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Fault diagnosis method for industrial motor bearings based on multi-local model decision fusion

A decision fusion, industrial motor technology, applied in the testing of mechanical components, testing of machine/structural components, instruments, etc., can solve problems such as difficult to effectively learn complex representative features and nonlinear relationships, to avoid influence, accurate The effect of fault diagnosis

Active Publication Date: 2022-04-22
HOHAI UNIV CHANGZHOU
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  • Claims
  • Application Information

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Problems solved by technology

Most traditional intelligence methods belong to shallow learning models, which are difficult to effectively learn complex representative features and nonlinear relationships

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  • Fault diagnosis method for industrial motor bearings based on multi-local model decision fusion
  • Fault diagnosis method for industrial motor bearings based on multi-local model decision fusion
  • Fault diagnosis method for industrial motor bearings based on multi-local model decision fusion

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Embodiment Construction

[0036] In order to enable those skilled in the art to better understand the technical solutions in the application, the technical solutions in the embodiments of the application are clearly and completely described below. Obviously, the described embodiments are only part of the embodiments of the application, and Not all examples. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.

[0037] A method for diagnosing industrial motor bearing faults based on multi-local model decision fusion, comprising the following steps:

[0038] S1 data collection: use two different types of sensors to collect data related to motor bearings;

[0039] S2 data processing: perform preprocessing and feature extraction on two kinds of data collected by two different types of sensors respectively;

[0040] S3 establishes a multi-local diagnostic ...

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Abstract

The invention discloses an industrial motor bearing fault diagnosis method based on multi-local model decision fusion. Types of sensors to collect data related to motor bearings; data processing, preprocessing and feature extraction for the two types of data collected; building a multi-local diagnostic model, using Bi‑LSTM neural network-based neural network to build local Diagnosis model; decision-level fusion, based on DSmT theory, the local diagnosis results output by two local diagnosis models are fused at decision-level and the final diagnosis result is obtained. The industrial motor bearing fault diagnosis method designed by the invention can better help the factory to timely and accurately find the motor fault caused by the bearing damage, and avoid the influence on the motor operation and the factory production efficiency due to the shutdown to a certain extent.

Description

technical field [0001] The invention relates to an industrial motor bearing fault diagnosis method based on multi-local model decision fusion, belonging to the field of motor fault detection. Background technique [0002] In today's society, industrial motors generally provide power for the operation of electrical appliances or various machinery. Among them, the motor bearing failure is an urgent problem to be solved in the motor prone to failure. According to statistics, 40-70% of electromechanical transmission system and motor failures are caused by rolling bearing damage, which may cause a great impact on the operation of the motor due to downtime, which in turn will affect the production efficiency of the factory and cause a certain degree of damage The economic impact, what's more, cause large-scale accidents, so that the personal safety of operators is threatened. Therefore, it is necessary to check the various unstable or fault states of the motor bearings, predict ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01M13/045
CPCG01M13/045
Inventor 刘立张子贤孙宁韩光洁
Owner HOHAI UNIV CHANGZHOU
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