A fault detection method based on automatic encoder and Bayesian network

A technology of Bayesian network and autoencoder, which is applied in the direction of instruments, character and pattern recognition, manufacturing computing systems, etc. It can solve problems such as dynamic time expansion without consideration, and achieve the effect of satisfying diagnosis, rapid diagnosis and avoiding influence

Active Publication Date: 2019-01-04
TSINGHUA UNIV
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The encoder and decoder of traditional autoencoders are generally composed of multi-l

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  • A fault detection method based on automatic encoder and Bayesian network
  • A fault detection method based on automatic encoder and Bayesian network
  • A fault detection method based on automatic encoder and Bayesian network

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[0060] The present invention proposes a fault detection method based on an autoencoder and a Bayesian network, which will be further described in detail below in conjunction with the accompanying drawings and specific implementation examples.

[0061] The present invention proposes a fault detection method based on an autoencoder and a Bayesian network, which is divided into an offline stage and an online stage. The overall process is as follows figure 1 shown, including the following steps:

[0062] 1) Offline stage;

[0063] 1-1) Collect the data of the chemical production process and build a sample data set;

[0064] Select several variable data from any continuous chemical production process to construct a chemical process data set. The selection of variables is selected according to the specific chemical process; select one of the appropriate lengths from the chemical process data set (the general length is 10 of the number of selected variables) to 50 times) of normal ...

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Abstract

The invention provides a fault detection method based on an automatic encoder and a Bayesian network, belonging to the field of chemical process fault diagnosis. The off-line phase of the method is toselect variable data from any chemical continuous production process to construct chemical process data set and sample data set. The automatic encoder model is trained with sample data set and the statistic T2 and SPE detection threshold are calculated. Bayesian Networks are constructed with Chemical Process Data Set and the Conditional Probability is estimated; at the present stage, the real-time data are acquired and input into the automatic encoder model to obtain the correspond estimated value, and the corresponding T2 and SPE values of the input data are calculated and compared with thedetection threshold value: if the condition is satisfied, the chemical production process is normal; if not, the contribution of each variable is calculated, and the root cause of the fault is found through the Bayesian network. The invention automatically extracts features from process data of chemical production, effectively applies to a non-linear dynamic chemical process, and realizes the detection and rapid diagnosis of chemical process faults.

Description

technical field [0001] The invention relates to the field of chemical process fault diagnosis, in particular to a fault detection method based on an automatic encoder and a Bayesian network. Background technique [0002] The chemical production process is a complex process. In order to maximize the economic benefits, the chemical production process often requires "safe, stable, long, full and excellent", and the production equipment is required to be able to run smoothly for a long period of time. With the continuous development of automatic control technology, the control of the device is tightly coupled with the state variables. When a major variable in the chemical production process is abnormal, these abnormalities will be propagated through the device through mass transfer, heat transfer, etc., thereby This causes the entire device to fluctuate, causing the device to flood with alarms, making it difficult for operators to diagnose the fault correctly. If the operator ...

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Application Information

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IPC IPC(8): G06Q10/06G06Q50/04G06K9/62
CPCG06Q10/0635G06Q50/04G06F18/24155Y02P90/30
Inventor 赵劲松程非凡
Owner TSINGHUA UNIV
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