Fault Diagnosis of Induction Motor using Neural Networks

He, Qiang; Du, Dongping

doi:10.1109/icmlc.2007.4370306

Cited by 16 publications

(9 citation statements)

References 19 publications

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“…To further demonstrate the effectiveness of the proposed motor fault diagnosis based on CMAC, we compared the diagnostic results by this method and by other neural network systems [7] in Table 7. In addition, other than a comparison with the multilayer perceptron (MLP) method on the fault diagnosis performance, a comparison with the fuzzy neural network (FNN) [1] and the extension theory (ET) [3] is added to highlight the superiority of the proposed fault diagnosis method.…”

Section: Diagnostic Resultsmentioning

confidence: 99%

“…Presently, researchers have put a lot of effort in quest for such techniques [4]- [7]; for instance, an attempt to employ algorithms of artificial intelligence [4] in motor fault diagnoses, which method can enhance the efficiency of motor fault diagnoses, helping save effort and time. Other researchers applied methods of neural network (NN) [5]- [7] to create fault diagnosis system, which uses the weighting value connecting neurons to do algorithms to, in turn, determine the types of faults. That is a method capable of making faster diagnoses though, it requires a lot of data to learn with [8]- [12].…”

Section: Introductionmentioning

confidence: 99%

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A Motor Fault Diagnosis System Based on Cerebellar Model Articulation Controller

2019

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The purpose of this paper is to construct a smart induction motor fault diagnosis system with cerebellar model articulation controller (CMAC). First, we divide induction motor faults in three kinds, rotor mandrel fault, bearing fault and electrical fault. Then, we subdivide them into ten types, in each of which the vibration signal spectra of induction motors were measured and sorted for establishing the individual fault types. From the information on motor faults, we identify representative characteristic frequency spectra for the faults to further establish the correlations between each of the fault types and its corresponding characteristic frequency spectrum as the basis for the development of a motor fault diagnosis system. In this paper, the theoretic basis is CMAC, and the data of vibration signal spectrum measured against the motor faults are used to train the fault diagnosis system. We then conducted fault diagnoses with the data of actual motor run. The test results demonstrated that the proposed induction motor fault diagnosis system is capable of fast algorithm, requires less data to train with, as well as has excellent power of identification.INDEX TERMS Fault vibration signal, cerebellar model articulation controller (CMAC), characteristic frequency spectrum, motor fault diagnosis.

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Section: Diagnostic Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Motor Fault Diagnosis System Based on Cerebellar Model Articulation Controller

2019

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“…Hence, a new technology for diagnosing system faults deserves study. In this regard, there has been much effort made in both Taiwan and overseas [12][13][14][15]. The methods that have resulted from these efforts combine more than two algorithms to form an artificial intelligence (AI) system for motor fault diagnosis, which can increase the efficiency in fault diagnosis for motor drive systems and thus helps save labor and time costs.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis and Tolerant Control of Three-Level Neutral-Point Clamped Inverters in Motor Drives

Chao

2020

Energies

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This paper presents an extension theory-based assessment method to perform fault diagnosis for inverters in motor driving systems. First, a three-level neutral-point clamped (NPC) inverter is created using the PSIM software package to simulate faults for any power transistor in the NPC-type inverter. Fast Fourier transformation is used to transform the line current signals in the time domain into a spectrum in the frequency domain for analysis of the corresponding spectrum of features of the inverter for faults with different power transistors. Then, the relationships between fault types and specific spectra are established as characteristics for the extension assessment method, which is then used to create a smart fault diagnosis system for inverters. Fault-tolerant control (FTC) is used here when the rated output of a faulty inverter is decreased in order to maintain balanced output in three phases by changing the framework of the transistor connection. This is performed to reinforce the reliability of the inverter. Finally, by the simulation and experimental results, the feasibility of the proposed smart fault diagnosis system is confirmed. The proposed fault diagnosis method is advantageous due to its minimal use of data and lack of a learning process, which thereby reduces the fault diagnosis time and makes the method easily used in practice. The proposed fault-tolerant control strategy allows both online and smooth switching in the wiring structure of the inverter.

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“…The most frequently used NN in the diagnostic systems is the feedforward Multilayer Perceptron (MLP) type neural network. Despite the many advantages of MLP networks such as ease of description and hardware implementation, they require proper selection of structure [29,32,33], learning methods [6,7], activation function [7] and network input vector [8]. When teaching MLP networks, the most common algorithms are Levenberg-Marquardt (LM) algorithm [7,8,29] and the Back-Propagation (BP) algorithm [7,32,34].…”

Section: Introductionmentioning

confidence: 99%

“…The use of Genetic Algorithms (GAs) in the process of selecting the optimal structure and the number of neurons in the hidden layers of MLP network is presented in [33]. In [32] the authors used an analytical approach to determine the number of neurons of the hidden layers of the fault detector, based on the size of the input and output vectors of the layer. The effectiveness of the MLP network is also significantly affected by the activation function used [7].…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of Selected Neural Network Structures Based on Axial Flux Analysis in Stator and Rotor Winding Incipient Fault Detection of Inverter-fed Induction Motors

et al. 2019

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This paper presents a comparative study on the application of different neural network structures to early detection of electrical faults in induction motor drives. The diagnosis inference of the stator inter-turn short-circuits and broken rotor bars is based on the analysis of an axial flux of the induction motor. In order to automate the fault detection process, three different structures of neural networks were used: multi-layer perceptron, self-organizing Kohonen network and recursive Hopfield network. Tests were carried out for various levels of stator and rotor failures. In order to assess the sensitivity of the applied neural detectors, the tests were carried out for variable load conditions and for different values of the supply voltage frequency. Experimental results of the elaborated neural detectors are presented and discussed.

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Fault Diagnosis of Induction Motor using Neural Networks

Cited by 16 publications

References 19 publications

A Motor Fault Diagnosis System Based on Cerebellar Model Articulation Controller

A Motor Fault Diagnosis System Based on Cerebellar Model Articulation Controller

Fault Diagnosis and Tolerant Control of Three-Level Neutral-Point Clamped Inverters in Motor Drives

Effectiveness of Selected Neural Network Structures Based on Axial Flux Analysis in Stator and Rotor Winding Incipient Fault Detection of Inverter-fed Induction Motors

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