A dynamic structure-adaptive symbolic approach for slewing bearings' life prediction under variable working conditions. (January 2021)

• Record Type: Journal Article
• Authors: Ding, Peng; Jia, Minping; Wang, Hua
• Is Part Of: Structural health monitoring. Volume 20:Number 1(2021)
• Page Start: 273
• Page End: 302
• Publication Date: 2021-01
• Subjects: Symbolic life model groups -- slewing bearing -- residual useful life prediction -- evolving model structure -- real-time adaptation -- genetic programming; Structural health monitoring -- Periodicals; Structural stability -- Periodicals; Strength of materials -- Periodicals; Nondestructive testing -- Periodicals; Constructions -- Stabilité -- Périodiques; Résistance des matériaux -- Périodiques; Contrôle non destructif -- Périodiques; Electronic journals; 624.17
• DOI: 10.1177/1475921720929939
• Languages: English
• ISSNs: 1475-9217

Abstract

Data-driven technologies, especially artificial intelligence ones, are widely used in residual useful life (RUL) prediction of machinery. They are flexible in predicting RUL without grasping prior knowledge of physical mechanisms. However, interpretability is generally absent, which makes them like "black boxes." This shortcoming directly raises considerable uncertainty of high-reliability applications, questions on the trustworthiness of decision-making results, and even results in poor generalization under cross-domain RUL predictions. Therefore, the idea of exploring explainable life prediction models with desired cross-domain prediction performances is motivated. The above dilemmas are tackled through the in-depth research of symbolic life models: an evolving symbolic regression approach, namely, dynamic structure–adaptive symbolic approach (DSASA). DSASA, visually displaying internal model structures, gives considerations to historical samples and dynamically accommodates real-time degradation of service parts. In brief, multi-signal-based health indicators are first entered into three genetic programming algorithms for initial life modeling. Afterward, DSASA reconstructs initial life expressions and tracks the real-time degradation of machinery via dynamic coupling terms. Finally, cross-validations are conducted through slewing bearings' accelerated run-to-failed experiments under variable working conditions. Encouraging prediction results deliver that DSASA has reliable generalizations and significantly reduces prediction error by 82.5%, 45.5%, and 79.8% compared with initial models before reconstruction.