Predicting the Remaining Service Life of Railroad Bearings: Leveraging Machine Learning and Onboard Sensor Data

By continuously monitoring train bearing health in terms of temperature and vibration levels of bearings tested in a laboratory setting, statistical regression models have been developed to establish relationships between the sensor-acquired bearing health data with several explanatory factors that potentially influence the bearing deterioration. Despite their merits, statistical models fall short of reliable prediction accuracy levels since they entail restrictive assumptions, such as a priori known functional relationship between the response and input variables. A data-driven machine learning algorithm is presented, which can unravel the nonlinear deterioration model purely based on the bearing health data, even when the structure is not apparent. More specifically, a Gradient Boosting Machine is trained using vast amounts of laboratory data collected over the course of over a decade. This will help predict bearing failure, thus, providing railroads and railcar owners the opportunity to schedule preventive maintenance cycles rather than costly reactive ones

Computing Intelligence Technique and Multiresolution Data Processing for Condition Monitoring

Condition monitoring (CM) of rotary machines has gained increasing importance and extensive research in recent years. Due to the rapid growth of data volume, automated data processing is necessary in order to deal with massive data efficiently to produce timely and accurate diagnostic results. Artificial intelligence (AI) and adaptive data processing approaches can be promising solutions to the challenge of large data volume. Unfortunately, the majority of AI-based techniques in CM have been developed for only the post-processing (classification) stage, whereas the critical tasks including feature extraction and selection are still manually processed, which often require considerable time and efforts but also yield a performance depending on prior knowledge and diagnostic expertise. To achieve an automatic data processing, the research of this PhD project provides an integrated framework with two main approaches. Firstly, it focuses on extending AI techniques in all phases, including feature extraction by applying Componential Coding Neural Network (CCNN) which has been found to have unique properties of being trained through unsupervised learning, capable of dealing with raw datasets, translation invariance and high computational efficiency. These advantages of CCNN make it particularly suitable for automated analyzing of the vibration data arisen from typical machine components such as the rolling element bearings which exhibit periodic phenomena with high non-stationary and strong noise contamination. Then, once an anomaly is detected, a further analysis technique to identify the fault is proposed using a multiresolution data analysis approach based on Double-Density Discrete Wavelet Transform (DD-DWT) which was grounded on over-sampled filter banks with smooth tight frames. This makes it nearly shift-invariant which is important for extracting non-stationary periodical peaks. Also, in order to denoise and enhance the diagnostic features, a novel level-dependant adaptive thresholding method based on harmonic to signal ratio (HSR) is developed and implemented on the selected wavelet coefficients. This method has been developed to be a semi-automated (adaptive) approach to facilitate the process of fault diagnosis. The developed framework has been evaluated using both simulated and measured datasets from typical healthy and defective tapered roller bearings which are critical parts of all rotating machines. The results have demonstrated that the CCNN is a robust technique for early fault detection, and also showed that adaptive DD-DWT is a robust technique for diagnosing the faults induced to test bearings. The developed framework has achieved multi-objectives of high detection sensitivity, reliable diagnosis and minimized computing complexity

Fault detection in rotating machinery using acoustic emission

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonRotating machinery is a critical asset of industrial plants worldwide. Bearings and gearboxes are two of the most common components found in rotating machinery of industrial plants. The malfunction of bearings and gearboxes lead the machine to fail and often these failures occur catastrophically leading to personnel injuries. Therefore it is of high importance to identify the deterioration at an early stage. Among the techniques applied to detect damage in rotating machinery, acoustic emission has been a prevalent field of research for its potential to detect defects at an earlier stage than other more established techniques such as vibration analysis and oil analysis. However, to reliably detect the fault at an early stage de-noising techniques often must be applied to reduce the AE noise generated by neighbouring components and normal component operation. For this purpose a novel signal processing algorithm has been developed combining Wavelet Packets as a pre-processor, Hilbert Transform, Autocorrelation function and Fast Fourier transform. The combination of these techniques allows identification of g repetitive patterns in the AE signal that are attributable to bearing and gear damage. The enhancement for early stage defect detection in bearings and gears provided by this method is beneficial in planning maintenance in advance, reducing machinery down-time and consequently reducing the costs associated with bearing breakdown. The effectiveness of the proposed method has been investigated experimentally using seeded and naturally developed defects in gears and bearings. In addition, research into the optimal Wavelet Packet node that offers the best de-noising results has been performed showing that the 250-750 kHz band gives the best SNR results. The detection of shaft angular misalignment using Acoustic Emission has been investigated and compared with acceleration spectra. The results obtained show enhancements of AE in detection shaft angular misalignment over vibration analysis in SNR and stability with varying operational conditions

