Bearing fault diagnosis method combining improved sparse filter and KELM

Abstract

The invention discloses a bearing fault diagnosis method combining an improved sparse filter and a KELM. The method comprises the following steps: embedding a Min-Max regular term into an original sparse filter to obtain an improved sparse filter. The Min-Max regular term can describe the internal structure information of the original data, and promotes the similar samples to be close to each other and promotes the samples of different classes to be separated from each other, thereby generating discriminative characteristics. Feature discrimination mainly lies in that class label information is used in construction of the Min-Max regular term, and a pseudo label is used for replacing a real label to guide the construction of the Min-Max regular term. Vibration signals of different operation conditions of a rolling bearing are collected to serve as a training set, the training set is used for training an improved sparse filter model and a kernel extreme learning machine model to obtainmodel parameters, and therefore establishment of a fault diagnosis classification model is completed, and the diagnosis model can accurately identify the rolling bearing fault.

Description

technical field [0001] The invention belongs to a fault diagnosis method, in particular to a bearing fault diagnosis method combined with an improved sparse filter and KELM. Background technique [0002] Rolling bearings are important components of rotating machinery and electrical equipment, known as "the joints of industry", and their operating status is directly related to the production efficiency and safety of equipment. In some large-scale industrial equipment such as aeroengines and gas turbines, rolling bearings often work in harsh environments such as high temperature, high speed and heavy load, which will inevitably cause some failures of rolling bearings, and the monitoring and diagnosis of rolling bearing operating status, timely and accurate diagnosis The failure of rolling bearings is particularly critical. Since the vibration signal of the rolling bearing is rich in information reflecting the operating state when the rolling bearing is running, in recent year...

AI Technical Summary

Problems solved by technology

However, the disadvantage of the original SF is that it ignores the internal structure of the input data, which may cause it to fail to learn highly discriminative features, and thus make the KELM classifier unable to obtain more accurate classification results.

Images