Algorithms for Fault Detection and Diagnosis

Due to the increasing demand for security and reliability in manufacturing and mechatronic systems, early detection and diagnosis of faults are key points to reduce economic losses caused by unscheduled maintenance and downtimes, to increase safety, to prevent the endangerment of human beings involved in the process operations and to improve reliability and availability of autonomous systems. The development of algorithms for health monitoring and fault and anomaly detection, capable of the early detection, isolation, or even prediction of technical component malfunctioning, is becoming more and more crucial in this context. This Special Issue is devoted to new research efforts and results concerning recent advances and challenges in the application of “Algorithms for Fault Detection and Diagnosis”, articulated over a wide range of sectors. The aim is to provide a collection of some of the current state-of-the-art algorithms within this context, together with new advanced theoretical solutions

Diagnosis of low-speed bearings via vibration-based entropy indicators and acoustic emissions

Tesi del Pla de doctorat industrial de la Generalitat de Catalunya. Tesi en modalitat compendi de publicacions, amb diferents seccions retallades per drets dels editorsWind energy is one ofthe main renewable energies to replace fossil fuels in the generation of electricityworldwide. To enhance and accelerate its implementation at a large scale, it is vital to reduce the costs associated with maintenance. As com ponent breakages force the turbine to stop for long repair times, the wind industry m ust switch from the old-fashioned preventive or corrective maintenance to condition-based maintenance (also called predictive maintenance). The condition­based maintenance of pitch bearings is especiallychallenging, as the operating conditions include high mechanical stress and low rotational speed. Since these operating conditions im pact negatively on the results of the standard methods and techniques applied in current condition-based monitoring systems, the condition-based maintenance of pitch bearings is still a challenge. Therefore, this thes is is focused on the research of novel methods and techniques that obtain reliable information on the state of pitch bearings for condition-based maintenance. lnitially, the acknowledgment ofthe state ofthe art is performed to recognize the methods and signals. This step endorses the decision to analyze the vibration signals and acoustic emissions throughout this thesis. Due to the particular operating conditions of pitch bearings, this research states the need to create data sets to replicate the particular operating conditions in a controlled laboratory experiment. As a res ult, a datas et based on vibrations, and a second datas et based on acoustic emissions are generated. The vibration datas et allows the validation of a novel algorithm for the low-speed bearing diagnosis, which is based on the concept of entropy by the definition of Shannon and Rényi. In com parison to the classical methods found in the literature, the diagnosis of low-speed bearings based on entropy-based indicators can extract more reliable information. Moreover, the research of the com bination of several indicators to improve the diagnosis revea Is that the entropy-based indicators can extract more information than regular indicators used in academia. The datas et of acoustic emissions from low-speed bearings helps to contribute to the development of methods for diagnosis. In this research, the analysis of the energyfrom the signals reveals a dependencyon the intensityand the presence of damage. In addition, a relation between the waveform ofthe analyzed energy and the existence of damage is em phas ized.La energía eólica es una de las principales energías renovables consideradas para reemplazar los combustibles fósiles en la generación de electricidad a nivel mundial. Para mejorar y acelerar su implementación a gran escala, es vital reducir los costes asociados con el mantenimiento. Como las roturas de los componentes obligan a la turbina a detenerse durante largos períodos de reparación, la industria eólica necesita cambiar del anticuado mantenimiento preventiv o correctivo al mantenimiento basado en la condición (también llamado mantenimiento predictivo). El mantenimiento basado en la condición de los rodamientos pitch es especialmente desafiante, porque las condiciones de operación incluyen un alto estrés mecánico y bajas velocidades de rotación. Debido a que estas condiciones de operación impactan negativamente en los resultados de los métodos y técnicas estándar aplicados en los sistemas actuales de monitoreo basados en el estado, el mantenimiento basado en el estado de los rodamientos pitch sigue siendo un desafío. Por tanto, esta tesis se centra en la investigación de métodos y técnicas novedosas que obtengan información fiable sobre el estado de los rodamientos pitch para el mantenimiento basado en la condición. Inicialmente, se realiza el reconocimiento del estado del arte para reconocer los métodos y señales utilizados. Este paso avala la decisión de analizar las señales de vibración y las emisiones acústicas a lo largo de esta tesis. Debido a las condiciones de funcionamiento particulares de los rodamientos pitch, esta investigación reconoce la necesidad de crear un conjunto de datos para replicar las condiciones de funcionamiento particulares del rodamiento pitch en una experiencia de laboratorio controlado. Como resultado, se genera un conjunto de datos basado en vibraciones y un segundo conjunto de datos basado en emisiones acústicas. El conjunto de datos de vibraciones permite la validación de un algoritmo novedoso para el diagnóstico de rodamientos de baja velocidad, el cual se basa en el concepto de la entropía según la definición de Shannon y Rényi. En comparación con los métodos clásicos que se encuentran en la literatura, el diagnóstico de rodamientos de baja velocidad basado en indicadores basados en la entropía puede extraer información