Rolling bearing fault probabilistic intelligent diagnosis method based on adaptive MRVM

Abstract

The invention discloses a rolling bearing probabilistic intelligent fault diagnosis method based on adaptive MRVM. The method comprises the steps that the original fault data of a rolling bearing are measured through an acceleration sensor; a vibration signal is segmented, and wavelet packet energy characteristics are extracted; principal component analysis and dimension reduction are used for normalization simultaneously; a training sample set and a test sample set are processed and divided; an algorithm is used to adaptively select nuclear parameters; the training sample set is used to train and test a multi-class correlation vector machine; and the test result is compared with the actual fault type to acquire the validity of a diagnosis model. According to the invention, the method overcomes the defect that a traditional intelligent fault diagnosis method cannot output the fault probability value; the fault diagnosis accuracy of the rolling bearing is improved; more fault type determining information of the rolling bearing can be provided; through the fault type probability value provided by the invention, the state of the rolling bearing can be further assessed; and method has the advantages of good engineering value and application prospect.

Description

technical field [0001] The invention belongs to the field of intelligent fault diagnosis of rolling bearings, in particular to an intelligent fault diagnosis method for rolling bearings based on an adaptive multi-classification correlation vector machine model (MRVM). Background technique [0002] Rolling bearings are an essential and important component of rotating machinery and equipment. Once a problem occurs in a rolling bearing, it will cause economic losses in the slightest and endanger life in the worst case. Therefore, knowing the real-time working status of rolling bearings is of great significance for monitoring whether large-scale mechanical equipment is running normally. [0003] Intelligent fault diagnosis is one of the important technologies for rolling bearing fault diagnosis. Fault identification is mainly performed through fault feature extraction combined with a fault recognizer, which essentially belongs to the category of pattern recognition. The quality o...

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Problems solved by technology

However, in the selection of nuclear parameters, the nuclear parameters need to be set in advance, and there is a lot of uncertainty

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