Study and Application of Fault Prediction Methods with Improved Reservoir Neural Networks

Qunxiong Zhu; Yiwen Jia; Di Peng; Yuan Xu

doi:10.1016/j.cjche.2014.05.016

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Study and Application of Fault Prediction Methods with Improved Reservoir Neural Networks

PROCESS MONITOR | Updated：2026-01-06

- Study and Application of Fault Prediction Methods with Improved Reservoir Neural Networks
- Study and Application of Fault Prediction Methods with Improved Reservoir Neural Networks
- 中国化学工程学报（英文版） 2014年22卷第7期页码：812-819
- Affiliations：
  
  College of Information Science and Technology, Beijing University of Chemical Technology,Beijing,China,100029
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2014.05.016
  中图分类号：
- 纸质出版：2014
- Accepted：
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Qunxiong Zhu, Yiwen Jia, Di Peng, 等. Study and Application of Fault Prediction Methods with Improved Reservoir Neural Networks[J]. 中国化学工程学报（英文版）, 2014,22(7):812-819.

Qunxiong Zhu, Yiwen Jia, Di Peng, Yuan Xu. Study and Application of Fault Prediction Methods with Improved Reservoir Neural Networks[J]. , 2014, 22(7): 812-819.
Qunxiong Zhu, Yiwen Jia, Di Peng, 等. Study and Application of Fault Prediction Methods with Improved Reservoir Neural Networks[J]. 中国化学工程学报（英文版）, 2014,22(7):812-819. DOI： 10.1016/j.cjche.2014.05.016.

Qunxiong Zhu, Yiwen Jia, Di Peng, Yuan Xu. Study and Application of Fault Prediction Methods with Improved Reservoir Neural Networks[J]. , 2014, 22(7): 812-819. DOI： 10.1016/j.cjche.2014.05.016.

摘要

Abstract

Time-series prediction is one of themajor methodologies used for fault prediction. Themethods based on recurrent neural networks have been widely used in time-series prediction for their remarkable non-liner mapping ability. As a new recurrent neural network

reservoir neural network can effectively process the time-series prediction. However

the ill-posedness problemof reservoir neural networks has seriously restricted the generalization performance. In this paper

a fault prediction algorithm based on time-series is proposed using improved reservoir neural networks. The basic idea is taking structure risk into consideration

that is

the cost function involves not only the experience risk factor but also the structure risk factor. Thus a regulation coefficient is introduced to calculate the outputweight of the reservoir neural network. As a result

the amplitude of outputweight is effectively controlled and the ill-posedness problemis solved. Because the training speed of ordinary reservoir networks is naturally fast

the improved reservoir networks for time-series prediction are good in speed and generalization ability. Experiments on Mackey-Glass and sunspot time series prediction prove the effectiveness of the algorithm. The proposed algorithm is applied to TE process fault prediction. We first forecast some timeseries obtained from TE and then predict the fault type adopting the static reservoirs with the predicted data. The final prediction correct rate reaches 81%.

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