más confiable. Además, la investigación de la combinación de varios indicadores para mejorar el diagnóstico revela que los indicadores basados en la entropía pueden extraer más información que los indicadores habituales utilizados en la academia. El conjunto de datos de las emisiones acústicas de los rodamientos de baja velocidad ayuda a contribuir al desarrollo de métodos de diagnóstico. En esta investigación, el análisis de la energía de las señales revela una dependencia de la intensidad y la presencia de daño. Además, se enfatiza una relación entre la forma de onda de la energía analizada y la existencia de daño.L'energia eòlica és una de les principals energies renovables considerades per reemplaçar els combustibles fòssils en la generació d'electricitat a nivell mundial. Per millorar i accelerar la seva implementació a gran escala, és vital reduir els costos associats amb el manteniment. Com els trencaments dels components obliguen a la turbina a aturar-se durant llargs períodes de reparació, la industria eòlica necessita canviar de l'antiquat manteniment preventiu o correctiu al manteniment basat en la condició (també anomenat manteniment predictiu). El manteniment basat en la condició dels rodaments de pas és especialment desafiant, perquè les condicions d’operació inclouen un alt estrès mecànic i baixes velocitats de rotació. A causa de que aquestes condicions d’operació impacten negativament en els resultats dels mètodes i tècniques estàndard aplicats en els sistemes actuals de monitorització basats en l'estat, el manteniment basat en l'estat dels rodaments de pas segueix sent un desafiament. Per tant, aquesta tesi se centra en la investigació de mètodes i tècniques noves que obtinguin informació fiable sobre l'estat dels rodaments de pas per al manteniment basat en la condició. Inicialment, es realitza el reconeixement de l'estat de l'art per reconèixer els mètodes i senyals utilitzats. Aquest pas avala la decisió d'analitzar els senyals de vibració i les emissions acústiques al llarg d'aquesta tesi. A causa de les condicions de funcionament particulars dels rodaments de pas, aquesta investigació reconeix la necessitat de crear un conjunt de dades per replicar les condicions de funcionament particulars del rodament de pas en un experiment de laboratori controlat. Com a resultat, es genera un conjunt de dades basat en vibracions i un segon conjunt de dades basat en emissions acústiques. El conjunt de dades de vibracions permet la validació d'un algoritme nou per al diagnòstic de rodaments de baixa velocitat, el qual es basa en el concepte de l'entropia segons la definició de Shannon i Renyi. En comparació amb els mètodes clàssics que es troben a la literatura, el diagnòstic de rodaments de baixa velocitat basat en indicadors basats en l'entropia pot extreure informació més fiable. A més, la investigació de la combinació de diversos indicadors per millorar el diagnòstic revela que els indicadors basats en l'entropia poden extreure més informació que els indicadors habituals utilitzats en la literatura. El conjunt de dades de les emissions acústiques dels rodaments de baixa velocitat ajuda a contribuir al desenvolupament de mètodes de diagnòstic. En aquesta investigació, l’anàlisi de l'energia de les senyals revela una dependència de la intensitat i la presència de dany. A més, s'emfatitza una relació entre la forma d'ona de l'energia analitzada i l’existència de dany.Energia eolikoa mundu mailan elektrizitatea sortu eta erregai fosilak ordezkatzeko energia berriztagarri nagusietako bat da. Eskala handiko ezarpena hobetu eta bizkortzeko, ezinbestekoa da mantentze-lanekin lotutako kostuak murriztea. Osagaien hausturek turbina konponketa-aldi luzeetan gelditzera behartzen dutenez, industria eolikoak mantentze-lan prebentibo edo zuzentzaile zaharkitutik egoeran oinarritutako mantentzelanetara aldatu behar du (mantentze-lan prediktiboa ere esaten zaio). Pitch errodamenduen egoeran oinarritutako mantentzea bereziki desa atzailea da, tentsio mekaniko handiak jasaten baitituzte eta errotazio-abiadura txikietan egoten baitira abian. Operaziobaldintza horiek eragin negatiboa dutenez egoeran oinarritutako egungo monitorizazio sistemetan erabiltzen diren metodo eta teknika estandarren emaitzetan, pitch errodamenduen egoeran oinarritutako mantentze-lanak erronka bat izaten jarraitzen du. Tesi hau egoeran oinarritutako mantenurako pitch errodamenduen egoerari buruzko informazio dagarria lortzen duten metodo eta teknika berritzaileen ikerketan oinarritzen da. Hasieran, teknologiaren egungo egoera aztertzen da, erabilitako metodoak eta seinaleak ezagutzeko. Urrats honek tesi honetan zehar bibrazio-seinaleak eta emisio akustikoak aztertzeko erabakia bermatzen du. Pitch errodamenduen funtzionamendu baldintza bereziak direla eta, ikerketa honek adierazten du beharrezkoa dela datu multzo bat sortzea pitch errodamenduaren funtzionamendu baldintza partikularrak erreplikatzeko laborategi kontrolatuko testuinguru batean. Ondorioz, bibrazioetan oinarritutako datu-multzo bat eta emisio akustikoetan oinarritutako bigarren datu-multzo bat sortzen dira. Bibrazioen datu-multzoak abiadura txikiko errodamenduen diagnostikorako algoritmo berritzaile bat baliozkotzea ahalbidetzen du, zeina entropiaren kontzeptuan oinarritzen baita Shannon eta R enyiren de nizioaren arabera. Literaturan dauden metodo klasikoekin alderatuta, entropian oinarritutako adierazleek abiadura txikiko errodamenduen diagnostikorako informazio dagarriagoa atera dezakete. Gainera, diagnostikoa hobetzeko hainbat adierazleren konbinazioaren ikerketak agerian uzten du entropian oinarritutako adierazleek akademian erabiltzen diren ohiko adierazleek baino informazio gehiago atera dezaketela. Abiadura txikiko errodamenduen emisio akustikoen datu multzoak diagnostiko metodoak garatzen laguntzen du. Ikerketa lan honetan, seinaleen energiaren azterketak intentsitatearekiko eta kaltearen presentziarekiko dependentzia adierazten du. Gainera, aztertutako energiaren uhin-formaren eta kaltearen arteko erlazioa nabarmentzen da.Postprint (published version

