A new sorting feature-based temporal convolutional network for remaining useful life prediction of rotating machinery. (October 2021)

• Record Type: Journal Article
• Authors: Sun, Heng; Xia, Min; Hu, Yawei; Lu, Siliang; Liu, Yongbin; Wang, Qunjing
• Is Part Of: Computers & electrical engineering. Volume 95(2021)
• Publication Date: 2021-10
• Subjects: Sorting feature -- RUL prediction -- rotating machinery -- local mean decomposition -- temporal convolutional network; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107413
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: New sorting feature is designed for machine remaining useful life (RUL) prediction Original vibration signal is decomposed using local mean decomposition Sorting feature is constructed using bubble sort algorithm RUL prediction is based on temporal convolutional network model Abstract: A new sorting feature is designed to improve the remaining useful life (RUL) prediction accuracy of rotating machinery. First, the life-cycle signals of rotating machinery are decomposed into several subsignals through local mean decomposition. Second, the amplitude values of each subsignal are sorted from minimum to maximum, and the sorting feature is constructed. The sorting features of all subsignals are then arranged to construct a feature map. Lastly, the feature maps are inputted into a temporal convolutional network to train a prediction model. The well-trained model is used to predict the RULs of other rotating machines. The effectiveness and efficiency of the proposed method are validated using bearing and gearbox datasets in comparison with other conventional techniques. The new sorting feature reflects the intrinsic connections among signal amplitudes and therefore improves prediction accuracy. The proposed method exhibits great potential applications in the RUL prediction of rotating machines. Graphical abstract: Image, graphical abstract The framework and algorithm flowchart of the proposed method.