Incipient fault prediction based on warning control limit self-learning for the rolling bearing

Traditional rolling bearing fault warning usually adopts fixed threshold grading alarm, which exist many false alarms and missed alarms. The keys to solve this problem are how to effectively learn the indicators that can represent the health state of the equipment from vibration signals and self-learn the warning control limits. A method of bearing early fault prediction based on warning control limit self-learning was proposed. The Short-time Fourier Transform (STFT) was used to extract fault features of vibration data. A health indicator construction method based on Matrix Variate Gaussian Convolutional Deep Belief Network (MVGCDBN) was proposed, which could combine the fault features into high-level features without destroying the internal structure of two-dimensional sample space, and construct the health indicator through the full connection layer. The probability distribution of health indicators under normal operating conditions was fitted and goodness of fit was tested, and the upper quantile was used as the fault warning control limit. The effectiveness of the proposed method was verified with the international standard bearing data set. © 2024 CIMS. All rights reserved.