Rolling bearing fault diagnosis method under variable-load based on unsupervised characteristic alignment

Abstract

The invention discloses a rolling bearing fault diagnosis method under variable load based on unsupervised characteristic alignment, and belongs to the domain of the rolling bearing fault diagnosis. For the problems that source domain data and target domain data belong to different distributions and a target domain sample does not contain a label science a certain load data is absent in the actualwork of the rolling bearing, the method comprises the following steps: acquiring time frequency characteristics of a vibration signal by combining variation modal decomposition with singular value decomposition, and constructing a multi-domain characteristic set by combining the vibration signal time domain and frequency domain characteristics; importing a sub-space alignment algorithm capable ofrealizing unsupervised domain adaption in the transfer learning, and performing improvement, and combining a kernel mapping method with a SA algorithm. The training data and the testing data are mapped to the same high-dimensional space, the state corresponding to other load data is identified by utilizing the known load data of the rolling bearing under the condition that the target domain lackslabel, and the method has high fault diagnosis accuracy rate.

Description

technical field [0001] The invention relates to a rolling bearing fault diagnosis method under variable load, belonging to the field of rolling bearing fault diagnosis. Background technique [0002] Rolling bearings are key components of rotating machinery and are widely used in industrial production. Fault diagnosis of rolling bearings will effectively ensure the normal and smooth operation of equipment and prevent major accidents [1]. Rolling bearings often work under variable load conditions, resulting in the lack of or inability to obtain training data with the same distribution as the data to be tested in actual work [2]. Fault diagnosis of unknown tag vibration signals under other loads based on known tag vibration signals has important practical significance [3]. [0003] Machine intelligent fault diagnosis mainly includes feature extraction, fault diagnosis and prediction [4]. The time-frequency feature extraction methods of rolling bearing vibration signals have b...

AI Technical Summary

Problems solved by technology

[0007] In view of the lack of some load data in the actual work of rolling bearings, which makes the data in the source domain and the data in the target domain belong to different distributio

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