title: An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox.
abstract: A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment.
authors.affiliation: School of Mechanical Engineering, Tianjin University, Tianjin 300354, China. jingluyang@outlook.com.
authors.forename: Luyang
authors.initials: L
authors.lastname: Jing
authors.affiliation: School of Mechanical Engineering, Tianjin University, Tianjin 300354, China. taiyongwang@yahoo.com.
authors.forename: Taiyong
authors.initials: T
authors.lastname: Wang
authors.affiliation: School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China. zhaomingxjtu@mail.xjtu.edu.cn.
authors.forename: Ming
authors.initials: M
authors.lastname: Zhao
authors.affiliation: School of Mechanical Engineering, Tianjin University, Tianjin 300354, China. pengwang@tju.edu.cn.
authors.forename: Peng
authors.initials: P
authors.lastname: Wang
keywords: deep convolutional neural networks; fault diagnosis; feature learning; multi-sensor data fusion; planetary gearbox