Deep Learning Domain Adaptation for Electro-Mechanical Actuator Fault Diagnosis Under Variable Driving Waveforms

Abstract

Benefited from the sampling signal from monitoring sensors installed in electro-mechanical actuator (EMA), data-driven methods, such as deep learning models that can mine adaptive features from monitoring signal, have received wide attention in EMA fault diagnosis. A generalized hypothesis accepted by previous efforts on EMA fault diagnosis, is that target domain datasets (i.e., testing monitoring signal) are drawn from the same distribution with source domain datasets (i.e., training monitoring signal). However, except for the different working loads and velocities, various driving signal waveforms also exits in EMA, which further increase the domain discrepancy challenge and decrease the performance of those deep learning models. Therefore, this paper proposes a deep learning domain adaptation method that introduces joint maximum mean discrepancy (JMMD) into convolutional neural network (CNN), which aims to reduce the domain difference between labeled source domain datasets and unlabeled target domain datasets. In order to simulate domain discrepancy phenomenon, the NASA EMA datasets, sampled from acceleration sensors under variable driving waveforms with different working loads and velocities, are considered in this paper. Experiment results indicate that CNN-JMMD method can reduce the distribution difference and improve the diagnosis accuracy in source and target domains. In comparison with several available methods, extensive experimental evaluations on six domain tasks indicate the effectiveness of the proposed method. In addition, better performance has been obtained by selecting the proper trade-off factor between CNN and JMMD.