An improved higher-order analytical energy operator with adaptive local iterative filtering for early fault diagnosis of bearings

Early fault diagnosis in rolling bearings is crucial to maintenance and safety in industry. To highlight the weak fault features from complex signals combined with multiple interferences and heavy background noise, a novel approach for bearing fault diagnosis based on higher-order analytic energy operator (HO-AEO) and adaptive local iterative filtering (ALIF) is put forward. HO-AEO has better effect in dealing with heavy noise. However, it is subjected to the limitation of mono-components. To solve this limitation, ALIF is adopted firstly to decompose the nonlinear, non-stationary signals into multiple mono-components adaptively. In the next, the resonance frequency band as the optimal intrinsic mode function (IMF) is selected according to the maximum kurtosis. In the following, HO-AEO is utilized to highlight weak fault characteristics of the selected IMF. Finally, the early bearing fault is diagnosed by the energy operator spectrum based on fast Fourier transform (FFT). Comparisons in the simulation indicate that the fourth order HO-AEO shows the best performance in fault diagnosis compared with Teager energy operator (TEO), analytic energy operator (AEO), the second and the third order HO-AEO. The simulated test and experimental results demonstrate that the proposed approach could effectively extract weak fault characteristics from contaminated vibration signals

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

Application of variational mode decomposition in vibration analysis of machine components

