MDCCS Based Multistage Life Prediction of Slewing Bearing with a Novel Performance Description: an Improved Variational Mode Decomposition Approach

Ding, Peng; Wang, H.; Dai, Yongfen; Chen, J.; Zhang, H.; Sun, Fengming

doi:10.1007/s40799-019-00308-0

Cited by 15 publications

(16 citation statements)

References 26 publications

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“…Our study applies three kinds of sensors: accelerometers, temperature, and torque sensors to fully characterize and reflect the entire degradation process of slewing bearings. [29][30][31] Locations of sensor installations are drawn in Figure 4(b) and (c), where each accelerometer is arranged every 90 degrees to monitor the degradation process more comprehensively, temperature sensors are installed at the oil fill hole, and the torque sensor is fixed on the drive assembly by couplings.…”

Section: Methodology For Rul Prediction Of Slewing Bearingsmentioning

confidence: 99%

“…Life cycle fatigue or damage tests of slewing bearings cost too much, so this community has not yet owned a large amount of reliable data like rolling bearings. Wang and colleagues [29][30][31] fused multi-physical signals to represent degradation indicators, and then realized RUL prediction by data-driven technology. This multi-physical signal-oriented method can characterize degradation and damage, so as to improve prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

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

Ding

Jia

Wang

2020

Structural Health Monitoring

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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.

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Section: Methodology For Rul Prediction Of Slewing Bearingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Ding

Jia

Wang

2020

Structural Health Monitoring

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“…Three main types of features: time domain, frequency domain and time-frequency domain features are utilized in this paper. Detailed formula expressions are the same with [16].…”

Section: B Features Extraction and Selectionmentioning

confidence: 99%

“…Vibration is the traditional signal for condition monitoring and life prediction of mechanical parts. Experts in the field of slewing bearings utilize more than vibration signal [14]- [16] for their life prediction or fault diagnosis. We suppose that failure information will be lost if vibration signal is used alone for condition monitoring of slewing bearings and temperature or torque signals are more representative of degeneracy of slewing bearings.…”

Section: ) Comparisons Under Different Situationmentioning

confidence: 99%

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A Symbolic Regression Based Residual Useful Life Model for Slewing Bearings

2019

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Slewing bearings are vital functional components of large machinery. It is of far reaching significance to study their life prediction and health management. Many studies are based on data-driven approaches. However, part of them in the form of ''black-box'' lack actual physical meanings due to opacity model structures and have difficulty in choosing optimal parameters. Few kinds of literature focus on explicit model relationships for slewing bearings' life models. In this paper, a novel approach based on symbolic regression is proposed with the aim of exploring slewing bearings' explicit life models in depth and to predict residual useful life (RUL). The proposed method integrates the strengths of multiple signals describing a comprehensive response to slewing bearings' health and various genetic programming (GP) algorithms modeling life expressions. In addition, independent, hybrid, and piecewise strategies are introduced and explicit model relationships with respect to degradation indicators (DIs) are established via GPs. To verify the proposed method, three run-to-failure experiments under discrepant operating conditions of slewing bearings are carried out. Prediction results demonstrate that models generated by epigenetic linear genetic programming (ELGP) under hybrid and piecewise modeling strategy with similarity-based combination strategy perform best. More importantly, their life expressions are more succinct and intelligible than in other situations. INDEX TERMS Predictive models, prognostics and health management, remaining life assessment, slewing bearings, symbolic regression.

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Intelligent fault diagnosis and health stage division of bearing based on tensor clustering and feature space denoising

Wei

Jin

et al. 2023

Appl Intell

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MDCCS Based Multistage Life Prediction of Slewing Bearing with a Novel Performance Description: an Improved Variational Mode Decomposition Approach

Cited by 15 publications

References 26 publications

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

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

A Symbolic Regression Based Residual Useful Life Model for Slewing Bearings

Intelligent fault diagnosis and health stage division of bearing based on tensor clustering and feature space denoising

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