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Industrial process fault detection method based on autoregression dynamic hidden variable model

A fault detection, industrial process technology, applied in program control, electrical testing/monitoring, testing/monitoring control systems, etc., can solve the problems of ignoring the static characteristics of the internal structure of the data, unfavorable for the implementation of industrial process automation, and low accuracy.

Inactive Publication Date: 2016-03-16
ZHEJIANG UNIVERSITY OF SCIENCE AND TECHNOLOGY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, traditional dynamic modeling and monitoring methods still have some shortcomings
First of all, since these methods all use extended matrices, the model parameters and degrees of freedom are much larger than the corresponding static models
Second, these methods can only guarantee to effectively extract the autocorrelation of the data, but ignore the static characteristics that reflect the internal structure of the data.
Therefore, these traditional dynamic models cannot accurately extract the dynamic and static characteristics of the data at the same time, and the process monitoring method based on this is not accurate, which is not conducive to the implementation of automation in industrial processes

Method used

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  • Industrial process fault detection method based on autoregression dynamic hidden variable model
  • Industrial process fault detection method based on autoregression dynamic hidden variable model
  • Industrial process fault detection method based on autoregression dynamic hidden variable model

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Embodiment 1

[0061] An industrial process fault detection method based on the autoregressive dynamic hidden variable model, the method aims at the fault detection problem of the industrial process, first uses the distributed control system to collect data under normal working conditions, and establishes the autoregressive dynamic hidden variable model; the model structure The parameters of the model are estimated by the Kalman filter, the Karl smoother and the expectation-maximization algorithm. On this basis, two monitoring statistics T are constructed using the dynamic and static noise of the model 2 and SPE and their corresponding statistical limits and SPE lim ; To monitor the new process data, the Kalman filter can be used to derive the dynamic hidden variables of the test data, and calculate the corresponding statistics and fault detection results.

[0062] The main steps of the technical solution adopted in the present invention are as follows:

[0063] (1): Utilize the data coll...

Embodiment 2

[0119] The effectiveness of the present invention is illustrated below in conjunction with the example of a specific industrial process. The data of this process comes from the US TE (TennesseeEastman--Tennessee-Eastman) chemical process experiment, and the prototype is an actual process flow of Eastman Chemical Company. The data of the TE process has complex characteristics such as high dimensionality, nonlinearity, autocorrelation, and time variation, and has become a common simulation platform in the field of industrial process control and monitoring. The entire TE process includes 41 measured variables and 12 manipulated variables (control variables), of which 41 measured variables include 22 continuous measured variables and 19 component measured values, which are sampled every 3 minutes, including 21 batches of fault data . Of these failures, 16 are known and 5 are unknown1301. Faults 1 to 7 are related to step changes in process variables, such as the inlet temperatur...

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Abstract

The invention relates to an industrial process fault detection method based on an autoregression dynamic hidden variable model. According to the method, by use of data at a normal operation state in an industrial process, a dynamical process model with universality is established, and the model predicts a model structure through a Kalman filter and a smoother and an expectation maximization algorithm, such that dynamical and static coupling relation of industrial data can be effectively extracted; and then, based on dynamic and static noise of the dynamic model, a corresponding monitoring statistical amount is constructed, and a final fault detection result is obtained. Compared with other existing methods, the method provided by the invention can greatly improve the effects of industrial process dynamic modeling and fault detection, reduces the false alarm rate and the missed alarm rate of faults, improves the monitoring performance to a quite large degree, enhances the understanding capability and the operation confidence of process operators for the process and better facilitates automation implementation of the industrial process.

Description

technical field [0001] The invention belongs to the field of industrial process control, and in particular relates to an industrial process fault detection method based on an autoregressive dynamic hidden variable model. Background technique [0002] In recent years, the problem of real-time monitoring and control of modern industrial processes has been paid more and more attention by the industrial and academic circles. It plays a vital role in improving product quality and ensuring the safe and stable operation of the production process, and has become an indispensable part of the industrial production process. Due to equipment aging, improper operation, environmental interference and other reasons, different types of process failures will inevitably occur in the production process. If these failures cannot be discovered and dealt with in time, their disturbances to the production process may be continuously propagated and amplified, resulting in cause irreparable losses....

Claims

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Application Information

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IPC IPC(8): G05B23/02
CPCG05B23/0243G05B2219/23447
Inventor 周乐侯北平宋执环
Owner ZHEJIANG UNIVERSITY OF SCIENCE AND TECHNOLOGY
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