DSSAE-based intermittent process fault diagnosis method

A fault diagnosis and process variable technology, applied in neural learning methods, instruments, biological neural network models, etc., can solve the problems of low diagnostic accuracy and weak model robustness, and achieve high accuracy and good robustness. Effect

Inactive Publication Date: 2019-04-12
BEIJING UNIV OF TECH
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Problems solved by technology

However, they can only achieve good classification results under small samples, a...

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  • DSSAE-based intermittent process fault diagnosis method
  • DSSAE-based intermittent process fault diagnosis method
  • DSSAE-based intermittent process fault diagnosis method

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Embodiment Construction

[0051] PenSim2.0, a penicillin simulation platform developed by the process monitoring and technology group of Illinois State Institute of Technology in the United States, provides a standard platform for monitoring and fault diagnosis of batch processes. This platform has become an internationally influential penicillin simulation platform.

[0052] The present invention takes this platform as the simulation research object, sets the reaction time of each batch of penicillin fermentation as 400h, and the sampling interval as 1 hour, and selects 10 process variables for simulation research, as shown in Table 1. At the same time, the simulation platform can set three types of faults: 1. Air flow, 2. Stirring power, 3. Substrate flow acceleration rate. Each type of fault can be divided into two types: step disturbance and ramp disturbance, and the amplitude of the two disturbances, the introduction time and termination time of the disturbance can be further set.

[0053] Table 1 V...

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Abstract

The invention discloses a DSSAE-based intermittent process fault diagnosis method, and relates to the technical field of fault diagnosis of machine learning. The method comprises three stages of datapreprocessing, model training and fault diagnosis, wherein the'data preprocessing 'comprises the step of unfolding three-dimensional data in the fermentation process along the variable direction; secondly, corroding input data into a damaged data sample by utilizing random mapping, namely adding a noise reduction code; and finally, carrying out normalization processing on the corroded damaged datasample. And the model training comprises two processes of unsupervised pre-training and supervised tuning. And the fault diagnosis comprises the steps of inputting the collected test data into a trained network for fault diagnosis, outputting a fault category, and counting the accuracy. According to the DSSAE fault diagnosis method and device, only the data in the fault time period are filled, and the influence on the accuracy of DSSAE fault diagnosis due to the fact that too many unknowned data are manually filled is reduced. And meanwhile, the noise reduction codes are added, so that the robustness of the network is improved.

Description

technical field [0001] The invention relates to the technical field of machine learning fault diagnosis, in particular to an online fault diagnosis technology for batch processes. The method based on machine learning of the present invention is a specific application in the fault diagnosis of a typical batch process—penicillin fermentation process. Background technique [0002] As an extremely important production method in modern industrial processes, batch process has been widely used in the production of various and high value-added products such as medicine, food, biochemical industry, and semiconductor. However, in the actual process, due to a series of problems such as equipment aging and sudden changes in the external environment, failures occur from time to time. Therefore, fault diagnosis of batch process becomes very important to ensure the safety of production process and improve product quality. [0003] For the fault diagnosis of batch process, the methods com...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/084G06N3/088G06N3/045G06F18/24G06F18/214
Inventor 高学金王豪高慧慧
Owner BEIJING UNIV OF TECH
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