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

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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Problems solved by technology

Poor real-time performance, long unplanned downtime, missing the best maintenance time, complicated link installation, etc.

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