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A Missing Data Bearing Fault Diagnosis Method Based on Improved BP Neural Network Estimation

A BP neural network and missing data technology, applied in the field of missing data bearing fault diagnosis, which can solve the problems of missing data set health evaluation method, accuracy limitation, and inability to cluster incomplete data.

Active Publication Date: 2017-05-24
LIAONING UNIVERSITY
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AI Technical Summary

Problems solved by technology

In actual industrial production, due to the limitation of the accuracy of the acquisition equipment, the influence of noise or data omission and other reasons, the data collected by the bearing is missing and an incomplete data set is generated.
But the fuzzy C-means clustering algorithm cannot directly cluster incomplete data
Currently, there is no health evaluation method for missing bearing datasets

Method used

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  • A Missing Data Bearing Fault Diagnosis Method Based on Improved BP Neural Network Estimation
  • A Missing Data Bearing Fault Diagnosis Method Based on Improved BP Neural Network Estimation
  • A Missing Data Bearing Fault Diagnosis Method Based on Improved BP Neural Network Estimation

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Embodiment Construction

[0054] 1. BP neural network

[0055] The BP neural network is usually composed of three layers: the input layer, the hidden layer and the output layer. The layers are fully interconnected, but the nodes in each layer are not connected. like figure 1 Shown is a BP neural network model with a single hidden layer. The BP network consists of an input layer, a hidden layer and an output layer. Each circle represents a node, and each layer contains n, l, m nodes. Links between nodes are represented by arrows, and each arrow represents a weight. w ij Indicates the connection weight between the input layer and the hidden layer, w jk Indicates the connection weights of the hidden layer and the output layer. Data processing and calculation will be performed by each node of the hidden layer and output layer, and the specific number of hidden layer nodes will be determined in the experiment.

[0056] 2. Improved BP neural network

[0057] The training samples of the basic BP neura...

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Abstract

A bearing fault diagnosis method capable of recovering missing data of back propagation neural network estimation values includes the steps of 1) bearing data preprocessing; 2) training sample determining and optimizing; 3) network initializing; 4) training based on a back propagation neural network after recovering; 5) missing attribute estimating; 6) clustering analyzing of data sets. The bearing fault diagnosis method capable of recovering the missing data of the back propagation neural network estimation values has the advantages that bearing data with the missing data can be processed, integral data obtained after recovering can be subjected to clustering by the aid of a fuzzy c-means clustering algorithm, and accordingly health of a bearing can be evaluated.

Description

technical field [0001] The invention relates to a method for diagnosing bearing faults with missing data by improving BP neural network evaluation. Background technique [0002] In modern production, rolling bearings are widely used in rotating machinery, and the health of rolling bearings is one of the important influences on the operation of the entire machinery. Rolling bearings need to have high reliability, and the occurrence of bearing faults during mechanical operation may lead to fatal mechanical failures. Therefore, the evaluation technology of rolling bearing health is extremely important. [0003] In recent years, health evaluation technology has developed rapidly, and research results have emerged continuously, and various methods have been adopted. The most widely used is the fuzzy C-means clustering algorithm. In actual industrial production, due to the limitation of the accuracy of the acquisition equipment, the influence of noise or data omission and other ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/08
Inventor 张利王蓓蕾刘萌萌夏天王鹭王军孙颖
Owner LIAONING UNIVERSITY