Monitoring and diagnosis of machinery in maintenance are often undertaken using vibration analysis. The machine vibration signal is invariably complex and diverse, and thus useful information and features are difficult to extract. Variational mode decomposition (VMD) is a recent signal processing method that able to extract some of important features from machine vibration signal. The performance of the VMD method depends on the selection of its input parameters, especially the mode number and balancing parameter (also known as quadratic penalty term). However, the current VMD method is still using a manual effort to extract the input parameters where it subjects to interpretation of experienced experts. Hence, machine diagnosis becomes time consuming and prone to error. The aim of this research was to propose an automated parameter selection method for selecting the VMD input parameters. The proposed method consisted of two-stage selections where the first stage selection was used to select the initial mode number and the second stage selection was used to select the optimized mode number and balancing parameter. A new machine diagnosis approach was developed, named as VMD Differential Evolution Algorithm (VMDEA)-Extreme Learning Machine (ELM). Vibration signal datasets were then reconstructed using VMDEA and the multi-domain features consisted of time-domain, frequency-domain and multi-scale fuzzy entropy were extracted. It was demonstrated that the VMDEA method was able to reduce the computational time about 14% to 53% as compared to VMD-Genetic Algorithm (GA), VMD-Particle Swarm Optimization (PSO) and VMD-Differential Evolution (DE) approaches for bearing, shaft and gear. It also exhibited a better convergence with about two to nine less iterations as compared to VMD-GA, VMD-PSO and VMD-DE for bearing, shaft and gear. The VMDEA-ELM was able to illustrate higher classification accuracy about 11% to 20% than Empirical Mode Decomposition (EMD)-ELM, Ensemble EMD (EEMD)-ELM and Complimentary EEMD (CEEMD)-ELM for bearing shaft and gear. The bearing datasets from Case Western Reserve University were tested with VMDEA-ELM model and compared with Support Vector Machine (SVM)-Dempster-Shafer (DS), EEMD Optimal Mode Multi-scale Fuzzy Entropy Fault Diagnosis (EOMSMFD), Wavelet Packet Transform (WPT)-Local Characteristic-scale Decomposition (LCD)- ELM, and Arctangent S-shaped PSO least square support vector machine (ATSWPLM) models in term of its classification accuracy. The VMDEA-ELM model demonstrates better diagnosis accuracy with small differences between 2% to 4% as compared to EOMSMFD and WPT-LCD-ELM but less diagnosis accuracy in the range of 4% to 5% as compared to SVM-DS and ATSWPLM. The diagnosis approach VMDEA-ELM was also able to provide faster classification performance about 6 40 times faster than Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM). This study provides an improved solution in determining an optimized VMD parameters by using VMDEA. It also demonstrates a more accurate and effective diagnostic approach for machine maintenance using VMDEA-ELM

Bearing fault diagnosis based on adaptive mutiscale fuzzy entropy and support vector machine

This paper proposes a new rolling bearing fault diagnosis method based on adaptive multiscale fuzzy entropy (AMFE) and support vector machine (SVM). Unlike existing multiscale Fuzzy entropy (MFE) algorithms, the scales of AMFE method are adaptively determined by using the robust Hermite-local mean decomposition (HLMD) method. AMFE method can be achieved by calculating the Fuzzy Entropy (FuzzyEn) of residual sums of the product functions (PFs) through consecutive removal of high-frequency components. Subsequently, the obtained fault features are fed into the multi-fault classifier SVM to automatically fulfill the fault patterns recognition. The experimental results show that the proposed method outperforms the traditional MFE method for the nonlinear and non-stationary signal analysis, which can be applied to recognize the different categories of rolling bearings

Novel bearing fault diagnosis model integrated with dual-tree complex wavelet transform, permutation entropy and optimized FCM

