Remaining Useful Life Estimation of Bearings Using Data-Driven Ridge Regression

Predicting the remaining useful life (RUL) of mechanical bearings is a challenging industrial task since RUL can differ even for the same equipment due to many uncertainties such as operating condition, model inaccuracy, and sensory noise in various industrial applications. This paper proposes the R...

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Bibliographic Details
Published in:Applied sciences 2020-12, Vol.10 (24), p.8977
Main Authors: Park, Pangun, Jung, Mingyu, Di Marco, Piergiuseppe
Format: Article
Language:English
Subjects:
Algorithms
Bayesian analysis
Bearings
Collaboration
Failure
fault prognosis
Industrial applications
Kalman filters
Machine learning
Methods
Neural networks
optimization
Parameter estimation
Partial differential equations
Principal components analysis
remaining useful life
ridge regression
Sliding
Statistical analysis
Support vector machines
Time series
Useful life
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Summary:Predicting the remaining useful life (RUL) of mechanical bearings is a challenging industrial task since RUL can differ even for the same equipment due to many uncertainties such as operating condition, model inaccuracy, and sensory noise in various industrial applications. This paper proposes the RUL prediction method combining analytical model-based and data-driven approaches to forecast when a failure will occur based on the time series data of bearings. Feature importance ranking and principal component analysis construct a reliable and predictable health indicator from various statistical time, frequency, and time–frequency domain features of the observed signal. The adaptive sliding window method then optimizes the parameters of the degradation model based on the ridge regression of the time series sequence with the sliding window. The proposed adaptive scheme provides significant performance improvement in terms of the RUL estimation accuracy and robustness against the possible errors of the degradation model compared to the traditional Bayesian approaches.
ISSN:2076-3417
2076-3417
DOI:10.3390/app10248977