基于置信规则库推理的过程报警预测方法
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摘要
为了更加有效的利用流程工业中的各种历史报警信息,对未来时刻的系统安全性进行评判,提出了一种基于置信规则库推理的过程报警时间序列预测方法,利用过程变量的历史报警数据建立置信规则库模型,采取粒子群算法进行模型参数学习。该预测模型可以对未来一段时间过程可能产生的报警状态变化趋势进行预测。通过数值实例仿真及实际工业过程报警数据对该模型进行验证,得到了较为满意的预测结果。
To better utilize historical alarm information of industrial processes, this paper introduces a belief rule base model based process alarm time series prognosing inference approach which is able to evaluate the process safety performance in the future. The belief rule base model involved is established using historical alarm data of process variables, while a particle swarm optimization algorithm is used for the model parameter learning. The online implementation of the model can help predict trends of the process alarm states in the future. A numerical simulation and industrial process alarm data are used to demonstrate the effectiveness of the approach with satisfying prognosis results.
To better utilize historical alarm information of industrial processes, this paper introduces a belief rule base model based process alarm time series prognosing inference approach which is able to evaluate the process safety performance in the future. The belief rule base model involved is established using historical alarm data of process variables, while a particle swarm optimization algorithm is used for the model parameter learning. The online implementation of the model can help predict trends of the process alarm states in the future. A numerical simulation and industrial process alarm data are used to demonstrate the effectiveness of the approach with satisfying prognosis results.
引文
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[8] Zhou Z J, Hu C H. An effective hybrid approach based on grey and ARMA for forecasting gyro drift[J]. Chaos, Solitons&Fractals, 2008,35(3):525-529.
[9] Yang J B, Liu J, Wang J, et al. Belief rule-base inference methodology using the evidential reasoning approach-RIMER[J].Systems, Man and Cybernetics, Part A:Systems and Humans, IEEE Transactions on, 2006,36(2):266-285.
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[12] Zhou Z J, Hu C H, Xu D L, et al. New model for system behavior prediction based on belief rule based systems[J]. Information Sciences, 2010, 180(24):4834-4864.
[13] Liu J, Martinez L, Calzada A, et al. A novel belief rule base representation, generation and its inference methodology[J].Knowledge-Based Systems, 2013,53:129-141.
[14] Yang J B, Liu J, Xu D L, et al. Optimization models for training belief-rule-based systems[J]. Systems, Man and Cybernetics, Part A:Systems and Humans, IEEE Transactions on, 2007,37(4):569-585.
[15] Zhou Z J, Hu C H, Yang J B, et al. A sequential learning algorithm for online constructing belief-rule-based systems[J]. Expert Systems with Applications, 2010,37(2):1790-1799.
[16] Chen Y W, Yang J B, Xu D L, et al. Inference analysis and adaptive training for belief rule based systems[J]. Expert Systems with Applications, 2011,38(10):12845-12860.
[17] Xu D L, Liu J, Yang J B, et al. Inference and learning methodology of belief-rule-based expert system for pipeline leak detection[J]. Expert Systems with Applications, 2007,32(1):103-113.
[18]王珂珂,赵汗青,吕强,等.基于改进PSO的自主车辆实时运动规划方法[J].控制工程,2013,20(1):173-178.Wang K K, Zhao H Q, Lv Q, et al. An Real-time Motion Planning Method for ALV Based on Improved PSO Algorithm[J]. Control Engineering of China,2013,20(1):173-178.
[2] Zheng X, Liu M. An overview of accident forecasting methodologies[J]. Journal of Loss Prevention in the process Industries, 2009, 22(4):484-491.
[3] Yang S K. An experiment of state estimation for predictive maintenance using Kalman filter on a DC motor[J]. Reliability engineering&system safety, 2002,75(1):103-111.
[4] Roy S S, Pratihar D K. Soft computing-based expert systems to predict energy consumption and stability margin in turning gaits of six-legged robots[J]. Expert Systems with Applications, 2012, 39(5):5460-5469.
[5] Gautam A K, Chelani A B, Jain V K, et al. A new scheme to predict chaotic time series of air pollutant concentrations using artificial neural network and nearest neighbor searching[J]. Atmospheric Environment, 2008,42(18):4409-4417.
[6] Jiang Y, Yao Y, Deng S, et al. Applying grey forecasting to predicting the operating energy performance of air cooled water chillers[J].International Journal of Refrigeration, 2004,27(4):385-392.
[7]王锋,李宏光,臧灏.基于Logistic和ARMA模型的过程报警预测[J].化工学报,2012,63(9):2941-2947.Wang F, Li H G, Zang H. Process alarm prognosis based on Logistic and ARMA models[J]. CIESC Journal, 2012,63(9):2941-2947.
[8] Zhou Z J, Hu C H. An effective hybrid approach based on grey and ARMA for forecasting gyro drift[J]. Chaos, Solitons&Fractals, 2008,35(3):525-529.
[9] Yang J B, Liu J, Wang J, et al. Belief rule-base inference methodology using the evidential reasoning approach-RIMER[J].Systems, Man and Cybernetics, Part A:Systems and Humans, IEEE Transactions on, 2006,36(2):266-285.
[10] Zhang B C,Han X X,Zhou Z J,et al. Construction of a new BRB based model for time series forecasting[J]. Applied Soft Computing,2013,13(12):4548-4556.
[11] Liu Y, Xie F, Xie C, et al. Prediction of time series of NPP operating parameters using dynamic model based on BP neural network[J].Annals of Nuclear Energy, 2015, 85:566-575.
[12] Zhou Z J, Hu C H, Xu D L, et al. New model for system behavior prediction based on belief rule based systems[J]. Information Sciences, 2010, 180(24):4834-4864.
[13] Liu J, Martinez L, Calzada A, et al. A novel belief rule base representation, generation and its inference methodology[J].Knowledge-Based Systems, 2013,53:129-141.
[14] Yang J B, Liu J, Xu D L, et al. Optimization models for training belief-rule-based systems[J]. Systems, Man and Cybernetics, Part A:Systems and Humans, IEEE Transactions on, 2007,37(4):569-585.
[15] Zhou Z J, Hu C H, Yang J B, et al. A sequential learning algorithm for online constructing belief-rule-based systems[J]. Expert Systems with Applications, 2010,37(2):1790-1799.
[16] Chen Y W, Yang J B, Xu D L, et al. Inference analysis and adaptive training for belief rule based systems[J]. Expert Systems with Applications, 2011,38(10):12845-12860.
[17] Xu D L, Liu J, Yang J B, et al. Inference and learning methodology of belief-rule-based expert system for pipeline leak detection[J]. Expert Systems with Applications, 2007,32(1):103-113.
[18]王珂珂,赵汗青,吕强,等.基于改进PSO的自主车辆实时运动规划方法[J].控制工程,2013,20(1):173-178.Wang K K, Zhao H Q, Lv Q, et al. An Real-time Motion Planning Method for ALV Based on Improved PSO Algorithm[J]. Control Engineering of China,2013,20(1):173-178.