Mechanical equipment fault type diagnosis method

A technology of mechanical equipment and fault types, applied in the field of artificial intelligence, can solve the problems of small sample size and simplification of fault data, and achieve the effect of improving versatility and accuracy and expanding spatial distribution

Active Publication Date: 2021-08-06
中国人民解放军92578部队 +1
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Problems solved by technology

To solve the problem of small sample size and simplification of fault data, an

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  • Mechanical equipment fault type diagnosis method
  • Mechanical equipment fault type diagnosis method
  • Mechanical equipment fault type diagnosis method

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[0012] The diagnostic method of the mechanical equipment fault specification proposed by the present invention, including:

[0013] Collecting different models and operating mechanical equipment fault data sets, and preprocesses the data set; establishing an initial network model, multiple generators pass shared local weight connections, each generator corresponds to a mechanical equipment in a data set , Against learning characteristics; confrontational training for the model until the Nash equilibrium state; the weight of the generator is unchanged, the training discriminator is discriminated until the discriminator obtains a valid mechanical equipment fault species discriminant.

[0014] One embodiment of the method of the present invention is described below with reference to the drawings:

[0015] (1) Obtain four data sets for fault diagnosis from four public data, including Cathfall University Bearing Data Center Bearing Data Set (CWRU-BD), US Mechanical Fault Prevention Tec...

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Abstract

The invention belongs to the technical field of artificial intelligence, and particularly relates to a mechanical equipment fault type diagnosis method. According to the method, mode collapse is relieved and a fault data space is expanded by learning effective fault features, so that diagnosis of mechanical fault types is carried out. The set of weight sharing generators is designed to generate fault data of the same type. Universal features of the same fault can pass through a local sharing layer. Through discriminator training based on generated data and real data, the discriminator can obtain fault diagnosis capability. According to the method, a fault data generation network of multiple generators is constructed, so that the problem of'mode collapse 'easily occurring in a current method for generating fault data based on a single-generator neural network is solved, and the problem of a single mode of generating fault data is solved; and, through a local weight sharing mechanism of the group generator, basic fault features of data of the same fault type are effectively learned, so that spatial distribution of fault data is effectively expanded, and universality and accuracy of fault classification are improved.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence, and in particular relates to a method for diagnosing mechanical equipment faults. Background technique [0002] In modern industrial society, mechanical equipment is an indispensable and common component, which plays a vital role in industrial production based on mechanical equipment. The complex working environment and irregular operation process affect the safety of industrial equipment and cause abnormalities in mechanical equipment. Moreover, the effective maintenance of mechanical equipment is the basic requirement for maintaining normal operation. Preventing equipment failures can reduce property losses and avoid serious accidents. Therefore, it is necessary to prevent and discover mechanical equipment failures, correctly identify the types of failures, and provide corresponding solutions for mechanical failures. [0003] With more and more data accumulated by mechanical e...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/047G06N3/048G06N3/045G06F18/24
Inventor 高晟耀郭庆稳宋艳李沂滨高辉
Owner 中国人民解放军92578部队
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