Li, Junxing, Wang, Zhihua, and Shen, Lijuan (2024) A novel RUL prediction method for rolling bearings based on dynamic control chart and adaptive incremental filtering. Measurement Science and Technology, 35. 106138.

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Abstract

Degradation of rolling bearings typically consists of two stages: a stable stage (Stage I) characterized by stable fluctuations in the health indicator (HI), and a degradation stage (Stage II) where early damage leads to HI degradation, eventually reaching the failure threshold. Therefore, to achieve remaining useful life prediction for bearings, three aspects should be studied: (1) degradation modeling; (2) inter stage change point identification; (3) degradation state updating. Firstly, a two-stage degradation model is constructed by simultaneously considering inherent randomness, individual differences, and measurement errors. Then, a dynamic statistical process control (SPC) method is proposed to identify the change point from Stage I to Stage II. The SPC is designed to dynamically control limits based on the bearing’s condition monitoring data to prevent false alarms. An adaptive incremental filtering is proposed to update the degradation states by simultaneously considering the state increment and the dynamics of the system noise and measurement noise. The effectiveness of the proposed method is validated on 16 004 bearing test data and XJTU-SY bearing data. Results show that the proposed method can accuracy identify the change point and improve the accuracy of the prediction result during stage II.

Item ID:	93851
Item Type:	Article (Research - C1)
ISSN:	1361-6501
Copyright Information:	© 2024 IOP Publishing Ltd All rights, including for text and data mining, AI training, and similar technologies, are reserved.
Date Deposited:	31 Aug 2026 04:50
FoR Codes:	49 MATHEMATICAL SCIENCES > 4905 Statistics > 490508 Statistical data science @ 30% 40 ENGINEERING > 4017 Mechanical engineering > 401704 Mechanical engineering asset management @ 70%
SEO Codes:	24 MANUFACTURING > 2412 Machinery and equipment > 241204 Industrial machinery and equipment @ 100%
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