A novel performance degradation prognostics approach and its application on ball screw

The performance degradation prognostics of ball screw means important economic value and engineering application prospect. This paper proposes a performance degradation prognostics method which can be applied on ball screw. A clustering-based ensemble deep auto-encoders (EDAEs) was designed based on the selective ensemble and majority voting to extract features from the acceleration data. Then the sensitive feature distributions of different degradation cycles constitute the Gaussian mixture model (GMM), and the overlap degree of these distributions can be calculated to construct the health indicator. Finally, deep forest algorithm was introduced to achieve trend prognosis of health indicator. Meanwhile, the validity of the proposed method is confirmed by whole life cycle data of ball screw. The experimental results demonstrate that the proposed method can accurately identify the risk level of performance and realize the performance degradation prognostics.