Intelligent fault diagnosis of rotating machinery using lightweight network with modified tree-structured parzen estimators

Deep learning-based methods have been widely used in the field of rotating machinery fault diagnosis. It is of practical significance to improve the calculation speed of the model on the premise of ensuring accuracy, so as to realise real-time fault diagnosis. However, designing an efficient and lightweight fault diagnosis network requires expert knowledge to determine the network structure and adjust the hyperparameters of the network, which is time-consuming and laborious. In order to design fault diagnosis networks considering both time and accuracy effortlessly, a novel lightweight network with modified tree-structured parzen estimators (LN-MT) is proposed for intelligent fault diagnosis of rotating machinery. Firstly, a lightweight framework based on global average pooling and group convolution is proposed, and a hyperparameter optimisation (HPO) method based on Bayesian optimisation called tree-structured parzen estimator is utilised to automatically search the optimal hyperparameters for the fault diagnosis task. The objective of the HPO algorithm is the weighting of accuracy and calculating time, so as to find models that balance both time and accuracy. The results of comparison experiments indicate that LN-MT can achieve superior fault diagnosis accuracies with few trainable parameters and less calculating time.

Automated summary

A lightweight network with modified tree-structured parzen estimators (LN-MT) is proposed for intelligent fault diagnosis of rotating machinery in this paper. By introducing a lightweight framework based on global average pooling and group convolution, and utilizing tree-structured parzen estimator for hyperparameter optimization based on Bayesian optimization, models that balance both time and accuracy are found. Comparison experiments show that LN-MT achieves superior fault diagnosis accuracies with few trainable parameters and less calculating time.