Field degradation modeling and prognostics under time-varying operating conditions: A Bayesian based filtering algorithm

Li, Shizheng; Yang, Zhaojun; Chen, Chuanhai; Yu, Chunming; Tian, Hailong; Jin, Tongtong

doi:10.1016/j.apm.2021.06.032

Cited by 5 publications

(1 citation statement)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In such tests, products are exposed to harsher-than-normal conditions (e.g., load, strain, temperature, voltage, vibration, and pressure) in an efort to collect failure time data in a short amount of time without changing the failure mechanisms [48,49]. A lot of existing research using accelerating test data to verify their prognostic methods [50][51][52], and their studies show that features extracted in accelerating test data can be used to validate the remaining useful life, indicate the degradation process, and evaluate the reliability. In our case, we need to make sure that the test of harmonic reducers cannot exceed the tolerance limit [53].…”

Section: Accelerated Life Test Of Harmonic Reducers As Shown Inmentioning

confidence: 99%

Discovering New Prognostic Features for the Harmonic Reducer in Remaining Useful Life Prediction

Jiang

et al. 2023

Shock and Vibration

View full text Add to dashboard Cite

Vibration and current signals are widely used in fault diagnosis and life prediction of electromechanical transmission systems. However, due to the complex working environment of harmonic reducer in the industrial robot, using a single signal for failure analysis or life prediction may risk to false alarming due to lacking of fault information. In the early operation stage of equipment, fault information contained in the signal is too weak to be extracted. In addition, there is certain correlation among the features, which can lead to meaningless superposition of the calculation process. Therefore, a new health index construction method is proposed integrating the current signal and vibration signal and reducing the redundancy among the features in multidomains and can effectively enhance the fault information. In addition, in view of the local optimum and slow speed caused by the random initialization of the network model, an improved life prediction method is proposed to optimize BP neural network to improve the prediction performance. The proposed method is verified by the test data of the harmonic reducer. Results show that the proposed method can predict the remaining useful life of the harmonic reducer more accurately and effectively.

show abstract

Section: Accelerated Life Test Of Harmonic Reducers As Shown Inmentioning

confidence: 99%

Discovering New Prognostic Features for the Harmonic Reducer in Remaining Useful Life Prediction

Jiang

et al. 2023

Shock and Vibration

View full text Add to dashboard Cite

show abstract

Multi-attribute Bayesian fault prediction for hidden-state systems under condition monitoring

Duan

et al. 2022

Applied Mathematical Modelling

View full text Add to dashboard Cite

Unbiased parameters estimation and mis-specification analysis of Wiener process-based degradation model with random effects

Tang

Wang

Sun

et al. 2022

Applied Mathematical Modelling

View full text Add to dashboard Cite

Field degradation modeling and prognostics under time-varying operating conditions: A Bayesian based filtering algorithm

Cited by 5 publications

References 34 publications

Discovering New Prognostic Features for the Harmonic Reducer in Remaining Useful Life Prediction

Discovering New Prognostic Features for the Harmonic Reducer in Remaining Useful Life Prediction

Multi-attribute Bayesian fault prediction for hidden-state systems under condition monitoring

Unbiased parameters estimation and mis-specification analysis of Wiener process-based degradation model with random effects

Contact Info

Product

Resources

About