Abstract
Purpose
Rotating machinery fault diagnosis is getting more attention nowadays as it improves industrial safety. Most fault diagnosis approaches proposed by researchers can diagnose only one fault at a time. However, compound defects tend to occur more frequently because of the close interaction of many components in industrial applications. Hence, a compound fault diagnosis is required to operate the machinery safely over a long time.
Methods
In this study, a unique Bessel kernel-based Time–Frequency Distribution known as the Bessel Transform is proposed as a technique for the fault detection of a compound gear-bearing system. The Bessel Transform is paired with a feature selection technique based on an artificial bee colony algorithm to choose the features that provide accurate information about the problems. Finally, the chosen features are classified using a long-short memory network.
Results
A case study is used to validate the effectiveness of the suggested approach, and a testing efficiency of 96.75% is achieved.
Conclusion
The results show that the proposed transform in compound gear-bearing fault identification is adequate compared with the traditional time–frequency transforms in compound gear-bearing identification.
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Athisayam, A., Kondal, M. A Unified Approach for Compound Gear-Bearing Fault Diagnosis Using Bessel Transform, Artificial Bee Colony-Based Feature Selection and LSTM Networks. J. Vib. Eng. Technol. 12, 2959–2973 (2024). https://doi.org/10.1007/s42417-023-01024-1
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DOI: https://doi.org/10.1007/s42417-023-01024-1