A review on machinery diagnostics and prognostics implementing condition-based maintenance

With the continuous increase in complexity and expense of industrial systems, there is less tolerance for performance degradation, productivity decrease, and safety hazards, which greatly necessitates to detect and identify any kinds of potential abnormalities and faults as early as possible and implement real-time fault-tolerant operation for minimizing performance degradation and avoiding dangerous situations. During the last four decades, fruitful results have been reported about fault diagnosis and fault-tolerant control methods and their applications in a variety of engineering systems. The three-part survey paper aims to give a comprehensive review of real-time fault diagnosis and fault-tolerant control, with particular attention on the results reported in the last decade. In this paper, fault diagnosis approaches and their applications are comprehensively reviewed from model- and signal-based perspectives, respectively.

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A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches

Deep learning and its applications to machine health monitoring

Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

Artificial intelligence for fault diagnosis of rotating machinery: A review

Support vector machine in machine condition monitoring and fault diagnosis

In this paper, an intelligent prognostic for battery health based on sample entropy (SampEn) feature of discharge voltage is proposed. SampEn can provide computational means for assessing the predictability of a time series and also can quantity the regularity of a data sequence. Therefore, when it is applied to discharge voltage battery data, it could serve an indicator for battery health. In this work, the intelligent ability is introduced by utilizing machine learning methods namely support vector machine (SVM) and relevance vector machine (RVM). SampEn and estimated state of charge (SOH) are employed as data input and target vector of learning algorithms, respectively. The results show that the proposed method is plausible due to the good performance of SVM and RVM in SOH prediction. In our study, RVM outperforms SVM based battery health prognostics.

Intelligent prognostics for battery health monitoring based on sample entropy

This paper studies the application of independent component analysis (ICA) and support vector machines (SVMs) to detect and diagnose of induction motor faults. The ICA is used for feature extraction and data reduction from original features. The principal components analysis is also applied in feature extraction process for comparison with ICA does. In this paper, the training of the SVMs is carried out using the sequential minimal optimization algorithm and the strategy of multi-class SVMs-based classification is applied to perform the faults identification. Also, the performance of classification process due to the choice of kernel function is presented to show the excellent of characteristic of kernel function. Various scenarios are examined using data sets of vibration and stator current signals from experiments, and the results are compared to get the best performance of classification process.

Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors

Recently, principal components analysis (PCA) and independent components analysis (ICA) was introduced for doing feature extraction. PCA and ICA linearly transform the original input into new uncorrelated and independent features space respectively. In this paper, the feasibility of using nonlinear feature extraction is studied and it is applied in support vector machines (SVMs) to classify the faults of induction motor. In nonlinear feature extraction, we employed the PCA and ICA procedure and adopted the kernel trick to nonlinearly map the data into a feature space. A strategy of multi-class SVM-based classification is applied to perform the faults diagnosis. The performance of classification process due to various feature extraction method and the choice of kernel function is presented and compared to show the excellent of classification process.

Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors

This study concerns with fault diagnosis of low speed bearing using multi-class relevance vector machine (RVM) and support vector machine (SVM). A low speed test rig was developed to simulate various types of bearing defects associated with shaft speeds as low as 10rpm under several loading conditions. The data was acquired from the low speed bearing test rig using acoustic emission (AE) and accelerometer sensors under a constant load with different speeds. The aim of this study is to address the problem of detecting an incipient bearing fault and to find reliable methods for low speed machine fault diagnosis. In this paper, two methods of multi-class classification techniques for fault diagnosis through RVM and SVM are presented and the effectiveness of using AE and vibration signals due to low impact rate for low speed diagnosis. In the present study, component analysis was performed initially to extract the features and to reduce the dimensionality of original data features. The classification for fault diagnosis was also conducted using original data feature and without feature extraction. The result shows that multi-class RVM produces promising results and has the potential for use in fault diagnosis of low speed machine.

Bo-Suk Yang

Papers

Support vector machine in machine condition monitoring and fault diagnosis

Intelligent prognostics for battery health monitoring based on sample entropy

Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors

Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors

Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine