Abstract
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.
Highlights
Modern industrial companies must continuously maintain their production resources by using appropriate maintenance strategies to improve availability, reliability, and safety while reducing their maintenance costs [1,2,3]
The adaptive sliding window method optimizes the local model parameters of the degradation model based on the ridge regression of the time series health indicator to estimate the remaining useful life (RUL) value
We compare the performance of the proposed scheme to existing Bayesian approaches for predicting the RUL value of the experimental data of the PHM
Summary
Modern industrial companies must continuously maintain their production resources by using appropriate maintenance strategies to improve availability, reliability, and safety while reducing their maintenance costs [1,2,3]. The data-driven prognostic approach converts the monitoring and operational data into relevant information and degradation models [4,5,12] This approach utilizes various tools, including statistical method and machine learning, to build the degradation model of the mechanical systems and equipment. These fundamental drawbacks limit the adoption of DL models for safety-critical applications [16,17,18] Another major challenge is the insufficient data to apply the DL technique for the RUL prediction of bearings. We evaluate the performance of the proposed scheme compared to existing Bayesian approaches for predicting the RUL value of the experimental data of mechanical bearings.
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