Bearing fault detecting and locating method and detecting and locating model implementation system and method

Abstract

The invention provides a bearing fault detecting and locating method and a detecting and locating model implementation system and method. Data preprocessing is performed on the no-tag classification data of a rolling bearing and then the data are inputted to a trained feature learning and detection model so that the fast detecting and locating problem of the rolling bearing under multiple fault modes can be solved, and statistics of the probability of each type of classification result is performed through voting by the minimization loss function; and the certain fault feature of the most votes is determined as the currently estimated fault mode and the fault part is located. The whole feature learning process does not require any manual feature extraction process, the original data act asthe input of the feature learning algorithm, the unsupervised feature learning process is used in the learning process, and the extracted bearing fault features can be efficiently self-expressed through deep data expansion and projection so that the problem of acquisition difficulty of the tag data can be solved, and the method has the characteristic of high detecting and locating accuracy.

Description

technical field [0001] The invention relates to a fault detection and positioning method and a detection and positioning model realization system and method suitable for bearings, in particular to a fault detection and positioning method and a detection and positioning model realization system and method suitable for rolling bearing detection and positioning. Background technique [0002] In recent years, with the trend of large-scale, complex, continuous, automated and centralized development of mechanical equipment, the structure and composition of equipment have become more and more complex, and the connections between subsystems have become closer and closer. The common point of operation is that it is impossible to completely rely on traditional methods to establish accurate physical models for monitoring, and due to the influence of nonlinear factors (such as damping, stiffness, friction gap, external load, etc.) The non-stationary data of the state and operating mecha...

AI Technical Summary

Problems solved by technology

[0008] A technical problem to be solved by the present invention is to provide a system for automatically extracting the characteristics of bearing working condition data for fault detection and positioning, which overcomes the shortcomings of manual design features and feature expression in existing bearing fault diagnosis, and the difficulty in obtaining label data. and method

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