Rotor fault diagnosis in a Squirrel-Cage Induction Machine using support vector

“…Something comparable can be said about SVM based methodologies, which have been given enormous consideration over recent years. In [17] there is a remarkable example of recent applications of SVM-based strategies to classify rotor faults.…”

Section: Introductionmentioning

confidence: 99%

Shallow Versus Deep Neural Networks in Gear Fault Diagnosis

Cirrincione

Kumar

Mohammadi

et al. 2020

IEEE Trans. Energy Convers.

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Accurate gear defect detection in induction machinebased systems is a fundamental issue in several industrial applications. At this aim, shallow neural networks, i.e. architectures with only one hidden layer, have been used after a feature extraction step from vibration, torque, acoustic pressure and electrical signals. Their additional complexity is justified by their ability in extracting its own features and in the very high-test classification rates. These signals are here analyzed, both geometrically and topologically, in order to estimate the class manifolds and their reciprocal positioning. At this aim, the different states of the gears are studied by using linear (Pareto charts, biplots, principal angles) and nonlinear (curvilinear component analysis) techniques, while the class clusters are visualized by using the parallel coordinates. It is deduced that the class manifolds are compact and well separated. This result justifies the use of a shallow neural network, instead of a deep one, as already remarked in the literature, but with no theoretical justification. The experimental section confirms this assertion, and also compares the shallow neural network results with the other machine learning techniques used in the literature.

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“…Currently, different diagnostic methods are based on various diagnostic signals. In the case of detecting failures of the stator and rotor windings of IM, these signals are: voltages [10,11], currents [3,4], vibrations [12,13], axial flux [14,15], speed and temperature [11,16], torque [17] and also the internal signals of the control structure in the vector-controlled drive systems [18]. The selection of the diagnostic signal and the method of searching for symptoms of damage related to the signal processing algorithm used, have a direct impact on the speed of the detection process.…”

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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Rotor fault diagnosis in a Squirrel-Cage Induction Machine using support vector

Cited by 11 publications

References 19 publications

Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art

Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art

Shallow Versus Deep Neural Networks in Gear Fault Diagnosis

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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