Automatic Fault Diagnosis Technology of Roller Bearings of High-speed Rail Based on IFD and AE

INTRODUCTION: With the development of technology and policy support, high-speed rail's temporal and spatial layout is gradually expanding, and it becomes essential to ensure high-safety operation. OBJECTIVES: The real-time correlation fault diagnosis technology of critical components of electromechanical systems of high-speed trains is analyzed, and a new method of automatic fault diagnosis based on genetic support vector machine is proposed. METHODS: In this study, the Author combines two techniques, IFD and AE, and introduces an adaptive weighting algorithm to fuse the data of the two and experimentally verify their accuracy. RESULTS: The experimental results show that in the IFD experiment, the 2-point frequency at 1050 speed is 347.6 Hz, and the 3-point frequency is 498.4 Hz, both of which are very close to the 2 and 3 times frequencies of the 1-point frequency, and the multiplicative relationship is much more straightforward. CONCLUSION: Combining IFD and AE can realize automatic and accurate diagnosis of bearing state and pre-diagnosis of bearings by adaptive weighted fusion algorithm, which is effective in the practical mechanical diagnosis of rolling bearing faults in high-speed railroads

Friction, Vibration and Dynamic Properties of Transmission System under Wear Progression

This reprint focuses on wear and fatigue analysis, the dynamic properties of coating surfaces in transmission systems, and non-destructive condition monitoring for the health management of transmission systems. Transmission systems play a vital role in various types of industrial structure, including wind turbines, vehicles, mining and material-handling equipment, offshore vessels, and aircrafts. Surface wear is an inevitable phenomenon during the service life of transmission systems (such as on gearboxes, bearings, and shafts), and wear propagation can reduce the durability of the contact coating surface. As a result, the performance of the transmission system can degrade significantly, which can cause sudden shutdown of the whole system and lead to unexpected economic loss and accidents. Therefore, to ensure adequate health management of the transmission system, it is necessary to investigate the friction, vibration, and dynamic properties of its contact coating surface and monitor its operating conditions

A Combined Numerical and Experimental Approach for Rolling Bearing Modelling and Prognostics

