A public data-set for synchronous motor electrical faults diagnosis with CNN and LSTM reference classifiers

• Published in: Energy and AI Vol. 14; p. 100274
• Main Authors: Sun, Zhenglong, Machlev, Ram, Wang, Qianchao, Belikov, Juri, Levron, Yoash, Baimel, Dmitry
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-10-2023; Elsevier
• Subjects: Classification; CNN LSTM; Deep-learning; Motor faults; Public data-set; Synchronous motors; CNN LSTM; Motor faults; Synchronous motors; Deep-learning; Public data-set; Classification
• ISSN: 2666-5468; 2666-5468
• DOI: 10.1016/j.egyai.2023.100274

In the last two decades, motor operation monitoring tools have become a necessity, and many studies focus on the detection and diagnosis of motor electrical faults. However, at present, a core obstacle that prevents the direct comparison of such classification techniques is the lack of a standard database that can be used as a benchmark. In view of this, we offer here a public experimental data-set that has beendesigned specifically for the comparison of synchronous motor electrical fault classifiers. The data-set comprises five types of motor electrical faults: open phase between inverter and motor; short circuit/leakage current between two phases; short circuit/leakage current in phase-to-neutral; rotor excitation voltage disconnection; and variation of rotor excitation current. In addition, each fault has been recorded as a four-dimensional signal: three phase voltages; three phase currents; motor speed; and motor current. The package includes two deep-learning reference classifiers that are based on a convolutional neural network (CNN) and long short term memory (LSTM). Due to the good performance of these classifiers, we suggest that they can be used by the community as benchmarks for the development of new and better motor electrical fault classification algorithms. The database and the reference classifiers are examined and insights regarding different combinations of features and lengths of recording points are provided. The developed code is available online, and is free to use.
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•An experimental dataset is provided for the comparison of synchronous motor fault classifiers.•The dataset includes five types of motor electrical faults which are monitored by multiple features.•The package includes two reference classifiers that are based on deep-learning techniques.•These classifiers can serve as benchmarks for new motor faults classification algorithms.•The developed code is available online and is free to use.