Multiple-signal defect identification of hydraulic pump using an adaptive normalized model and S transform

Abstract

Axial piston pump plays a pivotal role in a hydraulic transmission system since it can supply the core power source. The complexity of structure and the invisibility of failure feature bring more difficulties and challenges to fault identification of an axial piston pump. It is of great meaning to exploit an effective and feasible fault identification method for the safe and stable system operation. An improved convolutional neural network (CNN) is constructed by mining and utilizing the interdisciplinary advantages of artificial intelligence and machinery engineering. First, the modeling takes the batch normalization strategy into account due to its capability of decreasing the data distribution differences. Second, Bayesian algorithm is used for intelligent tuning of hyperparameters. Third, the improved CNN is applied to fault identification based on the S transform of multiple source signals. The performance of the proposed method is demonstrated by the experiments on an axial piston pump. The identification accuracies based on three signals reach 88.17%, 95.94%, and 99.44%. Results show that it can automatically recognize typical faults of the axial piston pump, and present the superiority to common CNNs.