Rolling-element bearings are widely employed components which cover a major role in the NVH behaviour of the mechanical systems in which they are inserted. Therefore, it is crucial to thoroughly understand their fundamental properties and accurately quantify their most relevant parameters. Moreover, their inevitable failure due to contact fatigue leads to the necessity of correctly describing their dynamic behaviour. In fact, they permit to develop diagnostic and prognostic schemes, which are heavily requested in the nowadays industrial scenario due to their increasingly important role in the development of efficient maintenance strategies. As a result, throughout the years several techniques have been developed by researchers to address different challenges related to the modelling of these components. Within this context, this thesis aims at improving the available methods and at proposing novel approaches to tackle the modelling of rolling-element bearings both in case of static and dynamic simulations. In particular, the dissertation is divided in three major topics related to this field, i.e. the estimation of bearing radial stiffness trough the finite-element method, the lumped-parameter modelling of defective bearings and the development of physics-based prognostic models. The first part of the thesis deals with the finite-element simulations of rolling-element bearings. In particular, the investigation aims at providing an efficient procedure for the generation of load-dependent meshes. The method is developed with the primary objective of determining the radial stiffness of the examined components. In this regard, the main contribution to the subject is the definition of mesh element dimensions on the basis of analytical formulae and in the proposed methodology for the estimation of bearing stiffness. Then, the second part describes a multi-objective optimization technique for the estimation of unknown parameters in lumped parameter models of defective bearings. In fact, it was observed that several parameters which are commonly inserted in these models are hardly measurable or rather denoted by a high degree of uncertainty. On this basis, an optimization procedure aimed at minimizing the difference between experimental and numerical results is proposed. The novelty of the technique lies in the approach developed to tackle the problem and its peculiar implementation in the context of bearing lumped-parameter models. Lastly, the final part of the dissertation is devoted to the development of physics-based prognostic models. Specifically, two models are detailed, both based on a novel degradation-related parameter, i.e. the Equivalent Damaged Volume (EDV). An algorithm capable of extracting this quantity from experimental data is detailed. Then, EDV values are used as input parameters for two prognostic models. The first one aims at predicting the bearing vibration under different operative conditions with respect to a given reference deterioration history. On the other hand, the objective of the second model is to predict the time until a certain threshold on the equivalent damaged volume is crossed, regardless of the applied load and the shaft rotation speed. Therefore, the original aspect of this latter part is the development of prognostic models based on a novel indicator specifically introduced in this work. Results obtained from all proposed models are validated through analytical methods retrieved from the literature and by comparison with data acquired on a dedicated test bench. To this end, a test rig which was set-up at the Engineering Department of the University of Ferrara was employed to perform two type of tests, i.e. stationary tests on bearings with artificial defects and run-to-failure tests on initially healthy bearings. The characteristics of acceleration signals acquired during both tests are extensively discussed.I cuscinetti a rotolamento sono componenti meccanici che influenzano in maniera considerevole il comportamento dinamico dei sistemi all’interno dei quali sono montati. Pertanto, è di fondamentale importanza possedere strumenti atti alla stima dei loro parametri più rilevanti e avere a disposizione modelli dedicati allo studio delle loro caratteristiche dinamiche. Questo aspetto è di estrema importanza soprattutto nell’ottica dello sviluppo di schemi di diagnostica e prognostica, i quali sono sempre più richiesti all’interno dello scenario industriale odierno. In questo contesto, questa tesi si propone di migliorare le tecniche numeriche già esistenti e di fornire nuovi approcci per la modellazione dei cuscinetti a rotolamento sia nel caso di problemi statici che dinamici. Nello specifico, il lavoro tratta in maniera dettagliata tre diversi argomenti relativi a questo tema, ossia la stima della rigidezza radiale tramite il metodo agli elementi finiti, la modellazione a parametri concentrati di cuscinetti con difetti e lo sviluppo di modelli di prognostica physics-based. La prima parte della tesi concerne la simulazione di cuscinetti a rotolamento tramite il metodo ad elementi finiti. In particolare, lo studio si propone di fornire una procedura per la generazione di griglie le cui dimensioni dipendano dal carico applicato. Il metodo è sviluppato con l’obbiettivo di stimare in maniera computazionalmente efficace la rigidezza radiale del componente in esame. Pertanto, il contributo principale sul tema dato da questa prima parte riguarda il metodo analitico che permette di definire a priori le dimensioni degli elementi che costituiscono la mesh e la metodologia utilizzata per la stima della rigidezza. La seconda parte descrive una procedura di ottimizzazione multi obbiettivo per la stima dei parametri incogniti all’interno dei modelli a parametri concentrati di cuscinetti con difetti. Questa esigenza nasce dall’osservazione che numerosi parametri tipicamente inseriti in questa tipologia di modelli sono difficilmente misurabili oppure caratterizzati da un alto grado di incertezza. Perciò, nella seconda parte viene introdotta una tecnica innovativa che consente di stimare i parametri di modello che minimizzano la differenza fra risultati numerici e sperimentali in diverse condizioni di funzionamento. Infine, l’ultima parte è dedicata allo sviluppo di modelli di prognostica physics-based. A tal riguardo, vengono dettagliati due modelli basati su un nuovo indicatore di degrado del cuscinetto, denominato Equivalent Damaged Volume (EDV). Questo indicatore viene calcolato durante il funzionamento del cuscinetto tramite un algoritmo dedicato. I valori così ottenuti sono poi utilizzati come dati di input per i due modelli prognostici. Il primo mira a predire la vibrazione del cuscinetto in condizioni operative diverse rispetto ad una storia di degrado di riferimento. Diversamente, il secondo modello permette di prevedere il tempo rimanente prima del superamento di una soglia critica di volume equivalente danneggiato, indipendentemente da carico applicato e velocità di rotazione. Dunque, l’aspetto originale di quest’ultima parte ricade nello sviluppo di tecniche prognostiche basate su un nuovo indicatore introdotto ad-hoc in questo lavoro. I risultati ottenuti da tutti i modelli proposti sono validati grazie a metodi analitici di letteratura e a dati acquisiti in laboratorio per mezzo di un banco prova installato presso il Dipartimento di Ingegneria dell’Università di Ferrara. Il banco prova è stato utilizzato per realizzare due tipologie di prove, ossia test stazionari su cuscinetti che presentano difetti artificiali e prove di tipo run-to-failure su cuscinetti inizialmente sani. Le caratteristiche dei segnali di accelerazione acquisiti in entrambe le prove sono discussi in maniera esaustiva

