Fault diagnosis of marine electric thruster bearing based on fusing multi-sensor deep learning models

Abstract

The data-driven fault diagnosis of bearings is important in the marine electric thruster. For avoiding information loss when manually extracting features and unreliable diagnosis by a single sensor, a novel method of fusing multi-sensor deep learning models is proposed. The improved one-dimensional convolution network (1DCNN) can adaptively extract features from single-sensor signals and use them for preliminary fault diagnosis at first. Then, the diagnosis results of different sensors are fused by the evidential reasoning (ER) rule. We found that the diagnosis accuracy at the training set size of 40 % is 99.4 %, which is better than four machine learning methods and ten state-of-the-art deep learning methods. Furthermore, at different noise levels (0–10 dB), the diagnostic accuracy is higher than 89 %, showing more robustness than single-sensor deep learning methods. Meanwhile, its suitability is further validated under different torque conditions.