Review of recent advances in the application of the wavelet transform to diagnose cracked rotors

Wavelet transform (WT) has been used in the diagnosis of cracked rotors since the 1990s. At present, WT is one of the most commonly used tools to treat signals in several fields. Understandably, this has been an area of extensive scientific research, which is why this paper aims to summarize briefly the major advances in the field since 2008. The present review considers advances in the use and application of WT, the selection of the parameters used, and the key achievements in using WT for crack diagnosis.The authors would like to thank the Spanish government for financing through the CDTI project RANKINE21 IDI-20101560

Effective Crack Detection in Railway Axles Using Vibration Signals and WPT Energy

Crack detection for railway axles is key to avoiding catastrophic accidents. Currently, non-destructive testing is used for that purpose. The present work applies vibration signal analysis to diagnose cracks in real railway axles installed on a real Y21 bogie working on a rig. Vibration signals were obtained from two wheelsets with cracks at the middle section of the axle with depths from 5.7 to 15 mm, at several conditions of load and speed. Vibration signals were processed by means of wavelet packet transform energy. Energies obtained were used to train an artificial neural network, with reliable diagnosis results. The success rate of 5.7 mm defects was 96.27%, and the reliability in detecting larger defects reached almost 100%, with a false alarm ratio lower than 5.5%.The research work described in this paper was supported by the Spanish Government through the MAQ-STATUS DPI2015-69325-C2-1-R project. Authors would also thank the support provided by the participating companies (Renfe, Alstom Spain, SKF Spain, and Danobat Railway Systems) in this project

Railway Axle Condition Monitoring Technique Based on Wavelet Packet Transform Features and Support Vector Machines

Railway axles are critical to the safety of railway vehicles. However, railway axle maintenance is currently based on scheduled preventive maintenance using Nondestructive Testing. The use of condition monitoring techniques would provide information about the status of the axle between periodical inspections, and it would be very valuable in the prevention of catastrophic failures. Nevertheless, in the literature, there are not many studies focusing on this area and there is a lack of experimental data. In this work, a reliable real-time condition-monitoring technique for railway axles is proposed. The technique was validated using vibration measurements obtained at the axle boxes of a full bogie installed on a rig, where four different cracked railway axles were tested. The technique is based on vibration analysis by means of the Wavelet Packet Transform (WPT) energy, combined with a Support Vector Machine (SVM) diagnosis model. In all cases, it was observed that the WPT energy of the vibration signals at the first natural frequency of the axle when the wheelset is first installed (the healthy condition) increases when a crack is artificially created. An SVM diagnosis model based on the WPT energy at this frequency demonstrates good reliability, with a false alarm rate of lower than 10% and defect detection for damage occurring in more than 6.5% of the section in more than 90% of the cases. The minimum number of wheelsets required to build a general model to avoid mounting effects, among others things, is also discussed.This research was funded by the Spanish Government through the project MAQSTATUS with grantnumber DPI2015-69325-C2-1-R

Application of Wavelets-based SVM Classification for Automated Fault Diagnosis and Prognosis of Mechanical Systems

