Failure Classified Method for Diesel Generators Based Long Short-Term Memory Approach

Deep learning technologies have emerged as optimum solutions for many industrial applications, especially failure-classified of rotating machine problems. The deep learning approach can automatically identify failure types and provide recommendations for unsafe conditions of diesel generators. This...

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Bibliographic Details
Published in2024 International Conference on System Science and Engineering (ICSSE) pp. 1 - 6
Main Authors Liu, Wen-Bin, Bui, Thuc-Minh, Quoc, Tien Nguyen, Thi, Van Pham, Cho, Ming-Yuan, Da, Thao Nguyen, Thanh, Phuong Nguyen
Format Conference Proceeding
LanguageEnglish
Published IEEE 26.06.2024
Subjects
Accuracy
Benchmark testing
Deep learning
deep learning machine
failure diagnosis
Generators
industrial diesel generators
long short-term memory
Maintenance
Measurement
Recurrent neural networks
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Summary:Deep learning technologies have emerged as optimum solutions for many industrial applications, especially failure-classified of rotating machine problems. The deep learning approach can automatically identify failure types and provide recommendations for unsafe conditions of diesel generators. This research proposed the failure diagnosis method based on long short-term memory to provide the maintenance indicators based on different collected variables. The accumulated datasets in the laboratory evaluate the model capability of failure classified with various evaluating metrics. The classified accuracy is compared with the traditional recurrent neural network (RNN), which demonstrates the classified ability to accurately identify various failure types of diesel generators in the fourth industrial revolution.
ISSN:2325-0925
DOI:10.1109/ICSSE61472.2024.10608491