Construction method and application of industrial process fault diagnosis model

A fault diagnosis model and industrial process technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as difficulty in collecting fault samples and low training efficiency of fault diagnosis models.

Inactive Publication Date: 2019-11-15
HUAZHONG UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The present invention provides a method for constructing an industrial process fault diagnosis model and its application, which is used to solve the problem that the existing fault diagnosis model needs to carry out deep learning through fault samples. However, it is difficult to collect fault samples in industrial time series data, which leads to fault diagnosis model training. inefficient technical issues

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  • Construction method and application of industrial process fault diagnosis model
  • Construction method and application of industrial process fault diagnosis model
  • Construction method and application of industrial process fault diagnosis model

Examples

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Effect test

Embodiment 1

[0052] A construction method 100 of an industrial process fault diagnosis model, such as figure 1 shown, including:

[0053] Step 110, constructing a fault diagnosis framework, which includes: a feature extractor, a generator in a generative adversarial network, and a fault score calculator; the generator is used to encode each original sample generated by the feature extractor to obtain the first hidden feature , decode and restore the first hidden feature to obtain a generated sample, and encode the generated sample to obtain a second hidden feature;

[0054] Step 120, using the normal original sample set and the discriminator in the generative adversarial network, and using the discriminator to identify the generated sample corresponding to the original sample as an original sample based on each original sample, train the generator; wherein, the fault score The calculator is used to perform fault judgment based on each original sample to be tested and its corresponding fir...

Embodiment 2

[0083] A method for diagnosing industrial process faults, using the industrial process fault detector constructed by any construction method of the industrial process fault diagnosing model described in the first embodiment above, and performing fault diagnosis on the original sample to be tested.

[0084] Because the training of the above-mentioned fault diagnosis model only uses normal original samples, it avoids the problem that the fault diagnosis model is difficult to train and affects the efficient fault diagnosis due to too few fault samples in the industrial time series data and the difficulty in training the fault diagnosis model.

[0085] The relevant technical solutions are the same as those in Embodiment 1, and will not be repeated here.

[0086] Preferably, the fault diagnosis is carried out on the original sample to be tested, specifically: the industrial process fault detector constructed by using any one of the construction methods of the industrial process faul...

Embodiment 3

[0107] A storage medium, in which instructions are stored, and when the computer reads the instructions, the computer is made to execute any method for constructing an industrial process fault diagnosis model as described in the first embodiment above and / or the above embodiment Any one of the industrial process fault diagnosis methods mentioned in II.

[0108] The relevant technical solutions are the same as those in Embodiment 1, and will not be repeated here.

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Abstract

The invention relates to a construction method and application of an industrial process fault diagnosis model, and the method comprises the steps: constructing a fault diagnosis framework which comprises a generator which carries out the coding-decoding-coding of each original sample generated by a feature extractor, and obtains a first hidden feature, a generated sample and a second hidden feature; training a generator by adopting the normal original sample set and a discriminator in the generative adversarial network and taking the discriminator to discriminate the generated sample as an original sample as a target; and using the fault score calculator is for generating a sample and a second hidden feature to perform fault diagnosis based on each original sample to be detected and the corresponding first hidden feature. The generator in the generative adversarial network is introduced into the fault diagnosis model. The generator has a coding-decoding-coding function, the discriminator in the generative adversarial network is adopted to train the generator only based on the normal original sample, and the problems that the fault diagnosis model is difficult to train, low in efficiency and poor in effect due to the fact that industrial fault samples are too few are solved.

Description

technical field [0001] The invention belongs to the field of industrial big data fault diagnosis, and more specifically relates to a method for constructing an industrial process fault diagnosis model and its application. Background technique [0002] In industrial systems, using information obtained from sensors and other monitoring information about equipment status, through fault diagnosis algorithms, it is possible to determine whether the equipment is damaged or not, and to predict whether the equipment will be damaged. Therefore, timely and effective fault diagnosis can ensure the normal and orderly progress of industrial production and reduce the profit loss caused by equipment damage and downtime. Considering the characteristics of data in industrial processes, there are usually two ways for fault diagnosis: 1) Based on the analysis of fault mechanism, this requires technicians to be familiar with the structure, vibration mode, and fault performance characteristics o...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F2218/14G06F2218/08G06F18/24133
Inventor 袁烨姜文倩尹航程骋周倍同洪杨马贵君
Owner HUAZHONG UNIV OF SCI & TECH
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