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Probabilistic Intelligent Diagnosis Method of Rolling Bearing Fault Based on Adaptive MRVM

A rolling bearing and fault diagnosis technology, which is applied in the testing of mechanical components, testing of machine/structural components, instruments, etc., to achieve the effect of improving accuracy and realizing fault type diagnosis

Active Publication Date: 2019-05-28
CHUZHOU UNIV
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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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  • Probabilistic Intelligent Diagnosis Method of Rolling Bearing Fault Based on Adaptive MRVM
  • Probabilistic Intelligent Diagnosis Method of Rolling Bearing Fault Based on Adaptive MRVM
  • Probabilistic Intelligent Diagnosis Method of Rolling Bearing Fault Based on Adaptive MRVM

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Embodiment Construction

[0048] It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and examples.

[0049] Such as figure 1 As shown, the rolling bearing intelligent fault diagnosis method based on the self-adaptive multi-classification correlation vector machine model of the present invention comprises the following steps:

[0050] S10, first constructing a multi-category correlation vector machine model for rolling bearing fault diagnosis;

[0051] S20, collecting the original vibration signal of the rolling bearing to be fault diagnosed;

[0052] S30, performing segmentation processing on the original vibration signals of different fault types of the rolling bearing;

[0053] S40. Extract the wavelet packet energy feature of each segment of the rolling bearing signal, and normalize the ...

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Abstract

The invention discloses a probabilistic intelligent fault diagnosis method for rolling bearings based on self-adaptive MRVM. Dimensionality reduction and normalization processing are carried out at the same time. By processing and dividing the training sample set and the test sample set, an algorithm is used to adaptively select the kernel parameters, and the training sample set is used to train and test the multi-category correlation vector machine, and the test results are compared with the actual Fault types are compared to obtain the validity of the diagnostic model. The method of the invention overcomes the defect that the traditional intelligent fault diagnosis method cannot output the failure probability value, improves the accuracy of rolling bearing fault diagnosis, and provides more information on the identification of rolling bearing fault types. The failure type probability value provided by the invention It can further evaluate the state of rolling bearings, and has 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...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01M13/045G06K9/62
CPCG01M13/045G06F18/2411G06F18/214
Inventor 王波王志乐张青张健康熊鑫州夏剑阳肖子遥
Owner CHUZHOU UNIV
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