Advanced signal processing of wayside condition monitoring of railway wheelsets

Railway transport is an efficient and environmentally benign method of transport. With global warming effects intensifying it has become more urgent that mobility and economic prosperity are maintained by delivering increased transport efficient. Hence, railway transport has a significant role to play in the forthcoming decades. Punctuality and safety of railway operations is critical in ensuring unhindered transportation for passengers and freight. Rolling stock are required to operate at higher speeds and carry heavier axle loads than ever before. This puts increased pressure to rolling stock operators and infrastructure managers in trying to avoid disruption and potential accidents which also leads to higher transportation costs. Remote condition monitoring has increased in significance for railway transport over the last few years. However, there are still a lot to be done before breakthrough remote condition monitoring technologies are delivered at commercial scale in the wider international railway network. Different remote condition monitoring systems are installed wayside in order to evaluate the structural integrity of rolling stock wheelsets, detect any potential rolling stock fault in time and minimize the likelihood of a serious railway accident. The existing wayside condition monitoring system are based on infrared cameras, acoustic arrays and strain gauges. Despite significant investments by the rail industry in this area, false alarms can still occur and many of condition monitoring systems are able to detect faults once they become critical. In the present thesis, a novel approach based on integration of acoustic emission and vibration analysis together with advanced signal progressing is detailed. Tests ranging from laboratory tests under controlled conditions, all the way up to trials under actual operational conditions in the UK network have been carried out, yielding promising results. The experimental methodology employed has shown that acoustic emission is particularly efficient in detecting and ranking potential axle bearing defects. When acoustic emission is coupled with vibration analysis, it is possible to detect axle bearing defects whilst avoiding misinterpretation of wheel flats for axle bearing defects. The results obtained suggest that the widespread use of the reported methodology in the railway is feasible. The novel RCM system can enhance the reliability, availability, maintainability and safety of rolling stock wheelsets. Experimental work have been carried out under actual operational conditions in UK rail network at Cropredy, at Chiltern Railway line. The novel RCM system has been installed adjacent to Hot Box Axle Detector for comparison purposes. No interference on the track circuits is the main advantage of the proposed system. During the signal processing module of the system, freight and passenger train waveforms were identified to contain evidence of potential bearing faults. The results still require follow up validation from Network Rail. Time, frequency and time-frequency analysis have been applied to the acquired data. High amplitude peaks and signal modulation were visible at raw data. The acquired signals were transferred to frequency domain. Harmonics in frequency distribution were clearly seen. These frequency bands can be used as a reference for the band pass filter at HFRT process. HFRT algorithm has been effectively applied in the captured data in order to identify the fundamental fault frequency and its harmonics. Sidebands were also visible. TSK analysis was also applied in the raw signals. Frequency bands with high kurtosis values can be used as a reference for further analysis. In addition, laboratory experiments at University of Birmingham and Long Marston trials under controlled conditions have been carried out in order to evaluate the reliability of the system in early diagnosis of wheel and axle bearing defects. Acoustic emission and vibration signals have been collected. From the results obtained, it has effectively demonstrated that fault detection can be achieved using the frequency distribution of the signal. Defect type evaluation can be carried out by detecting the fundamental fault frequency at the HFRT process and fault quantification can achieved by Normalized Moving RMS analysis. In summary, the research contribution of this work is presented below: $\bullet$ Development and assessment of the vibroacoustic condition monitoring system for railway wheelsets. Experimental methodology and results considered in this study are the main contributions in the literature of this field. $\bullet$ In service passenger and freight trains have been monitored. Detection of potential bearing faults has been achieved. $\bullet$ Novel methodology applied at the acquired in-service data in order to determine the appropriate frequency range for the band-pass filter during the HFRT process. Frequency bands with high kurtosis values can be used as a reference for the band-pass filter. In addition, harmonics have been presented in frequency distribution of the signal. The frequency bands that harmonics were appeared can also be used for the design of the band-pass filter. $\bullet$ Comparison between advanced signal processing techniques using laboratory, in-field and in in-service signals. Detection of wheelsets faults, identification of type of the defect and quantification of fault severity can be achieved when combination of algorithm is applied at raw signals

