Classification of Fault Severity in Induction Machine Systems Based on Temporal Convolutions and Recurrent Networks

Author:

Mashayekhi V.1ORCID,Hasani Borzadaran S.2ORCID,Hoseintabar Marzebali M.1ORCID

Affiliation:

1. Department of Electrical Engineering, Shahrood University of Technology, Shahrood, Iran

2. Faculty of Computer Engineering, Shahrood University of Technology, Shahrood, Iran

Abstract

Detection and severity identification of mechanical and electrical faults by means of noninvasive methods such as electrical signatures of induction machine have attracted much attention in recent years. Since operating conditions of machines and severity of faults in incipient stages influence the amplitude of fault index in the fault detection process, diagnosing fault occurrence and severity can be more complicated. In this study, an efficient method for fault detection and classification in induction machine based on deep neural networks is introduced. The introduced method applies the long short-term memory (LSTM) and fully convolutional neural networks (FCNs) in a conjoined manner. The authors use the FCN architecture for feature extraction from the time-series signal and augment it with LSTM to improve classification performance. This structure has not been previously applied for fault severity detection in induction machine systems. The authors avoid manual feature engineering and, by eliminating the preprocessing phase, directly use time series of electrical signals for fault detection and classifications. The experimental results have been carried out in different fault severities and loads. The analysis of the results and comparison with other deep and classical methods show that the faulty cases can be separated based on severity and load levels with a high accuracy (98.92%), which shows that the adopted architecture is successful in automatically extracting discriminative features from the signal.

Publisher

Hindawi Limited

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology,Modeling and Simulation

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A grid fault diagnosis framework based on adaptive integrated decomposition and cross-modal attention fusion;Neural Networks;2024-10

2. Multi-mode signal fusion and improved residual dense network fault diagnosis of nuclear power plant;2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS);2023-09-22

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