Intelligent Diagnostics for Ball Screw Fault Through Indirect Sensing Using Deep Domain Adaptation

Abstract

Intelligent data-driven fault diagnostics for rotating machinery is well established. However, ball screws pose a unique challenge of impractical sensor locations for long-term deployment due to their complex motion trajectory and sophisticated mechanical structure. To overcome this challenge, an indirect sensing method is proposed. While techniques are available for multiple transfer learning tasks, cross-sensor domain adaptation remains unexplored. Thus, a convolutional neural network-based domain adaptation method is proposed that minimizes the maximum mean discrepancy of high-level representations between domains and exploits novel parallel data to attain class-level alignment. Proposed method achieved a mean testing accuracy of 98.25% upon validation on 33 transfer tasks designed across five accelerometer locations on a ball screw testbed depicting nine health conditions through variations in preload levels and backlash. This convenience of transferability of the diagnostic model between sensor locations can go a long way in robust and reliable condition monitoring of critical assets.