The Optimization of Vibration Data Analysis for the Detection and Diagnosis of Incipient Faults in Roller Bearings

The rolling element bearing is a key component of many machines. Accurate and timely diagnosis of its faults is critical for proactive predictive maintenance. The research described in this thesis focuses on the development of techniques for detecting and diagnosing incipient bearing faults. These techniques are based on improved dynamic models and enhanced signal processing algorithms. Various common fault detection techniques for rolling element bearings are reviewed in this work and a detailed experimental investigation is described for several selected methods. Envelope analysis is widely used to obtain the bearing defect harmonics from the spectrum of the envelope of a vibration signal. This enables the detection and diagnosis of faults, and has shown good results in identifying incipient faults occurring on the different parts of a bearing. However, a critical step in implementing envelope analysis is to determine the frequency band that contains the signal component corresponding to the bearing fault (the one with highest signal to noise ratio). The choice of filter band is conventionally made via manual inspection of the spectrum to identify the resonant frequency where the largest change has occurred. In this work, a spectral kurtosis (SK) method is enhanced to enable determination of the optimum envelope analysis parameters, including the filter band and centre frequency, through a short time Fourier transform (STFT). The results show that the maximum amplitude of the kurtogram indicates the optimal parameters of band pass filter that allows both outer race and inner race faults to be determined from the optimised envelope spectrum. A performance evaluation is carried out on the kurtogram and the fast kurtogram, based on a simulated impact signal masked by different noise levels. This shows that as the signal to noise ratio (SNR) reaches as low as -5dB the STFT-based kurtogram is more effective at identifying periodic components due to bearing faults, and is less influenced by irregular noise pulses than the wavelet based fast kurtogram. A study of the accuracy of rolling-bearing diagnostic features in detecting bearing wear processes and monitoring fault sizes is presented for a range of radial clearances. Subsequently, a nonlinear dynamic model of a deep groove ball bearing with five degrees of freedom is developed. The model incorporates local defects and clearance increments in order to gain the insight into the bearing dynamics. Simulation results indicate that the vibrations at fault characteristic frequencies exhibit significant variability for increasing clearances. An increased vibration level is detected at the bearing characteristic frequency for an outer race fault, whereas a decreased vibration level is found for an inner race fault. Outcomes of laboratory experiments on several bearing clearance grades, with different local defects, are used herein for model validation purposes. The experimental validation indicates that the envelope spectrum is not accurate enough to quantify the rolling element bearing fault severity adequately. To improve the results, a new method has been developed by combining a conventional bispectrum (CB) and modulation signal bispectrum (MSB) with envelope analysis. This suppresses the inevitable noise in the envelope signal, and hence provides more accurate diagnostic features. Both the simulation and the experimental results show that MSB extracts small changes from a faulty bearing more reliably, enabling more accurate and reliable fault severity diagnosis. Moreover, the vibration amplitudes at the fault characteristic frequencies exhibit significant changes with increasing clearance. However, the vibration amplitude tends to increase with the severity of an outer race fault and decrease with the severity of an inner race fault. It is therefore necessary to take these effects into account when diagnosing the size of a defect