Intelligent fault diagnosis system of induction motor based on transient current signal

Widodo, Achmad; Yang, Bo‐Suk; Gu, Dong-Sik; Choi, Byeong-Keun

doi:10.1016/j.mechatronics.2009.02.002

Cited by 68 publications

(24 citation statements)

References 21 publications

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“…Additionally, in order to emphasize the improvement of the proposed framework where FB expansion is used as a pre-processing tool, SVM using one-against-all strategy is also implemented as a classifier to compare the performance with SFAM and the previous studies (Niu et al, 2008;Widodo et al, 2009). The kernel parameter γ and the regularization C in SVM are similarly chosen.…”

Section: Fig 12 Classification Results Of Sfammentioning

confidence: 99%

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An application to transient current signal based induction motor fault diagnosis of Fourier–Bessel expansion and simplified fuzzy ARTMAP

Tran

Althobiani

Ball

et al. 2013

Expert Systems with Applications

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The start-up transient signals have been widely used for fault diagnosis of induction motor because they can reveal early defects in the development process, which are not easily detected with the signals in the steady state operation. However, transient signals are non-linear and contain multi components which need a suitable technique to process and identify the fault pattern. In this paper, the fault diagnosis problem of induction motor is conducted by a data driven framework where the Fourier-Bessel (FB) expansion is used as a tool to decompose transient current signal into series of single components. For each component, the statistical features in the time and the frequency domains are extracted to represent the characteristics of motor condition. The high dimensionality of the feature set is solved by generalized discriminant analysis (GDA) implementation to decrease the computational complexity of classification. In the meantime, with the aid of GDA, the separation of the feature clusters is increased, which enables the more classification accuracy to be achieved. Finally, the reduced dimensional features are used for classifier to perform the fault diagnosis results. The classifier used in this framework is the simplified fuzzy ARTMAP (SFAM) which belongs to a special class of neural networks (NNs) and provides a lower training time in comparison to other traditional NNs. The proposed framework is validated with transient current signals from an induction motor under different conditions including bowed rotor, broken rotor bar, eccentricity, faulty bearing, mass unbalance and phase unbalance. Additionally, this paper provides the comparative performance of (i) SFAM and support vector machine (SVM), (ii) SVM in the framework and SVM combined with wavelet transform in previous studies, (iii) the use of FB decomposition and Hilbert transform decomposition. The results show that the proposed diagnosis framework is capable of significantly improving the classification accuracy.

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Section: Fig 12 Classification Results Of Sfammentioning

confidence: 99%

“…To solve this issue, some previous works (Niu et al, 2008;Widodo et al, 2009) used smoothing in association with subtracting techniques to reduce/remove the line frequency. However, smoothing the signal could also eliminate useful information which reduces classification accuracy.…”

Section: Fb Expansion Based Signal Decompositionmentioning

confidence: 99%

An application to transient current signal based induction motor fault diagnosis of Fourier–Bessel expansion and simplified fuzzy ARTMAP

Tran

Althobiani

Ball

et al. 2013

Expert Systems with Applications

Self Cite

View full text Add to dashboard Cite

show abstract

“…Stator faults include inter-turn short circuit, overheating, insulation faults, and cracks and deformation failure in the core and base, as shown in Table 3 [31,[73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88][89][90][91][92]. The diagnostic methods for phase-to-phase and turn-to-ground short-circuit faults are similar to inter-turn short-circuit fault diagnosis methods.…”

Section: Fault Diagnosis Of Stators Of Pmsgsmentioning

confidence: 99%

“…Signal-based diagnostic methods Numerous operational parameters of DD-WTs are detected, such as voltage, current, power, flux, speed, and vibration. The methods based on signal processing, such as current spectrum analysis, motor current signature analysis, Fourier transform, symmetrical component method, coordinate transform, and wavelet transform, are adopted to diagnose the operating conditions of DD-WTs [77][78][79][80][81][82]. The knowledge-based diagnostic methods for the interturn fault include expert system [83], fuzzy logic [84,85], information fusion [86], pattern recognition [87], and artificial neural network (ANN) [88].…”

Section: Fault Diagnosis Of Stators Of Pmsgsmentioning

confidence: 99%

Overview of condition monitoring and operation control of electric power conversion systems in direct-drive wind turbines under faults

Huang

Xiao

et al. 2017

Front. Mech. Eng.

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Electric power conversion system (EPCS), which consists of a generator and power converter, is one of the most important subsystems in a direct-drive wind turbine (DD-WT). However, this component accounts for the most failures (approximately 60% of the total number) in the entire DD-WT system according to statistical data. To improve the reliability of EPCSs and reduce the operation and maintenance cost of DD-WTs, numerous researchers have studied condition monitoring (CM) and fault diagnostics (FD). Numerous CM and FD techniques, which have respective advantages and disadvantages, have emerged. This paper provides an overview of the CM, FD, and operation control of EPCSs in DD-WTs under faults. After introducing the functional principle and structure of EPCS, this survey discusses the common failures in wind generators and power converters; briefly reviewed CM and FD methods and operation control of these generators and power converters under faults; and discussed the grid voltage faults related to EPCSs in DD-WTs. These theories and their related technical concepts are systematically discussed. Finally, predicted development trends are presented. The paper provides a valuable reference for developing service quality evaluation methods and fault operation control systems to achieve high-performance and high-intelligence DD-WTs.

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“…Fuzzy neural network technology has been successfully applied in many fields [9]. Using fuzzy neural network technology can help identify the complex fault diagnosis mode and assess the severity of fault and predict their occurrence.…”

Section: Introductionmentioning

confidence: 99%

Research of Fault Diagnosis of Belt Conveyor Based on Fuzzy Neural Network

Yuan¹,

Meng²,

Sun³

2014

TOMEJ

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Abstract:To address deficiencies in the process of fault diagnosis of belt conveyor, this study uses a BP neural network algorithm combined with fuzzy theory to provide an intelligent fault diagnosis method for belt conveyor and to establish a BP neural network fault diagnosis model with a predictive function. Matlab is used to simulate the fuzzy BP neural network fault diagnosis of the belt conveyor. Results show that the fuzzy neural network can filter out unnecessary information; save time and space; and improve the fault diagnosis recognition, classification, and fault location capabilities of belt conveyor. The proposed model has high practical value for engineering.

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Intelligent fault diagnosis system of induction motor based on transient current signal

Cited by 68 publications

References 21 publications

An application to transient current signal based induction motor fault diagnosis of Fourier–Bessel expansion and simplified fuzzy ARTMAP

An application to transient current signal based induction motor fault diagnosis of Fourier–Bessel expansion and simplified fuzzy ARTMAP

Overview of condition monitoring and operation control of electric power conversion systems in direct-drive wind turbines under faults

Research of Fault Diagnosis of Belt Conveyor Based on Fuzzy Neural Network

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