Bo-Suk Yang

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

Abstract 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.

Application of nonlinear feature extraction and support vector machines for fault 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.

ART-KOHONEN neural network for fault diagnosis of rotating machinery

Abstract In this paper, a new neural network (NN) for fault diagnosis of rotating machinery which synthesises the theory of adaptive resonance theory (ART) and the learning strategy of Kohonen neural network (KNN), is proposed. For NNs, as the new case occurs, the corresponding data should be added to their dataset for learning. However, the ‘off-line’ NNs are unable to adapt autonomously and must be retrained by applying the complete dataset including the new data. The ART networks can solve the plasticity–stability dilemma. In other words, they are able to carry out ‘on-line’ training without forgetting previously trained patterns (stable training); it can recode previously trained categories adaptive to changes in the environment and is self-organising. ART–KNN also holds these characteristics, and more suitable than original ART for fault diagnosis of machinery. In order to test the proposed network, the vibration signal is selected as raw inputs due to its simplicity, accuracy and efficiency. The results of the experiments confirm the performance of the proposed network through comparing with other NNs, such as the self-organising feature maps (SOFMs), learning vector quantisation (LVQ) and radial basis function (RBF) NNs under the same conditions.The diagnosis success rate for the ART–Kohonen network was 100%, while the rates of SOFM, LVQ and RBF networks were 93%, 93% and 89%, respectively.

Intelligent prognostics for battery health monitoring based on sample entropy

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.

Development of an optimized condition-based maintenance system by data fusion and reliability-centered maintenance

Maintenance has gained in importance as a support function for ensuring equipment availability, quality products, on-time deliveries, and plant safety. Cost-effectiveness and accuracy are two basic criteria for good maintenance. Reducing maintenance cost can increase enterprise profit, while accurate maintenance action can sustain continuous and reliable operation of equipment. As instrumentation and information systems become cheaper and more reliable, condition-based maintenance becomes an important tool for running a plant or a factory. This paper presents a novel condition-based maintenance system that uses reliability-centered maintenance mechanism to optimize maintenance cost, and employs data fusion strategy for improving condition monitoring, health assessment, and prognostics. The proposed system is demonstrated by way of reasoning and case studies. The results show that optimized maintenance performance can be obtained with good generality.

Development of an e-maintenance system integrating advanced techniques

In recent years, globalization and fast growth of communication technologies, computer and information technologies have changed the pattern of maintenance. Accordingly, a new maintenance, e-maintenance has emerged and has been gradually replacing the traditional maintenance. In this paper, a new e-maintenance system is proposed that is dependent upon coordination, co-operation and negotiation through the use of Internet and tether-free (i.e. wireless, web, etc.) communication technologies. This e-maintenance enables manufacturing operations to achieve near-zero-downtime performance on a sharable, quick and convenient platform through integrating the existent advanced technologies with distributed sources. The main difference between the proposed e-maintenance and existing systems is the system structure. This e-maintenance consists of two subsystems: maintenance centre and local maintenance. The relationship of both subsystems can be considered as supplier and clients. This division can effectively reduce maintenance cost, maintenance system design period, and solve the problem of lack of experts. Therefore, the competition of company can be increased due to high reliability of e-maintenance. Some existing modules of this e-maintenance demonstrate its feasibility.

Integration of ART-Kohonen neural network and case-based reasoning for intelligent fault diagnosis

This paper presents a new approach for integrating case-based reasoning (CBR) with an ART-Kohonen neural network (ART-KNN) to enhance fault diagnosis. When solving a new problem, the neural network is used to make hypotheses and to guide the CBR module in the search for a similar previous case that supports one of the hypotheses. The knowledge acquired by the network is interpreted and mapped into symbolic diagnosis descriptors, which are kept and used by the system to determine whether a final answer is credible, and to build explanations for the reasoning carried out. ART-KNN, synthesizing the theory of adaptive resonance theory and the learning strategy of Kohonen neural network, can solve the plasticity-stability dilemma of conventional neural networks. It can carry out ‘on-line’ training without forgetting previously trained patterns (stable training), and recode previously trained categories adaptive to changes in the environment and is self-organizing, which differs from most of networks that only can be carried out off-line. The proposed system has been used in the faults diagnosis of electric motor to verify the system performance. The result shows the proposed system performs better than self-organizing feature map (SOFM) based system with respect to classification rate. q 2003 Elsevier Ltd. All rights reserved.

VIBEX: an expert system for vibration fault diagnosis of rotating machinery using decision tree and decision table

This paper proposes an expert system called VIBEX (VIBration EXpert) to aid plant operators in diagnosing the cause of abnormal vibration for rotating machinery. In order to automatize the diagnosis, a decision table based on the cause-symptom matrix is used as a probabilistic method for diagnosing abnormal vibration. Also a decision tree is used as the acquisition of structured knowledge in the form of concepts is introduced to build a knowledge base which is indispensable for vibration expert systems. The decision tree is a technique used for building knowledge-based systems by the inductive inference from examples and plays a role itself as a vibration diagnostic tool. The proposed system has been successfully implemented on Microsoft Windows environment and is written in Microsoft Visual Basic and Visual C++. To validate the system performance, the diagnostic system was tested with some examples using the two diagnostic methods.

Wavelet support vector machine for induction machine fault diagnosis based on transient current signal

This paper presents establishing intelligent system for faults detection and classification of induction motor using wavelet support vector machine (W-SVM). Support vector machines (SVM) is well known as intelligent classifier with strong generalization ability. Application of nonlinear SVM using kernel function is widely used for multi-class classification procedure. In this paper, building kernel function using wavelet will be introduced and applied for SVM multi-class classifier. Moreover, the feature vectors for training classification routine are obtained from transient current signal that preprocessed by discrete wavelet transform. In this work, principal component analysis (PCA) and kernel PCA are performed to reduce the dimension of features and to extract the useful features for classification process. Hence, a relatively new intelligent faults detection and classification method called W-SVM is established. This method is used to induction motor for faults classification based on transient current signal. The results show that the performance of classification has high accuracy based on experimental work.

Case-based reasoning system with Petri nets for induction motor fault diagnosis

Abstract This paper presents an innovative approach for integrating case-based reasoning (CBR) with Petri net for the fault diagnosis of induction motors. In the CBR system, maintenance engineers can retrieve the information from previous cases which closely resemble the new problem and solve the new problem using the information from the previous cases. The proposed system has been used in fault diagnosis of electric motor to confirm the system performance. The result shows the proposed system performs better than the conventional CBR system.