A Diagnosis Method of Mechanical Equipment Fault Types

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: 2022-03-25
中国人民解放军92578部队 +1
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AI Technical Summary

Problems solved by technology

To solve the problem of small sample size and simplification of fault data, and improve the versatility and scalability of the machine fault diagnosis model

Method used

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  • A Diagnosis Method of Mechanical Equipment Fault Types
  • A Diagnosis Method of Mechanical Equipment Fault Types
  • A Diagnosis Method of Mechanical Equipment Fault Types

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Experimental program
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Embodiment Construction

[0012] The diagnostic method of mechanical equipment fault type that the present invention proposes, comprises:

[0013] Collect mechanical equipment failure data sets of different models and working conditions, and preprocess the data sets; establish an initial network model, multiple generators are connected by sharing local weights, and each generator corresponds to the health status of mechanical equipment in a data set , confrontation learning features; the model is trained adversarially until it reaches the Nash equilibrium state; the weight of the generator remains unchanged, and the discriminator is trained until the discriminator obtains an effective ability to distinguish the types of mechanical equipment failures.

[0014] Below in conjunction with accompanying drawing, introduce an embodiment of the inventive method:

[0015] (1) Obtain four data sets for fault diagnosis from four public data sets, including Case Western Reserve University Bearing Data Center Beari...

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Abstract

The invention belongs to the technical field of artificial intelligence, and in particular relates to a method for diagnosing mechanical equipment faults. This method mitigates mode collapse and expands the fault data space by learning effective fault features for the diagnosis of mechanical fault types. The set of weight-sharing generators is designed to generate the same type of fault data. Common features of the same fault can be shared across layers locally. By training the discriminator based on generated data and real data, the discriminator can acquire fault diagnosis capabilities. By constructing a multi-generator fault data generation network, this method solves the problem of "mode collapse" that is prone to occur in the current method of generating fault data based on a single-generator neural network, and solves the single-mode problem of generating fault data; The shared mechanism can effectively learn the basic fault characteristics of the same fault type data, thereby effectively expanding the spatial distribution of fault data and improving the versatility and accuracy of fault classification.

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...

Claims

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Application Information

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Patent Type & Authority Patents(China)
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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