Intelligent fault diagnosis of hydraulic piston pump based on deep learning and Bayesian optimization

Hydraulic axial piston pump is broadly-used in aerospace, ocean engineering and construction machin-ery since it is the vital component of fluid power systems. In the light of the undiscoverability of its fault and the potential serious losses, it is valuable and challenging to complete the fault identification of a hydraulic pump accurately and effectively. Owing to the limitations of shallow machine learning methods in the intelligent fault diagnosis, more attention has been paid to deep learning methods. Hyperparameter plays an important role in a deep learning model. Although some manual tuning methods may represent good results in some cases, it is hard to reproduce due to the differences of datasets and other factors. Hence, Bayesian optimization (BO) algorithm is adopted to automatically select the hyperparameters. Firstly, the time-frequency images of vibration signals by continuous wavelet transform are taken as input data. Secondly, by setting some hyperparameters, a preliminary convolutional neural network (CNN) model is established. Thirdly, by identifying the range of each hyperparameter, BO based on Gaussian process is employed to construct an adaptive CNN model named CNN-BO. The performance of CNN-BO is verified by comparing with traditional LeNet 5 and improved LeNet 5 with manual optimization. The results indicate that CNN-BO can accomplish the intelligent fault diagnosis of a hydraulic pump accurately.(c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.

Automated summary

This article investigates the problem of fault diagnosis in hydraulic pumps, using a deep learning method for fault identification and introducing the Bayesian optimization algorithm for selecting hyperparameters. By comparing with traditional methods, the results show that CNN-BO can accurately achieve intelligent fault diagnosis of hydraulic pumps.