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

Time-series prediction is one of the major methodologies used for fault prediction. The methods 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 effective...

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Bibliographic Details
Published in:Chinese journal of chemical engineering 2014, Vol.22 (7), p.812-819
Main Authors: Zhu, Qunxiong, Jia, Yiwen, Peng, Di, Xu, Yuan
Format: Article
Language:English
Subjects:
Algorithms
Cost function
Fault prediction
Faults
Networks
Neural networks
Recurrent neural networks
Reservoir neural networks
Reservoirs
Risk analysis
Tennessee Eastman process
Time series
不适定性问题
储层
应用
改进型
故障预测
时间序列预测
递归神经网络
非线性映射能力
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Summary:Time-series prediction is one of the major methodologies used for fault prediction. The methods 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 problem of 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 output weight of the reservoir neural network. As a result, the amplitude of output weight is effectively controlled and the ill-posedness problem is 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%.
ISSN:1004-9541
2210-321X
DOI:10.1016/j.cjche.2014.05.016