In underground coal mining, the gear of a shearer’s rocker arm endures extreme stress and environmental fluctuations. Failures in this vital component can pose serious safety hazards, cause prolonged operational downtime, and result in significant financial losses. Therefore, accurate gear fault diagnosis is crucial. However, conventional diagnostic methods often struggle with limited feature extraction and poor performance when dealing with non-stationary, noisy signals typical of this environment. To address these challenges, a hybrid model consisting of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and Markov Transition Model (MTM) is proposed. In this framework, the CNN is used to extract both global and local features related to gear fault. A time-distributed feature extractor is then integrated with the LSTM to capture the temporal progression of these features, aiding in effective modeling of fault evolution over time. Finally, the MTM further refines classification by incorporating probabilistic state transition between fault conditions, thereby improving diagnostic stability and robustness under noise. Experimental validation was done using vibration data from the Taizhong Coal Machinery rocker arm test platform and gear data from Southeast University and achieved up to 99.79% accuracy. These results show this proposed method outperformed other advanced diagnostic methods, offering dependable fault diagnosis and strong noise resistance even under extreme noise conditions of −5 dB SNR.
Fenuku et al. (Fri,) studied this question.