In order to enhance the capability of feature extraction and fault classification of bearings, this study proposes a feature extraction approach based on dual-tree complex wavelet transform (DTCWT) and permutation entropy (PE), using the fuzzy c means clustering (FCM) to identify fault types. The vibration signal of bearings can be decomposed into several wavelet components with DTCWT which can describe the local characteristics of vibration signals accurately. And the PE of each wavelet component, which can describe the complexity of a time series, is calculated to be regarded as the fault features. Then forming the standard clustering centers by the FCM, we defined a standard using the Hamming approach degree to evaluate the classification results in the FCM. In order to verify the effectiveness of the proposed approach, compared with two other typical signal analysis methods: ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD), through extracting fault features, it required to identify the fault types and severities under variable operating conditions. The experimental results demonstrate that the proposed approach has a better accuracy and performance to diagnose a bearing fault under different fault severities and variable operating conditions. The proposed approach is suitable for a fault diagnosis due to its good ability to the feature extraction and fault classification

Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems

Over the last few decades, the field of fault diagnostics and structural health management has been experiencing rapid developments. The reliability, availability, and safety of engineering systems can be significantly improved by implementing multifaceted strategies of in situ diagnostics and prognostics. With the development of intelligence algorithms, smart sensors, and advanced data collection and modeling techniques, this challenging research area has been receiving ever-increasing attention in both fundamental research and engineering applications. This has been strongly supported by the extensive applications ranging from aerospace, automotive, transport, manufacturing, and processing industries to defense and infrastructure industries

Noise eliminated ensemble empirical mode decomposition scalogram analysis for rotating machinery fault diagnosis

Rotating machinery is one type of major industrial component that suffers from various faults and damage due to the constant workload to which it is subjected. Therefore, a fast and reliable fault diagnosis method is essential for machine condition monitoring. Artificial intelligence can be applied in fault feature extraction and classification. It is crucial to use an effective feature extraction method to obtain most of the fault information and a robust classifier to classify those features. In this study, an improved method, noise-eliminated ensemble empirical mode decomposition (NEEEMD), was proposed to reduce the white noise in the intrinsic functions and retain the optimum ensembles. A convolution neural network (CNN) classifier was applied for classification because of its feature-learning ability. A generalised CNN architecture was proposed to reduce the model training time. The classifier input used was 64×64 pixel RGB scalogram samples. However, CNN requires a large amount of training data to achieve high accuracy and robustness. Deep convolution generative adversarial network (DCGAN) was applied for data augmentation during the training phase. To evaluate the effectiveness of the proposed feature extraction method, scalograms from the related feature extraction methods such as ensemble empirical mode decomposition (EEMD), complementary EEMD (CEEMD) and continuous wavelet transform (CWT) were also classified. The effectiveness of the scalograms was also validated by comparing the classifier performance using greyscale samples from the raw vibration signals. The ability of CNN was compared with two traditional machine learning algorithms, k nearest neighbour (kNN) and the support vector machine (SVM), using statistical features from EEMD, CEEMD and NEEEMD. The proposed method was validated using bearing and blade datasets. The results show that the machine learning algorithms achieved comparatively lower accuracy than the proposed CNN model. All the outputs from the bearing and blade fault classifiers demonstrated that the scalogram samples from the proposed NEEEMD method obtained the highest accuracy, sensitivity and robustness using CNN. DCGAN was applied with the proposed NEEEMD scalograms to enhance the CNN classifier’s performance further and identify the optimal amount of training data. After training the classifier using the augmented samples, the results showed that the classifier obtained even higher validation and test accuracy with greater robustness. The test accuracies improved from 98%, 96.31% and 92.25% to 99.6%, 98.29% and 93.59%, respectively, for the different classifier models using NEEEMD. The proposed method can be used as a more generalised and robust method for rotating machinery fault diagnosis

Bearing fault diagnosis based on adaptive mutiscale fuzzy entropy and support vector machine

This paper proposes a new rolling bearing fault diagnosis method based on adaptive multiscale fuzzy entropy (AMFE) and support vector machine (SVM). Unlike existing multiscale Fuzzy entropy (MFE) algorithms, the scales of AMFE method are adaptively determined by using the robust Hermite-local mean decomposition (HLMD) method. AMFE method can be achieved by calculating the Fuzzy Entropy (FuzzyEn) of residual sums of the product functions (PFs) through consecutive removal of high-frequency components. Subsequently, the obtained fault features are fed into the multi-fault classifier SVM to automatically fulfill the fault patterns recognition. The experimental results show that the proposed method outperforms the traditional MFE method for the nonlinear and non-stationary signal analysis, which can be applied to recognize the different categories of rolling bearings