Detection of gear fault severity based on parameter-optimized deep belief network using sparrow search algorithm

In gear fault diagnosis, most current intelligent fault diagnosis methods show good classification performance for fault pattern recognition. However, when detecting fault severity, the difficulty of diagnosis is increased due to the high similarity between the monitoring signals, which requires improving the sensitivity, stability, and accuracy of diagnosis methods. To address this issue, a parameter-optimized deep belief network (DBN) based on sparrow search algorithm (SSA) is proposed for gear fault severity detection. Firstly, the initial DBN is trained by the labeled gear fault signals in different severities. Secondly, SSA is introduced to optimize the learning rate and the batch size of the initial DBN, so as to avoid the interference caused by selecting network parameters by subjective experience. Finally, the detection method of gear fault severity based on the improved DBN with the optimal parameter combination is constructed. The performance of the proposed method is evaluated by analyzing the gear datasets under five degrees of tooth-breaking fault, the results show that the average detection accuracy reaches over 96% with a standard deviation of 1.46%. Compared with other methods, it is proved that the proposed method has better feature extraction ability, stability, and accuracy for gear fault severity detection.

Automated summary

A parameter-optimized deep belief network (DBN) based on sparrow search algorithm (SSA) is proposed for gear fault severity detection, improving sensitivity, stability, and accuracy. By avoiding interference caused by subjective parameter selection, the method showed over 96% average detection accuracy with a standard deviation of 1.46%, proving better feature extraction ability, stability, and accuracy compared to other methods.