A dynamic structure-adaptive symbolic approach for slewing bearings’ life prediction under variable working conditions

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.

Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study with Industrial Data

“…Next, the PHM-XAI works associated with anomaly detection and failure prognostic are summarized in the following order of presentation: (a) interpretable model [45,46], (b) extraction-based approach [47], (c) decision rules and knowledge-based explanation [48], (d) attention mechanism [49], (e) model agnostic [50], and (f) visual explanation technique [51].…”

“…The dynamic structure-adaptive symbolic approach (DSASA), a cross-domain life prediction model, is elaborated in [45] for slewing bearings RUL prediction. The DSASA presents internal model structures visibly, takes historical run-to-failure data into account, and dynamically adapts to real-time deterioration.…”

Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study of Real-World Gas Turbine Anomalies

“…PHM-XAI works associated with anomaly detection and failure prognostic are summarized. In the order of presentation: (i) interpretable model [40,41]; (ii) extraction-based approach [42]; (iii) decision rules and knowledge-based explanation [43]; (iv) attention mechanism [44]; (v) model agnostic [45]; and (vi) visual explanation technique [46].…”

“…The dynamic structure-adaptive symbolic approach (DSASA), a cross-domain life prediction model, is elaborated in [40] for slewing bearings RUL prediction. The DSASA presents internal model structures visibly, takes historical run-to-failure data into account, and dynamically adapts real-time deterioration.…”

Modified Gaussian convolutional deep belief network and infrared thermal imaging for intelligent fault diagnosis of rotor-bearing system under time-varying speeds

“…Rotor-bearing system is a core part of rotating machinery, and its health states directly affect the safety and operation of the whole equipment. [1][2][3] Due to the harsh operation environments, rotor-bearing system is prone to causing various faults, which would lead to safety accidents and huge economic losses. Accurate and efficient fault diagnosis technology for rotor-bearing system can not only reduce maintenance costs but also improve the reliability and stability of rotating machinery.…”