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| Life prediction for rolling bearings utilizing both failure and truncated samples |
| ZHANG Yan1, TANG Bao-ping1, HAN Yan1, CHEN Tian-yi2 |
1. The State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China；
2. China Aerodynamics Research and Development Centre, Mianyang 621000, China |
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Abstract To overcome the limitations that the traditional bearing life prediction method relies on a database of failure samples and it cannot effectively utilize truncated samples, an intelligent method utilizing both failure and truncated samples was proposed for bearing life prediction. Firstly, the trend model for features characterizing bearing degradation was constructed based on the function principal component analysis (FPCA), and each feature was decomposed into a mean value, an eigenvector and a score vector of function principal components (FPC-scores). Secondly, the optimal life value of each truncated sample was estimated by minimizing the similarity index between its score vector and those of failure ones. Thirdly, all features in the whole life duration of each sample were estimated and reconstructed based on the feature trend model to generate training data. Finally, the prediction model was constructed based on a least square support vector machine for bearing life prediction. The test results of rolling bearings’ life prediction showed that the proposed method can improve the bearing life prediction accuracy with truncated samples, and it is robust to a certain level data missing.
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Received: 25 August 2016
Published: 28 November 2017
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