Anwendung der Wavelet-basierte SVM Klassifizierung für die automatisierte Fehlerdiagnose und -prognose mechanischer Systeme In dieser Arbeit werden Techniken der Mustererkennung auf verschiedene Problemstellungen der Fehlerdiagnose und -prognose angewendet. Die untersuchten Anwendungen stellen reale industrielle Anwendungen dar, bei denen verschiedene Messeigenschaften (wie zyklische, impulsive, und periodische Signale), verschiedene Charakteristik der Erkennungsobjektiven (wie kumulativ und einmalige Ereignisse), verschiedene Betriebsbedingungen und -parameter der Maschine, und verschiedene Fehler und Erkennungssystemanforderungen (wie Verschleiß, Riss, und Objekterkennung; Systemzustand und Restlebensdauer) die modulare Mustererkennungsverfahren und -techniken erfordern. Verschiedene Ansätze werden untersucht und angewendet, wie Support Vector Machine (SVM), Continuous Wavelet-Transform (CWT),Wavelet Packet Transform (WPT) und Diskrete Wavelet-Transform (DWT), und viele Konzepte und Lösungen werden vorgeschlagen und überprüft, um ein zuverlässiges Zustandsüberwachungssystem zu erreichen, dass die Instandhaltungsplanung der Maschine unterstützt und die Produktionsqualität und Produktionskosten verbessert. In der ersten untersuchten Anwendung in dieser Arbeit wird ein Ansatz für die Entwicklung eines Fehlerdiagnose- und -prognosesystems vorgestellt. Das System wird als Vorwarnmodul verwendet, um die Notwendigkeit für das Ersetzen von Verschleißteilen von Produktionsmaschinen zu erkennen und die Restlebensdauer des überwachten Teils zu bewerten. In der zweiten untersuchten Anwendung wird ein Produktionsverfahren überwacht. Ziel ist die Erkennung eines Objektes mit einer möglichst geringen Fehlalarmrate. Die Signale beinhalten nichtstationäre, impulsartige bzw. einmalige Ereignisse. Ein weiteres Merkmal der Sensorcluster-Signale ist die nicht gleichzeitige Erzeugung von Ereignissen, die die Verwendung von geeigneten Entscheidungsfusionstechniken erfordert. In der letzten untersuchten Anwendung, werden modell- und signalbasierte Verfahren für die Risserkennung und Prognose in rotierenden Maschinen untersucht, um eine Vorwarnung für Rotor-Risse zu erreichen für Online- Überwachung in Turbomaschinen. Die angetroffenen Signale sind periodische Schwingungssignale mit kumulativen Auswirkungen der Fehlerereignisse. Offene Fragen stellen sich bei den Themen Zustandsbewertung, Fehlerschweregrad und Restlebensdauer, basierend auf spezifischen Sensordaten mit besonderen anwendungsorientierten Eigenschaften. Diese Arbeit befasst sich mit diesen offenen Fragen, um ein zuverlässiges Zustandsüberwachungssystem zu erreichen. Es kann festgestellt werden, dass Wavelets und SVM sehr nützliche Werkzeuge für die Merkmalsextraktion und Klassifikation im Bereich der Zustandsüberwachung sind. Der Merkmalsraum von SVM ist nützlich für die Bewertung der verbleibenden Lebensdauer. Allerdings zeigt sich ebenfalls, dass angesichts der Herausforderungen anwendungsorientierte Lösungen gefunden werden müssen.In this thesis, the application of pattern recognition techniques is considered for different kinds of fault diagnosis and prognosis problems and applications. The investigated applications represent real industrial applications, in which different measurement characteristics (such as cyclic, impulsive, and periodic signals), different recognition objective characteristics (such as accumulative and one-time events), different operational conditions and parameters of the machine, and different faults and detection system requirements (such as wear, crack, and object detection; System state and remaining life time) are challenging the existence of modular pattern recognition procedures and techniques. Different approaches are investigated and applied such as Support Vector Machine (SVM), Discrete Wavelet Transform (DWT), Wavelet Packet Transform (WPT), and Continuous Wavelet Transform (CWT), and many concepts and solutions are proposed and verified, in order to achieve a reliable condition monitoring system, which supports the maintenance planning of the machine and adds value to the production quality and cost. In the first investigated application in this thesis, an approach for developing a fault diagnosis and prognosis system is presented. The system is used as a prewarning module to detect the necessity for replacing wear parts of production machines and to evaluate the remaining life time of the supervised part. The sensor signals encountered for processing are nondeterministic with cyclic nature related to the operation cycle of the machine. In the second investigated application, the goal is to monitor a production process for online detection of a target object with the lowest possible false alarm rate. The signals encountered in the system of this work are characterized with nonstationary impulsive one-time events representing the goal object. Another characteristic of the sensor cluster signals is the partly simultaneous stimulation of events which requires the use of suitable decision fusion techniques. In the last investigated application, two main approaches used for crack detection and prediction in rotating machinery; model based and signal based, are investigated, in order to achieve a prewarning technique for rotor cracks to be applied for online monitoring in turbo-machinery. The signals encountered are periodic vibration signals with accumulative impact of the fault incident. Open questions arise in the issues of state evaluation, severity estimation, and remaining life time prediction, based on specific sensor data with particular applicationoriented characteristics. This work deals with these open questions, in order to achieve a reliable condition monitoring system. As a general conclusion of the work, it can be stated that Wavelets and SVM are reliable tools for feature extraction and classification in the field of condition monitoring, and the feature space of SVM is useful for remaining life prediction. However; specific application oriented Solutions and tricks are necessary, considering the diversity of fault diagnosis and prognosis problems and difficulties

