Degradation prediction and rolling predictive maintenance policy for multi-sensor systems based on two-dimensional self-attention. (October 2022)

• Record Type: Journal Article
• Authors: Xia, Jun; Feng, Yunwen; Lu, Cheng; Song, Zhicen; Du, Qianyi
• Is Part Of: Advanced engineering informatics. Volume 54(2022)
• Publication Date: 2022-10
• Subjects: Maintenance policy -- Multi-sensor system -- Two-dimensional self-attention -- Rolling predictive maintenance -- Maintenance decision; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2022.101772
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: A degradation prediction method called TDSA is proposed; Proposed a high efficiency RPdM policy to make maintenance decisions; The TDSA method extract features from time dimensional and feature dimensional; The RPdM policy successfully to determined the order time and maintenance time; RPdM policy is robustness with different out-of-stock costs and corrective costs; Abstract: Traditional preventive maintenance policy gradually failed to guarantee the security and economy of current mechanical systems. This paper proposed a highly efficient rolling predictive maintenance (RPdM) policy for multi-sensor system, to make maintenance decisions. In this policy, to cope with the uncertainty of remaining useful life (RUL) prediction, the degradation process of the system is first divided into four intervals according to the inspection interval and spare parts lead time. Then, the two-dimensional self-attention (TDSA) method, which extract time dimensional and feature dimensional features by parallel computation, is developed to predict the probabilities of system RUL in the four intervals instead of the point of RUL. In addition, the output probabilities of the TDSA method are utilized to calculate the maintenance cost rate of the current inspection point and future point. The maintenance decision including spare parts ordering time and maintenance time is determined by comparing the maintenance cost rate of each inspection point, and the decision is updated at the next inspection point. To verify the effectiveness of the proposed RPdM policy, the C-MAPSS dataset provided by NASA is employed to implement degradation prediction and maintenance decision. Experiment results show that the cost rate of RPdM policy is lower than preventive maintenance policy, and only 27.7% higher than ideal maintenance policy which is impossible in real engineering. Moreover, the impact of different out-of-stock costs and corrective costs are explored and shows the good robustness of the RPdM policy.