SOM network clustering electromechanical equipment bearing fault analysis method based on transfer learning and manifold distance

A fault analysis method and transfer learning technology, applied in the field of fault diagnosis of electromechanical equipment, can solve problems such as long unplanned downtime, complex link installation, and poor real-time performance

Pending Publication Date: 2021-10-12
ANHUI UNIVERSITY
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Problems solved by technology

Poor real-time performance, long unplanned downtime, missi...

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  • SOM network clustering electromechanical equipment bearing fault analysis method based on transfer learning and manifold distance
  • SOM network clustering electromechanical equipment bearing fault analysis method based on transfer learning and manifold distance
  • SOM network clustering electromechanical equipment bearing fault analysis method based on transfer learning and manifold distance

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Embodiment Construction

[0046] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the examples of the present invention. Obviously, the described embodiments are some, not all, embodiments of the present invention.

[0047] refer to figure 1 , is a flow chart showing the steps of a SOM network clustering electromechanical equipment bearing fault analysis method based on transfer learning and manifold distance according to an embodiment of the present invention. The implementation process can be divided into three steps:

[0048] Step 1, the original acquisition signal is denoised by CEEDAN and FastICA technology to form a reconstructed original signal, extract the feature vector, and use it as the input of the SOM adaptive neural network;

[0049] In step one, include the following steps:

[0050] 1. Since the vibration signal has obvious nonlinearity, and time-domain analysis is difficul...

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Abstract

The invention relates to the technical field of transfer learning and adaptive neural network clustering bearing fault analysis, and discloses an SOM network clustering electromechanical equipment bearing fault analysis method based on transfer learning and manifold distance. The specific process is as follows: original acquisition signals are de-noised by CEEDAN and FastICA technologies to form reconstructed original signals, and the reconstructed original signals are used as the input of the SOM adaptive neural network; in the SOM network, a manifold distance is considered and utilized to calculate a similarity matrix, a transfer learning model mechanism is introduced, weight parameters of SOM neural network nodes of a source domain and a target domain of a data sample are shared, and a target domain clustering task function and neuron weights are optimized through a source domain training result; and a clustering task of the target domain sample is finished through an SOM neural network target domain output layer result, thus obtaining a clustering result, and outputting a mechanical fault diagnosis type.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis of electromechanical equipment, and in particular relates to a method for diagnosing faults of mine mechanical bearings based on SOM adaptive neural network clustering based on transfer learning and manifold distance calculation. Background technique [0002] With the rapid development of my country's modern industry, various industries are pursuing the high efficiency and stability of the industrial site. Under the guidance of the national development strategy, they are developing in the direction of intelligence, automation and informatization. As the demand for the quantity and quality of raw materials in various industries increases year by year, this makes the mining industry a grassroots pillar industry of the entire industry. As an upstream industry in the industrial chain, the mining industry must ensure the safety of its personnel and the safety and stability of on-site equipment....

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Application Information

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IPC IPC(8): G06F30/17G06F30/27G06F119/02G06F119/04
CPCG06F30/17G06F30/27G06F2119/02G06F2119/04
Inventor 徐岳杨富超
Owner ANHUI UNIVERSITY
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