SCI和EI收录∣中国化工学会会刊

›› 2014, Vol. 22 ›› Issue (7): 812-819.DOI: 10.1016/j.cjche.2014.05.016

• PROCESS MONITOR • Previous Articles     Next Articles

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

Qunxiong Zhu, Yiwen Jia, Di Peng, Yuan Xu   

  1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
  • Received:2013-05-15 Revised:2013-12-24 Online:2014-08-23 Published:2014-07-28
  • Supported by:
    Supported by the National Natural Science Foundation of China (61074153).

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

Qunxiong Zhu, Yiwen Jia, Di Peng, Yuan Xu   

  1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
  • 通讯作者: Yuan Xu
  • 基金资助:
    Supported by the National Natural Science Foundation of China (61074153).

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%.

Key words: Fault prediction, Time series, Reservoir neural networks, Tennessee Eastman process

摘要: 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%.

关键词: Fault prediction, Time series, Reservoir neural networks, Tennessee Eastman process