Explicit speed-integrated LSTM network for non-stationary gearbox vibration representation and fault detection under varying speed conditions

Yuejian Chen, Xuemei Liu, Meng Rao, Yong Qin, Zhipeng Wang and Yuanjin Ji

Reliability Engineering and System Safety, 2025, vol. 254, issue PA

Abstract: Condition monitoring of the gearbox plays a crucial role in implementing proactive maintenance strategies and minimizing the economic loss of unexpected failures. Gearboxes often operate under variable speed conditions, which makes the collected vibration monitoring signals non-stationary. Existing works did not explore the scientific structures that incorporate speed signals into the long short-term memory (LSTM) networks, and thus leave room for improvement at varying speed conditions. To this end, this paper proposes novel explicit speed-integrated LSTM (SI-LSTM) models to enhance the representation accuracy of non-stationary vibration signals and improve gearbox fault detection capability. The SI-LSTM models with three variants are designed to account for the effects of speed variations on vibration signals. In SI-LSTM model 1, the vibration and speed signals are directly merged and input into the LSTM network. In SI-LSTM model 2, the speed signal is integrated into the network before the final LSTM layer. SI-LSTM model 3 is designed with a dedicated LSTM layer for speed signal, and the state outputs of both speed and vibration LSTMs are then merged and input into a final LSTM layer. Comprehensive experiments are conducted on a helical fixed axis gearbox dataset and a planetary gearbox dataset, and finally SI-LSTM model 3 is the best recommended structure. Spectral analysis is used to demonstrate the effectiveness of SI-LSTM model 3. The performance are also compared with four state-of-the-art methods, and the SI-LSTM model 3 achieves the highest AUCs of 0.9998 and 0.9676 and the best vibration representation accuracy on fixed-axis and planetary gearbox datasets, respectively.

Keywords: Gearbox; Fault detection; Varying speed condition; Long short-term memory (search for similar items in EconPapers)
Date: 2025
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:254:y:2025:i:pa:s0951832024006677

DOI: 10.1016/j.ress.2024.110596

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