Multi-Fault Rapid Diagnosis for Wind Turbine Gearbox Using Sparse Bayesian Extreme Learning Machine

© 2013 IEEE. In order to reduce operation and maintenance costs, reliability, and quick response capability of multi-fault intelligent diagnosis for the wind turbine system are becoming more important. This paper proposes a rapid data-driven fault diagnostic method, which integrates data pre-processing and machine learning techniques. In terms of data pre-processing, fault features are extracted by using the proposed modified Hilbert-Huang transforms (HHT) and correlation techniques. Then, time domain analysis is conducted to make the feature more concise. A dimension vector will then be constructed by including the intrinsic mode function energy, time domain statistical features, and the maximum value of the HHT marginal spectrum. On the other hand, as the architecture and the learning algorithm of pairwise-coupled sparse Bayesian extreme learning machine (PC-SBELM) are more concise and effective, it could identify the single- and simultaneous-fault more quickly and precisely when compared with traditional identification techniques such as pairwise-coupled probabilistic neural networks (PC-PNN) and pairwise-coupled relevance vector machine (PC-RVM). In this case study, PC-SBELM is applied to build a real-time multi-fault diagnostic system. To verify the effectiveness of the proposed fault diagnostic framework, it is carried out on a real wind turbine gearbox system. The evaluation results show that the proposed framework can detect multi-fault in wind turbine gearbox much faster and more accurately than traditional identification techniques

EEMD-Based cICA method for single-channel signal separation and fault feature extraction of gearbox

This paper proposes a novel fault feature extraction method with the aim of extracting the fault feature submerged in the single-channel observation signal. The proposed method integrates the strengths of the constrained independent component analysis (cICA) extracting only the signals of interest (SOIs) with the advantage of ensemble empirical mode decomposition (EEMD) alleviating the mode mixing. The method, which is named EEMD-based cICA, not only enables gear fault feature extraction but also offers a new independent component analysis (ICA) mixing model with source noise and measured noise for the single-channel observation signal. The efficiency of the proposed method is tested on simulated as well as real-world vibration signals acquired from a multi-stage gearbox with a missing tooth and a chipped tooth, respectively

Development of new fault detection methods for rotating machines (roller bearings)

Abstract Early fault diagnosis of roller bearings is extremely important for rotating machines, especially for high speed, automatic and precise machines. Many research efforts have been focused on fault diagnosis and detection of roller bearings, since they constitute one the most important elements of rotating machinery. In this study a combination method is proposed for early damage detection of roller bearing. Wavelet packet transform (WPT) is applied to the collected data for denoising and the resulting clean data are break-down into some elementary components called Intrinsic mode functions (IMFs) using Ensemble empirical mode decomposition (EEMD) method. The normalized energy of three first IMFs are used as input for Support vector machine (SVM) to recognize whether signals are sorting out from healthy or faulty bearings. Then, since there is no robust guide to determine amplitude of added noise in EEMD technique, a new Performance improved EEMD (PIEEMD) is proposed to determine the appropriate value of added noise. A novel feature extraction method is also proposed for detecting small size defect using Teager-Kaiser energy operator (TKEO). TKEO is applied to IMFs obtained to create new feature vectors as input data for one-class SVM. The results of applying the method to acceleration signals collected from an experimental bearing test rig demonstrated that the method can be successfully used for early damage detection of roller bearings. Most of the diagnostic methods that have been developed up to now can be applied for the case stationary working conditions only (constant speed and load). However, bearings often work at time-varying conditions such as wind turbine supporting bearings, mining excavator bearings, vehicles, robots and all processes with run-up and run-down transients. Damage identification for bearings working under non-stationary operating conditions, especially for early/small defects, requires the use of appropriate techniques, which are generally different from those used for the case of stationary conditions, in order to extract fault-sensitive features which are at the same time insensitive to operational condition variations. Some methods have been proposed for damage detection of bearings working under time-varying speed conditions. However, their application might increase the instrumentation cost because of providing a phase reference signal. Furthermore, some methods such as order tracking methods still can be applied when the speed variation is limited. In this study, a novel combined method based on cointegration is proposed for the development of fault features which are sensitive to the presence of defects while in the same time they are insensitive to changes in the operational conditions. It does not require any additional measurements and can identify defects even for considerable speed variations. The signals acquired during run-up condition are decomposed into IMFs using the performance improved EEMD method. Then, the cointegration method is applied to the intrinsic mode functions to extract stationary residuals. The feature vectors are created by applying the Teager-Kaiser energy operator to the obtained stationary residuals. Finally, the feature vectors of the healthy bearing signals are utilized to construct a separating hyperplane using one-class support vector machine. Eventually the proposed method was applied to vibration signals measured on an experimental bearing test rig. The results verified that the method can successfully distinguish between healthy and faulty bearings even if the shaft speed changes dramatically

Information Theory and Its Application in Machine Condition Monitoring

Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries

A Literature Review of Fault Diagnosis Based on Ensemble Learning

The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance

A time-frequency analysis approach for condition monitoring of a wind turbine gearbox under varying load conditions