Trends in Fault Diagnosis for Electrical Machines

[EN] The fault diagnosis of rotating electrical machines has received an intense amount of research interest during the last 30 years. Reducing maintenance costs and preventing unscheduled downtimes, which result in losses of production and financial incomes, are the priorities of electrical drives manufacturers and operators. In fact, both correct diagnosis and early detection of incipient faults lead to fast unscheduled maintenance and short downtime for the process under consideration. They also prevent the harmful and sometimes devastating consequences of faults and failures. This topic has become far more attractive and critical as the population of electric machines has greatly increased in recent years. The total number of operating electrical machines in the world was around 16.1 billion in 2011, with a growth rate of about 50% in the last five years [1].Henao, H.; Capolino, G.; Fernández-Cabanas, M.; Filippetti, F.; Bruzzese, C.; Strangas, E.; Pusca, R.... (2014). Trends in Fault Diagnosis for Electrical Machines. IEEE Industrial Electronics Magazine. 8(2):31-42. doi:10.1109/MIE.2013.2287651S31428

Laser Ultrasound Inspection Based on Wavelet Transform and Data Clustering for Defect Estimation in Metallic Samples

Laser-generated ultrasound is a modern non-destructive testing technique. It has been investigated over recent years as an alternative to classical ultrasonic methods, mainly in industrial maintenance and quality control procedures. In this study, the detection and reconstruction of internal defects in a metallic sample is performed by means of a time-frequency analysis of ultrasonic waves generated by a laser-induced thermal mechanism. In the proposed methodology, we used wavelet transform due to its multi-resolution time frequency characteristics. In order to isolate and estimate the corresponding time of flight of eventual ultrasonic echoes related to internal defects, a density-based spatial clustering was applied to the resulting time frequency maps. Using the laser scan beam’s position, the ultrasonic transducer’s location and the echoes’ arrival times were determined, the estimation of the defect’s position was carried out afterwards. Finally, clustering algorithms were applied to the resulting geometric solutions from the set of the laser scan points which was proposed to obtain a two-dimensional projection of the defect outline over the scan plane. The study demonstrates that the proposed method of wavelet transform ultrasonic imaging can be effectively applied to detect and size internal defects without any reference information, which represents a valuable outcome for various applications in the industry. View Full-TextPeer ReviewedPostprint (published version

Vibration diagnosis of elastic shafts with a transverse crack

Detection of the shaft crack in a rotating machine is one of the most challenging problems in equipment predictive maintenance. In the available literature, various crack detection methods have been applied to study the dynamic behaviour of a cracked shaft. This study sought to attempt a vibration-based method. Elastic shafts with three different types of transverse cracks, including experimentally-induced fatigue crack, welded shaft crack, and wire-cut crack, were fabricated, and used to analyse the bending stiffness and frequency response in the vertical direction. The results from the cracked shafts were compared with that of an intact shaft. Bending stiffness of different shafts was measured as a function of rotation angle of the shafts. Among the three different crack types, the bending stiffness of the fatigue crack shaft showed a typical breathing behaviour, which was consistent with the previous theoretical results. The welded shaft crack also demonstrated opening and closing characteristics, but the stiffness was found to be much lower compared with that of a fatigue cracked shaft. As for the wire-cut crack, no breathing mechanism was observed for any rotational angle, due to the big width of the gap. Therefore, it is concluded that the fatigue induced crack is the most accurate method to evaluate the vibration characteristics of cracked shafts. Our results also indicated that existing switching model and harmonic models cannot describe the periodic stiffness of a transverse shaft crack accurately. Modal analysis was carried out on the intact shaft, as well as the three types of cracked shafts. Frequency responses in the X-axis direction were obtained. The correlation between the bending stiffness and the resonant frequency was examined, and it was experimentally proved that the decrease in resonant frequency was almost proportional to the reduction in the stiffness. Also, the amplitude of vibration response was found to be amplified by the crack element. The cause and implications of these results were analysed, and they are expected to deepen our understanding of crack diagnosis using vibration method

