Motor Bearing Failure Identification Using Multiple Long Short-Term Memory Training Strategies
DOI:
https://doi.org/10.54327/set2024/v4.i2.155Keywords:
Maintenance, Bearing, Fault, Deep Learning, Diagnosis, LSTMAbstract
In the context of condition-based maintenance of rotating machines in manufacturing systems, the early diagnosis of possible faults related to rolling elements of the bearing is mainly based on techniques from artificial intelligence, namely, Machine Learning (ML) and Deep Learning (DL). Approaches based on using Deep Learning methods have been the most coveted in recent years. Among a variety of models, the type of architecture known as Long-Short-Term Memory (LSTM) of Recurrent Neural Network (RNN) has both the ability to capture long-term dependencies and to adapt to sequential data modeling. It is therefore able to work on data without any preprocessing. This paper studies using four types of LSTM networks to diagnose bearing faults in a classification approach. It aims to intervene on both the input parameters and the network architecture, to achieve high performance. The proposed method is carried out in two different ways. In the first case, the data inputs are raw frames of vibration signals. However, in the second case, the network inputs are pre-computed time-frequency features. The results clearly showed that LSTMs are more accurate with the latter.
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Data Availability Statement
The datasets generated and/or analysed during the current study are available in the CWRU repository [34], https://engineering.case.edu/bearingdatacenter/welcome. Any other information about the findings of this study is available from the corresponding author, M. ATMANI (Youcef.atmani@ensta.edu.dz), upon request.
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Copyright (c) 2024 Youcef ATMANI, Ammar Mesloub, Said Rechak
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website, social networking sites, etc).