Fault-Detection Method for Batch Process Based on Sparse Distance

ZHANG Cheng; LI Yuan; GAO Xianwen

doi:10.13976/j.cnki.xk.2014.0588

Volume 43 Issue 5

Oct. 2014

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INFORMATION AND CONTROL > 2014 > 43(5): 588-595. > DOI: 10.13976/j.cnki.xk.2014.0588 CSTR: 32166.14.xk.2014.0588

ZHANG Cheng, LI Yuan, GAO Xianwen. Fault-Detection Method for Batch Process Based on Sparse Distance[J]. INFORMATION AND CONTROL, 2014, 43(5): 588-595. DOI: 10.13976/j.cnki.xk.2014.0588

Citation:

ZHANG Cheng, LI Yuan, GAO Xianwen. Fault-Detection Method for Batch Process Based on Sparse Distance[J]. INFORMATION AND CONTROL, 2014, 43(5): 588-595. DOI: 10.13976/j.cnki.xk.2014.0588

Citation:

ZHANG Cheng, LI Yuan, GAO Xianwen. Fault-Detection Method for Batch Process Based on Sparse Distance[J]. INFORMATION AND CONTROL, 2014, 43(5): 588-595. DOI: 10.13976/j.cnki.xk.2014.0588

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Fault-Detection Method for Batch Process Based on Sparse Distance

1.
School of Information Science and Technology, Northeastern University, Shenyang 110819, China
2.
Research Center for Technical Process Fault Diagnosis and Safety, Shenyang University of Chemical Technology, Shenyang 110142, China

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Received Date: July 23, 2013
Revised Date: April 08, 2014
Published Date: October 19, 2014

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Abstract

Abstract

Aiming at the features of multiple product processes, nonlinear and non-Gaussian, a fault detection method in batch process based on sparse distance (FD-SD) is proposed. Sparse distance is used to measure the density of training samples around a test sample and to analyze the training samples distribution feature near the test sample. Sparse distance is calculated by cumulative distribution function of sample distance through kernel density estimate function with changed window width. The control limit is calculated by cumulative distribution function of sparse distance, and then the detection model based on sparse distance is built. FD-SD does not needs the hypothesis of variables obeying Gauss and linear distribution and can improve the accuracy and reliability of the fault detection process. From Simulation results in artificial case and semiconductor etch batch process, it is shown that FD-SD can detect fault in nonlinear and multi-mode processes. The validity of FD-SD is proved by the results.
- sparse distance,
- fault detection,
- principal component analysis,
- batch process

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