Railway Axle Early Fatigue Crack Detection through Condition Monitoring Techniques

: The detection of cracks in rotating machinery is an unresolved issue today. In this work, a methodology for condition monitoring of railway axles is presented, based on crack detection by means of the automatic selection of patterns from the vibration signal measurement. The time waveforms were processed using the Wavelet Packet Transform, and appropriate alarm values for diagnosis were calculated automatically using non-supervised learning techniques based on Change Point Analysis algorithms. The validation was performed using vibration signals obtained during fatigue tests of two identical railway axle specimens, one of which cracked during the test while the other did not. During the test in which the axle cracked, the results show trend changes in the energy of the vibration signal associated with theoretical defect frequencies, which were particularly evident in the direction of vibration that was parallel to the track. These results are contrasted with those obtained during the test in which the fatigue limit was not exceeded, and the test therefore ended with the axle intact, verifying that the effects that were related to the crack did not appear in this case. With the results obtained, an adjusted alarm value for a condition monitoring process was established

Blade faults diagnosis in multi stage rotor system by means of wavelet analysis

Blade fault is one of the most causes of gas turbine failures. Vibration spectral analysis and blade pass frequency (BPF) monitoring are the most widely used methods for blade fault diagnosis. These methods however have limitations in the detection of incipient faults due to weak and/or transient signals, as well as inability to diagnose the blade faults types. This study investigates the applications of wavelet analysis in blade fault diagnosis of a multi stage rotor system, as an extension of previous works which involved a single stage only. Results showed that conventional wavelet analysis has limitations in segregating the BPFs and locating the faults. An improvement in Morlet wavelet was made to achieve high resolution in both time and frequency domains. Two new wavelets for high time-frequency resolutions were formulated and added to the standard MATLAB Wavelet Toolbox. The optimal parameters for the high frequency resolution wavelet were found at the centre of frequency, ????=4 and bandwidth, ??=0.5. For high time resolution wavelet, the optimal parameters were ????=4 and ??=10. A novel algorithm was formulated by combining the two newly developed wavelets. A variety of blade faults including blade creep rubbing, blade tip rubbing, stage rubbing, blade loss of part and blade twisting were tested and their vibration responses measured in a laboratory test facility. The proposed method showed potential in segregating closely spaced BPFs components and identifying the faulty stage and fault location. The method demonstrated the ability in differentiating various blade faults based on a unique pattern (“fingerprint”) of each fault produced by the newly added wavelet. The formulated algorithm was demonstrated to be suitable in monitoring rotor systems with multiple blade stages

Wavelet Transform Applied to Internal Defect Detection by Means of Laser Ultrasound

Laser-generated ultrasound represents an interesting nondestructive testing technique that is being investigated in the last years as performative alternative to classical ultrasonic-based approaches. The greatest difficulty in analyzing the acoustic emission response is that an in-depth knowledge of how acoustic waves propagate through the tested composite is required. In this regard, different signal processing approaches are being applied in order to assess the significance of features extracted from the resulting analysis. In this study, the detection capabilities of internal defects in a metallic sample are proposed to be studied by means of the time-frequency analysis of the ultrasonic waves resulting from laser-induced thermal mechanism. In the proposed study, the use of the wavelet transform considering different wavelet variants is considered due to its multi-resolution time-frequency characteristics. Also, a significant time-frequency technique widely applied in other fields of research is applied, the synchrosqueezed transform