This paper deals with the condition monitoring of wind turbine gearboxes under varying operating conditions. Generally, gearbox systems include nonlinearities so a simplified nonlinear gear model is developed, on which the time–frequency analysis method proposed is first applied for the easiest understanding of the challenges faced. The effect of varying loads is examined in the simulations and later on in real wind turbine gearbox experimental data. The Empirical Mode Decomposition (EMD) method is used to decompose the vibration signals into meaningful signal components associated with specific frequency bands of the signal. The mode mixing problem of the EMD is examined in the simulation part and the results in that part of the paper suggest that further research might be of interest in condition monitoring terms. For the amplitude–frequency demodulation of the signal components produced, the Hilbert Transform (HT) is used as a standard method. In addition, the Teager–Kaiser energy operator (TKEO), combined with an energy separation algorithm, is a recent alternative method, the performance of which is tested in the paper too. The results show that the TKEO approach is a promising alternative to the HT, since it can improve the estimation of the instantaneous spectral characteristics of the vibration data under certain conditions

Circuit Breaker Fault Diagnosis Method Based on Improved One-Dimensional Convolutional Neural Network

Aiming at the problems of manual feature extraction and poor generalization ability of model in traditional circuit breaker fault diagnosis technology, a circuit breaker fault diagnosis method based on improved one-dimensional convolutional neural network is proposed. Firstly, the input feature sequence is adaptively weighted by self-attention mechanism to highlight the weight of important information; Secondly, 1 1 convolution layer and global average pooling layer are used to replace the full connection layer, which reduces the model training parameters, improves the training efficiency and prevents the phenomenon of over-fitting. Aiming at the problem of small number of data samples, the data is enhanced by Generative Adversarial Network. After adding the generated data to the original data, the accuracy of fault identification is further improved. The experimental results show that this method can effectively and accurately identify different fault types of circuit breaker, and verify the feasibility of its engineering application

An Investigation into Vibration Based Techniques for Wind Turbine Blades Condition Monitoring

The rapid expansion of wind power has been accompanied by reported reliability problems and the aim is to provide a means of increasing wind turbine reliability, prevent break downs, increase availability and reduce maintenance costs and power outages. This research work reports the development of condition monitoring (CM) for early fault detection in wind turbine blades based on vibration measurements. The research started with a background and a survey of methods used for monitoring wind turbines. Then, finite element modelling (FEM) of three bladed horizontal axis wind turbine (HAWT) was developed to understand the nature and mechanism of the induced vibration. A HAWT test rig was constructed and equipped with computerised vibration measuring system for model verification. Statistical and spectral processing parameters then were used to analyse vibration signals that collected in healthy and faulty cases. Results obtained using time and frequency based techniques are not suitable for extracting blades condition related information. Consequently, empirical mode decomposition method (EMD), principal component analysis method (PCA) and continuous wavelet transform (CWT) are applied for extraction blade condition related features from the measured vibration. The result showed that although these methods generally proved their success in other fields, they have failed to detect small faults or changes in blade structure. Therefore, new techniques were developed using the above mentioned methods combined with feature intensity level (FIL) and crest factor. Namely, those are EDFIL, RMPCA and wavelet based FIL. The new techniques are found to be reliable, robust and sensitive to the severity of faults. Those analysis techniques are suitable to be the detection tool for an integrated wind turbine condition monitoring system. Directions for future work are also given at the end of the thesis

A review of aircraft auxiliary power unit faults, diagnostics and acoustic measurem

The Auxiliary Power Unit (APU) is an integral part of an aircraft, providing electrical and pneumatic power to various on-board sub-systems. APU failure results in delay or cancellation of a flight, accompanied by the imposition of hefty fines from the regional authorities. Such inadvertent situations can be avoided by continuously monitoring the health of the system and reporting any incipient fault to the MRO (Maintenance Repair and Overhaul) organization. Generally, enablers for such health monitoring techniques are embedded during a product's design. However, a situation may arise where only the critical components are regularly monitored, and their status presented to the operator. In such cases, efforts can be made during service to incorporate additional health monitoring features using the already installed sensing mechanisms supplemented by maintenance data or by instrumenting the system with appropriate sensors. Due to the inherently critical nature of aircraft systems, it is necessary that instrumentation does not interfere with a system's performance and does not pose any safety concerns. One such method is to install non-intrusive vibroacoustic sensors such that the system integrity is maintained while maximizing system fault diagnostic knowledge. To start such an approach, an in-depth literature survey is necessary as this has not been previously reported in a consolidated manner. Therefore, this paper concentrates on auxiliary power units, their failure modes, maintenance strategies, fault diagnostic methodologies, and their acoustic signature. The recent trend in APU design and requirements, and the need for innovative fault diagnostics techniques and acoustic measurements for future aircraft, have also been summarized. Finally, the paper will highlight the shortcomings found during the survey, the challenges, and prospects, of utilizing sound as a source of diagnostics for aircraft auxiliary power units