Bearing performance degradation assessment based on topological representation and hidden Markov model

Abstract

Bearing performance degradation assessment is essential to avoid abrupt machinery breakdown. However, background noise, outliers, and other interferences in the monitoring data may restrict the accuracy and stability of bearing performance degradation assessment in practical applications. In this study, a bearing performance degradation assessment method based on the topological representation and hidden Markov model is proposed. To construct a robust and representative feature space, the topological representations, specifically, topological meshes of the original features are obtained by self-organizing map, which can represent the general structure of the original feature space and eliminate outliers and other interferences. Then, the weight vectors of topological meshes are used as degradation features. Finally, the hidden Markov model is adopted as the assessment model to evaluate the bearing performance degradation tendency and detect the initial degradation effectively. To validate the effectiveness and superiority of the proposed method, two experimental datasets are analyzed. Compared with peer methods, the performance indicator curve of the proposed method presents a more smooth and accurate degradation tendency than comparative methods. Moreover, initial degradation can be identified accurately.