and advanced control and monitoring system. The system features unique capabilities in scope of system dynamics enabling to simulate overloads of impacts often present in real life mining machine load characteristics. The case study shows exemplary test performed on heavy duty gearbox of 250 kW power both with recorded parameters and analysis results. Observed dependencies between vibration and time-varying process parameters were presented and discussed. Currently Famur Group develops and implements several signal analysis algorithms, taking into account recent research in this field. Further development of the test rig will allow to verify suitability of these methods in industrial systems for monitoring machines working in non-stationary operational conditions. This verification is possible thanks to ability of simulation of conditions comparable with real-life machine operation.

Rolling Elements Bearing (REB) condition monitoring is mainly based on the analysis of acceleration (vibration) signal in the load direction. This is one of the three components of the acceleration vector in 3D space: the main idea of this paper is the recovery of additional fault information from all the three acceleration vector components by combining them to obtain the modulus of the spatial acceleration (SAM) vector. The REB diagnostic performances of the SAM are investigated and compared to the load direction vibration by means of two rough estimators of the “Signal-to-Noise” ratio (SNR) and the Spectral Kurtosis. The SAM provides a higher SNR than the single load direction. Finally, Spectral Kurtosis driven Envelope analysis is performed for further comparison of the two signals: its results highlight that demodulation of the SAM isn’t strictly necessary to extract the fault features.

The vector control of single phase induction machine using conventional decoupling approaches has a remarkable decrease of rotor flux when the machine is powered by a real voltage source inverter. To solve this problem, we propose as an efficient solution, a decoupling approach based on artificial neural networks in the vector control block of single phase induction machine. The application of this approach to the single-phase machine increases its dynamic performance and constitutes a contribution to the study of this machine. Indeed, this type of machine has not yet taken its whole share from various works present until now, compared to the three-phase induction machine. For three different decoupling approaches with two types of supply: perfect voltage source and real voltage source inverter; a comparative study through numerical simulations is presented. The simulation results show the feasibility and good performance obtained by the proposed approach.

The paper reviews the main faults identification of electric machines based on amplitude versus time and amplitude versus frequency vibration analysis. The aim of the paper is to introduce a new method in determining rotor faults based on stator vibration. The analysis method uses a multi-channel vibration analyzer provided with three accelerometers, set in a plane perpendicular to the stator axis. The method is suitable for determining rotor faults generated by the stator vibrations, other than those due to current harmonic components or supply voltage unbalance. The SvanPC software allowed the determination of the characteristics regarding velocity vibration versus time and versus frequency. Their interpretation permitted to determine the fault points on electrical machines in two industrial applications.

In this paper, we present fault prognosis and diagnosis technique in a three phase asynchronous machine. Based on statistical analysis of scalar indicator resulting from the TSA method (Time Synchronous Averaging), this technique will be dedicated to condition monitoring of machines. In addition, spectral analysis, using the Fast Fourier Transform (FFT) algorithm of the stator- current signature MCSA (Motor Current Signature Analysis), determines their frequency composition, and therefore, allows finding the spectral lines associated to the fault. This work highlights our first results related to the comparison of the spectral representation of the stator current and that of the residual current obtained by the TSA method. We proved the effectiveness of these techniques by simulation and experimental tests made on a wound rotor induction machine. The fault rotor is taken into account by an additional resistance of one of the rotor phases. The results of simulations and experiments underline the practical utility of this method.

The monitoring of wind energy production is fundamental to improve the performances of a wind farm during the operational phase. In order to perform reliable operational analysis, data mining of all available information spreading out from turbine control systems is required. In this work a Supervisory Control and Data Acquisition (SCADA) data analysis was performed on a small wind farm and new post-processing methods are proposed for condition monitoring of the aerogenerators. Indicators are defined to detect the malfunctioning of a wind turbine and to select meaningful data to investigate the causes of the anomalous behaviour of a turbine. The operating state database is used to collect information about the proper power production of a wind turbine, becoming a tool that can be used to verify if the contractual obligations between the original equipment manufacturer and the wind farm operator are met. Results demonstrate that a proper selection of the SCADA data can be very useful to measure the real performances of a wind farm and thus to define optimal repair/replacement and preventive maintenance policies that play a major role in case of energy production.

Operational Modal Analysis (OMA), also known as output-only modal analysis, allows identifying modal parameters only by using the response measurements of the structures in operational conditions when the input forces cannot be measured. These information can then be used to improve numerical models in order to monitor the operating and structural conditions of the system. This is a critical aspect both for condition monitoring and maintenance of large wind turbines, particularly in the off-shore sector where operation and maintenance represent a high percentage of total costs. Although OMA is widely applied, the wind turbine case still remains an open issue. Numerical aeroelastic models could be used, once they have been validated, to introduce virtual damages to the structures in order to analyze the generated data. Results from such models can then be used as baseline to monitor the operating and structural condition of the machine.

Power generation performance is a fundamental metric that all wind farm operators use to determine whether expected power throughput is actually being met. IEC 61400-12-1 has been drafted as an exhaustive power performance measurement scheme for wind turbines. The primary weakness of such a standard is the required level of depth of the associated performance tests, which is more than sufficient for operators to use to run daily wind farm activities. In addition, since this IEC test is not really meant for frequent evaluation, it also fails to capture any loss in power generation performance over time. This paper addresses the aforementioned weaknesses of the IEC standard by the application of data-driven approach to model a wind turbine’s power curve. A set of measurements during a known good condition is utilized to setup a baseline model. Regular power curve measurements are then compared while taking into account the multi-regime dynamics of the turbine. The approach was implemented using NI LabVIEW’s Watchdog Agent® Toolkit and was successfully validated using actual SCADA data collected from an on-shore wind turbine.

In recent years wind energy is the fastest growing branch of the power generation industry. The largest cost for the wind turbine is its maintenance. A common technique to decrease this cost is a remote monitoring based on vibration analysis. Growing number of monitored turbines requires an automated way of support for diagnostic experts. As full fault detection and identification is still a very challenging task, it is necessary to prepare an “early warning” tool, which would focus the attention on cases which are potentially dangerous.

In this paper is presented the implementation of a software for the analysis of vibration generated by wind turbines components. This software application was built in LabWiew programming environment and for vibration analysis and fault detection were used techniques as: wavelet analysis, envelope detection, FFT analysis, Cepstrum analysis, Vector RMS analysis. Vibration signal envelope detection was performed using a virtual instrument based on Hilbert transform and rms analysis was performed using virtual instrument “Vector RMS” specifically designed for vibration analysis. The experimental results were obtained by measuring the vibration signals of a bearing with a simulated fault on outer raceway.

In the paper the results of the characterization of the chatter phenomenon in the band sawing process are presented. In particular, the influence of the cutting speed and of the distance between the cutting blade supports on chatter characteristics was investigated. In addition to the cutting forces, and emitted sound, the machine vibrations described by the measured acceleration signals were used to characterize the chatter. Based on an analysis of these signals, a hysteresis of the chatter onset and chatter die-out cutting speeds was observed. The observed chatter hysteresis indicates that the chatter onset in band sawing is caused by a Hopf-like bifurcation, and that cutting speed is a promising parameter for chatter control. Additionally a strong effect on chatter characteristics of the distance between the cutting blade supports was experimentally confirmed.