Abstract:

：Entire life cycle degradation monitoring of rolling bearing is an important part of equipment initiative maintenance technology. Assessing the damage state effectively can realize near-zero downtime for equipment and exert its maximum productivity. In order to depict rolling bearing degradation trends effectively, the fuzzy C-means Algorithm (FCM) based on manifold learning is proposed. First of all, the time domain features, frequency domain features and wavelet packet time-frequency domain characteristics extracted from monitoring signals are used to constitute high-dimensional feature set. After then, the low-dimensional manifold features of the high-dimensional feature set are extracted according to the certain intrinsic dimension. In this sense, the FCM model based on locally linear embedding (LLE) manifold learning is built to evaluate current operating status of the rolling bearing. Finally, entire life cycle experiment of IMS rolling bearing is used to evaluate the efficiency of the proposed method for describing performance degradation stage of the rolling bearing.

Key words: fuzzy C-means, performance degradation, rolling bearing, LLE manifold learning