Abstract
A good health index (HI) plays an important role in improving the reliability and accuracy of the prediction of remaining useful life (RUL) of rolling bearings. In order to better integrate degradation information contained in high-dimensional features, construct HIs with a good trendability and obtain satisfactory RUL prediction effect, this paper proposes a HI construction method based on spectral clustering and trendability enhancement strategy. Firstly, 28-dimensional time–frequency features are extracted. Secondly, the features are clustered based on the spectral clustering method. Thirdly, the sensitive feature set of degradation process is constructed based on the trendability optimization. Finally, based on the calculation of trendability indicators of sensitive feature set, HIs based on the trendability enhancement strategy are calculated, and its performance can be verified through RUL prediction based on Support Vector Regression (SVR). The vibration data set of XJTU-SY bearing accelerated degradation test was used to evaluate the proposed method. The experimental results showed that the HIs constructed by this method have good accuracy in RUL prediction, and have better trendability and predictability compared with Root Mean Square (RMS) indicators and HIs obtained based on traditional k-means clustering.
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This research was financially supported by National Natural Science Foundation of China (No. 52005335), Shanghai Sailing Program (No. 18YF1417800).
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Jiang, H., Luo, J., Shao, Y., Ma, Q., Pan, H. (2021). A New Health Indicator Construction Approach and Its Application in Remaining Useful Life Prediction of Bearings. In: Zhang, H., Yang, Z., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2021. Communications in Computer and Information Science, vol 1449. Springer, Singapore. https://doi.org/10.1007/978-981-16-5188-5_21
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