Bearing fault diagnosis method based on hierarchical extreme learning machine

An extreme learning machine and fault diagnosis technology, which is applied in the testing of mechanical components, testing of machine/structural components, measuring devices, etc., can solve problems such as unstable learning effects, and achieve improved recognition accuracy and feature information utilization rate, high The effect of recognition accuracy and fast training speed

Inactive Publication Date: 2020-03-24
BEIJING JIAOTONG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, it also has disadvantages such as unstable learning effects. For its improved composite extreme learning (ML-ELM), deep extreme learning machine (AE_ELM), etc., each sacrificed in accuracy and speed.

Method used

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  • Bearing fault diagnosis method based on hierarchical extreme learning machine
  • Bearing fault diagnosis method based on hierarchical extreme learning machine
  • Bearing fault diagnosis method based on hierarchical extreme learning machine

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Embodiment 1

[0041] Such as figure 1 As shown, the embodiment of the present invention provides a bearing fault diagnosis method based on hierarchical extreme learning machine. First, the vibration signal of the bearing is decomposed into multiple modes through VMD to achieve the purpose of noise reduction and feature extraction; The SVD algorithm performs secondary feature extraction and data compression on the data of noise reduction and feature extraction; finally, the purpose of fault diagnosis is achieved by learning through the new layered extreme learning machine technology. Specifically include the following steps:

[0042] Step 1. Firstly, the vibration signal data is decomposed by using the VMD (Variational Mode Decomposition) algorithm, and decomposed into K modes. The number of sampling points for each mode is consistent with the original vibration signal. After VMD decomposition, the irregular noise is removed and converged around their respective center frequencies, which ha...

Embodiment 2

[0070] Embodiment 2 of the present invention provides a method of combining VMD-SVD for feature extraction, and finally using a layered extreme learning machine to implement a rolling bearing fault diagnosis method.

[0071] Specific steps are as follows:

[0072] Step 1. The bearing vibration signal is decomposed into multiple modes converging on the center frequency through VMD.

[0073] Step 2: Select the first four items of the decomposed mode and perform SVD decomposition respectively.

[0074] Step 3: Input the features extracted twice into the layered extreme learning machine for training to realize fault diagnosis.

[0075] Hierarchical extreme learning machine is a multi-layer nested improved algorithm of extreme learning machine. In the extreme learning machine algorithm, after specifying the input and output data and the classification type of data (zero regression or complex classification), it can adjust the neural network by itself. weights and thresholds, so t...

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Abstract

The invention provides a bearing fault diagnosis method based on a hierarchical extreme learning machine, and belongs to the technical field of mechanical part fault diagnosis, and the method comprises the steps: decomposing a vibration acceleration signal into a plurality of modal components through a VMD algorithm; selecting the first four modals sorted according to the size of the center frequency, carrying out feature extraction through an SVD algorithm, mapping input feature data into a random sparse hidden layer space, obtaining hidden information between training samples, and carrying out random mapping again on the feature data processed by the previous layer by each hidden layer through a sparse automatic encoder; and obtaining an optimal neural network weight through a fast iterative shrinkage algorithm (FISTA), so that the actual output is close to the specified label data. According to the method, noise reduction and accurate classification are realized at the same time, and the recognition accuracy and the feature information utilization rate can be improved under the condition of a hierarchical extreme learning machine; compared with an original extreme learning machine, higher recognition precision and higher training speed can be achieved in fault diagnosis of rolling bearing signals.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis of mechanical parts, in particular to a fault diagnosis method based on a layered extreme learning machine. Background technique [0002] Bearing fault diagnosis is an effective technology to ensure the stable operation of rotating machinery. Therefore, rapid identification of fault states plays an important role in social production and life. However, in the existing bearing fault diagnosis algorithms, accuracy and speed have always been two research issues that are difficult to balance. How to find an algorithm that can ensure both recognition accuracy and recognition speed is of great significance in real life. However, traditional methods such as BP neural network and legacy algorithm only guarantee the accuracy in actual use, but sacrifice a lot of computing time. The extreme learning machine (ELM) is an algorithm that has an extremely obvious advantage in speed compared with other ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/045
CPCG01M13/045
Inventor 贾利民左亚昆王志鹏王宁秦勇陈欣安
Owner BEIJING JIAOTONG UNIV
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