Hidden Markov model and nuisance attribute projection based bearing performance degradation assessment. (May 2016) - Jiang, Huiming; Chen, Jin; Dong, Guangming
Abstract: Hidden Markov model (HMM) has been widely applied in bearing performance degradation assessment. As a machine learning-based model, its accuracy, subsequently, is dependent on the sensitivity of the features used to estimate the degradation performance of bearings. It׳s a big challenge to extract effective features which are not influenced by other qualities or attributes uncorrelated with the bearing degradation condition. In this paper, a bearing performance degradation assessment method based on HMM and nuisance attribute projection (NAP) is proposed. NAP can filter out the effect of nuisance attributes in feature space through projection. The new feature space projected by NAP is more sensitive to bearing health changes and barely influenced by other interferences occurring in operation condition. To verify the effectiveness of the proposed method, two different experimental databases are utilized. The results show that the combination of HMM and NAP can effectively improve the accuracy and robustness of the bearing performance degradation assessment system. Highlights: Nuisance attribute projection (NAP) is firstly introduced to the bearing performance degradation assessment to mitigate the influence of problems irrelevant to the degradation state introduced by operation conditions. The bearing performance degradation assessment method based on continuous hidden Markov model (HMM) and nuisance attribute projection (NAP) is proposed. The proposed method is very effective in removing the influence introduced by sensors' locations, the operation condition and improves the robustness in the bearing performance degradation assessment, which is verified through different whole lifetime experiments.