Bearing fault detection and diagnosis based on active learning

Abstract

A bearing fault detection method based on active learning is disclosed. A vibration method is used to carry out limited time data acquisition on a bearing operation condition in each second. A peak factor, a pulse factor, a kurtosis factor, a pulse factor and a waveform factor of a bearing vibration signal are extracted in a time domain and are taken as characteristic factors for representing a bearing operation state so as to generate a data table. Each bearing is taken as a point and a maximum distance M between the two points is calculated, each one is taken as a center, 0.1M is a radius, and the point in a circle is taken as a density point, and a density rho is determined. The density is compared through a traversing method, and the distance with the closest point which is greater than the density is delta. According to a main-dominant relation acquired from the delta, a spanning tree is drawn. The first N points with the largest rho*delta value are selected as classification center points so as to carry out classification, and a training set is used to represent a classification label. Label verification is performed, and after the data corresponds to the label, a fault typecan be determined. Classification is accurate, and a bearing fault can be effectively detected and diagnosed.

Description

technical field [0001] The invention relates to the field of fault diagnosis, in particular to a method for fault detection of rolling bearings. Background technique [0002] Investigations and studies have shown that about 30% of machine equipment failures are caused by rolling bearing failures. Failure of rolling bearings will cause severe vibration and noise to the machine, and the machine will stop working if it is light, and it will cause catastrophic accidents such as plane crashes and train derailments. The vibration method has the advantages of simple signal acquisition and processing, good effect, and wide application range, so it is widely used. [0003] P.Konar and others used the SVM support vector algorithm to identify faults on the fault set of Western Reserve University in the United States; He Xiaoxia et al. used continuous wavelet analysis to process the vibration acceleration signal of the rolling bearing, and extracted the normal, inner ring peeling, and ...

AI Technical Summary

Problems solved by technology

Therefore, in actual situations, the recognition rate of traditional methods is extremely low.

Images