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Bearing fault diagnosis method based on knn-adaboost

A fault diagnosis and bearing technology, which is applied in the testing of mechanical components, testing of machine/structural components, instruments, etc., can solve problems such as accurate diagnosis of bearing faults

Inactive Publication Date: 2021-03-05
XI'AN POLYTECHNIC UNIVERSITY
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Problems solved by technology

However, there is currently no method for accurately diagnosing bearing faults.

Method used

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  • Bearing fault diagnosis method based on knn-adaboost
  • Bearing fault diagnosis method based on knn-adaboost
  • Bearing fault diagnosis method based on knn-adaboost

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Embodiment Construction

[0075] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0076] The bearing fault diagnosis method based on KNN-AdaBoost is carried out according to the following steps:

[0077] Step 1, obtain the multi-segment vibration signals of the shearer bearing in different states, and use all the vibration signals in each segment of the vibration signal as a set of vibration signals;

[0078] Step 2, each vibration signal in each group of vibration signals is preprocessed into a modal component for determining the center frequency and bandwidth, and multiple groups of modal components for determining the center frequency and bandwidth are obtained;

[0079] In step 2, preprocess each vibration signal into a modal component that determines the center frequency and bandwidth according to the following steps:

[0080] Step 2.1, establish a constrained variational model for each vibration signal, specifically...

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Abstract

The invention discloses a bearing fault diagnosis method based on KNN‑AdaBoost. Specifically, according to the variational mode decomposition algorithm, all vibration signals are preprocessed into modal components to determine the center frequency and bandwidth, and the modal components in each group of vibration signals are respectively selected. The modal component with the largest kurtosis is calculated to obtain multiple sets of bearing fault feature vectors; multiple KNN classifiers are obtained through training; AdaBoost strong classifiers are obtained according to multiple sets of bearing fault feature vectors and multiple KNN classifiers; use AdaBoost strong classifiers Classify the collected bearing vibration signals step by step. The bearing fault diagnosis method based on KNN-AdaBoost of the present invention can classify faults based on a large amount of real-time bearing monitoring data, and is helpful for quickly identifying fault types of bearings.

Description

technical field [0001] The technical field of coal shearer fault detection of the present invention relates to a bearing fault diagnosis method based on KNN-AdaBoost. Background technique [0002] The shearer is a large complex system integrating machinery, electricity and hydraulic pressure. With the development of the coal industry, the shearer has more and more functions and its own structure is becoming more and more complex. As a standardized transmission component of a shearer, the operating state of the bearing increasingly affects the working efficiency and life of the shearer. Relevant statistics show that about 30% of all failures of coal shearers are caused by bearings. For a long time, mechanical equipment maintenance personnel have used regular spot checks and maintenance to discover and promptly eliminate bearing faults, but they are still caught off guard by sudden accidents, which are likely to cause serious losses, and regular spot checks and maintenance wi...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01M13/045G06F30/20G06K9/00
CPCG01M13/045G06F30/20G06F2218/12
Inventor 宋玉琴邓思成师少达
Owner XI'AN POLYTECHNIC UNIVERSITY
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