An Interturn Short Circuit Fusion Model of Traction Motors for Fault Diagnosis

2021 ◽  
Author(s):  
Lisong He ◽  
Jinsong Kang ◽  
Hongzhe Li
Keyword(s):  
Fault Diagnosis ◽  
Short Circuit ◽  
Fusion Model ◽  
Traction Motors

scholarly journals Modeling and Fault Diagnosis of Interturn Short Circuit for Five-Phase Permanent Magnet Synchronous Motor

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Jian-wei Yang ◽  
Man-feng Dou ◽  
Zhi-yong Dai
Keyword(s):  
Parameter Estimation ◽  
Fault Diagnosis ◽  
Permanent Magnet ◽  
Fault Tolerant ◽  
Short Circuit ◽  
Estimation Method ◽  
Trust Region ◽  
Fault Tolerant Control ◽  
Trust Region Algorithm ◽  
Parameter Estimation Method

Taking advantage of the high reliability, multiphase permanent magnet synchronous motors (PMSMs), such as five-phase PMSM and six-phase PMSM, are widely used in fault-tolerant control applications. And one of the important fault-tolerant control problems is fault diagnosis. In most existing literatures, the fault diagnosis problem focuses on the three-phase PMSM. In this paper, compared to the most existing fault diagnosis approaches, a fault diagnosis method for Interturn short circuit (ITSC) fault of five-phase PMSM based on the trust region algorithm is presented. This paper has two contributions. (1) Analyzing the physical parameters of the motor, such as resistances and inductances, a novel mathematic model for ITSC fault of five-phase PMSM is established. (2) Introducing an object function related to the Interturn short circuit ratio, the fault parameters identification problem is reformulated as the extreme seeking problem. A trust region algorithm based parameter estimation method is proposed for tracking the actual Interturn short circuit ratio. The simulation and experimental results have validated the effectiveness of the proposed parameter estimation method.

scholarly journals A Comprehensive Review of Winding Short Circuit Fault and Irreversible Demagnetization Fault Detection in PM Type Machines

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3309 ◽  
Author(s):  
Zia Ullah ◽  
Jin Hur
Keyword(s):  
Fault Diagnosis ◽  
Early Stage ◽  
Short Circuit ◽  
Industrial Applications ◽  
Operational Environment ◽  
Compact Structure ◽  
Torque Density ◽  
Prime Concern ◽  
Attractive Option ◽  
The Impact

Contemporary research has shown impetus in the diagnostics of permanent magnet (PM) type machines. The manufacturers are now more interested in building diagnostics features in the control algorithms of machines to make them more salable and reliable. A compact structure, exclusive high-power density, high torque density, and efficiency make the PM machine an attractive option to use in industrial applications. The impact of a harsh operational environment most often leads to faults in PM machines. The diagnosis and nipping of such faults at an early stage have appeared as the prime concern of manufacturers and end users. This paper reviews the recent advances in fault diagnosis techniques of the two most frequently occurring faults, namely inter-turn short fault (ITSF) and irreversible demagnetization fault (IDF). ITSF is associated with a short circuit in stator winding turns in the same phase of the machine, while IDF is associated with the weakening strength of the PM in the rotor. A detailed literature review of different categories of fault indexes and their strengths and weaknesses is presented. The research trends in the fault diagnosis and the shortcomings of available literature are discussed. Moreover, potential research directions and techniques applicable for possible solutions are also extensively suggested.

Multiscale convolutional neural network and decision fusion for rolling bearing fault diagnosis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Defeng Lv ◽  
Huawei Wang ◽  
Changchang Che
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Rolling Bearing ◽  
Decision Fusion ◽  
Fusion Model ◽  
Vibration Signals ◽  
Content Type ◽  
Proposed Model

Purpose The purpose of this study is to achieve an accurate intelligent fault diagnosis of rolling bearing. Design/methodology/approach To extract deep features of the original vibration signal and improve the generalization ability and robustness of the fault diagnosis model, this paper proposes a fault diagnosis method of rolling bearing based on multiscale convolutional neural network (MCNN) and decision fusion. The original vibration signals are normalized and matrixed to form grayscale image samples. In addition, multiscale samples can be achieved by convoluting these samples with different convolution kernels. Subsequently, MCNN is constructed for fault diagnosis. The results of MCNN are put into a data fusion model to obtain comprehensive fault diagnosis results. Findings The bearing data sets with multiple multivariate time series are used to testify the effectiveness of the proposed method. The proposed model can achieve 99.8% accuracy of fault diagnosis. Based on MCNN and decision fusion, the accuracy can be improved by 0.7%–3.4% compared with other models. Originality/value The proposed model can extract deep general features of vibration signals by MCNN and obtained robust fault diagnosis results based on the decision fusion model. For a long time series of vibration signals with noise, the proposed model can still achieve accurate fault diagnosis.

Incipient short-circuit fault diagnosis of lithium-ion batteries

2020 ◽  
Vol 31 ◽  
pp. 101658 ◽  
Author(s):  
Jianwen Meng ◽  
Moussa Boukhnifer ◽  
Claude Delpha ◽  
Demba Diallo
Keyword(s):  
Fault Diagnosis ◽  
Lithium Ion Batteries ◽  
Short Circuit ◽  
Lithium Ion ◽  
Circuit Fault Diagnosis ◽  
Short Circuit Fault

scholarly journals Frequency and time fault diagnosis methods of power transformers

2018 ◽  
Vol 18 (4) ◽  
pp. 162-167 ◽  
Author(s):  
Miroslav Gutten ◽  
Daniel Korenciak ◽  
Matej Kucera ◽  
Richard Janura ◽  
Adam Glowacz ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
High Voltage ◽  
Impact Test ◽  
Power Transformer ◽  
Short Circuit ◽  
Small Deformation ◽  
Short Circuit Current ◽  
Diagnostic Methods ◽  
Response Analysis ◽  
Short Circuit Currents

Abstract The authors describe experimental and theoretical analyses of faults of power transformer winding. Faults were caused by mechanical effect of short-circuit currents. Measurements of transformer were carried out in high-voltage laboratory. Frequency and time diagnostic methods (method SFRA - Sweep Frequency Response Analysis, impact test) were used for the analyses. Coils of transformer windings were diagnosed by means of the SFRA method and the time impact test. The analyzed methods had a significant sensitivity to a relatively small deformation of coil. In the analysis a new technique for analyzing the effects of short-circuit currents is introduced. This technique is developed for high-voltage transformers (different types of power). The proposed analyses show that it is necessary to analyze the value of short-circuit current. Short-circuit current represents a danger for the operation of the power transformer. The proposed approach can be used for other types of transformers. Moreover, the presented techniques have a potential application for fault diagnosis of electrical equipment such as: transformers and electrical machines.

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