Intelligent ball screw fault diagnosis using a deep domain adaptation methodology

Abstract

Intelligent data-driven fault diagnosis methods have been successfully developed in the recent years. However, as one of the most important machines in the industries, the ball screw health monitoring problem has received less attention, due to the complex operating patterns and sophisticated mechanical structures. In practice, the working conditions of the ball screws usually change, that further makes the fault diagnosis problem more challenging since the data distributions are not the same. In order to address this issue, a deep learning-based domain adaptation method is proposed for the cross-domain ball screw fault diagnosis problem. The deep convolutional neural network is adopted for feature extraction and health condition classification. The maximum mean discrepancy metric is proposed to measure and optimize the data distributions of different operating conditions. A data segmentation method which is specially designed for the ball screw is further integrated. The experiments on the real ball screw condition monitoring data are carried out for validation. The results indicate the proposed approach is promising for the cross-domain diagnostic tasks of the ball screw in the real industries.