Electromechanical equipment bearing fault prediction method based on Bayesian network of transfer learning

Abstract

The invention discloses an electromechanical equipment bearing fault prediction method based on a Bayesian network of transfer learning. The method specifically comprises the following steps: carrying out noise removal processing on an original acquisition signal through CEEMDAN and FastICA technologies to form a reconstructed original signal; introducing migration learning, clsutering original signals by a neural network to obtain a signal set classified according to fault types, taking the signal set as a target domain data set and as input of a Bayesian network, and selecting a reference sample set from a source domain as a training set of the source domain Bayesian network; based on the covariable shift theory, using the training data of the source domain to complete the goal of minimizing the parameter loss value on the target domain, and optimizing the maximum likelihood estimation of the Bayesian network on the target domain; and returning a result by output of the Bayesian network on the target domain and is visually embodied, giving an alarm in an abnormal state, returning parameter information to mechanical equipment in a decline state, and indicating a machine to adjust self-adjusting parameters such as the rotating speed within a certain range.

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 mechanical bearings in mines based on a transfer learning-based maximum likelihood estimation Bayesian network. Background technique [0002] While my country's modern industry is pursuing high efficiency, intelligent monitoring has become an important technical force to ensure industrial stability. 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. However, the working environment of the mining industry is often relatively complicated. Most of the large-scale electromechanical equipment is used outdoors. Once a failure occurs, it will often cause unpredictable losses. . Bearings are composed of inner rings, outer rings, rolling elements, cages, and grease. Bearings used in mining machin...

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Problems solved by technology

It often causes problems such as poor real-time performance, long unplanned downtime, missing the best maintenance time, and complicated link installation

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