Early fault warning for nuclear power machinery is conducive to timely troubleshooting and reductions in safety risks and unnecessary costs. This paper presents a novel intelligent fault prediction method, integrated ...Early fault warning for nuclear power machinery is conducive to timely troubleshooting and reductions in safety risks and unnecessary costs. This paper presents a novel intelligent fault prediction method, integrated probabilistic principal component analysis(PPCA), multi-resolution wavelet analysis, Bayesian inference, and RNN model for nuclear power machinery that consider data uncertainty and chaotic time series. After denoising the source data, the Bayesian PPCA method is employed for dimensional reduction to obtain a refined data group. A recurrent neural network(RNN) prediction model is constructed, and a Bayesian statistical inference approach is developed to quantitatively assess the prediction reliability of the model. By modeling and analyzing the data collected on the steam turbine and components of a nuclear power plant, the results of the goodness of fit, mean square error distribution, and Bayesian confidence indicate that the proposed RNN model can implement early warning in the fault creep period. The accuracy and reliability of the proposed model are quantitatively verified.展开更多
Accurate fault prediction can obviously reduce cost and decrease the probability of accidents so as to improve the performance of the system testing and maintenance. Traditional fault prediction methods are always off...Accurate fault prediction can obviously reduce cost and decrease the probability of accidents so as to improve the performance of the system testing and maintenance. Traditional fault prediction methods are always offline that are not suitable for online and real-time processing. For the complicated nonlinear and non-stationary time series, it is hard to achieve exact predicting result with single models such as support vector regression （SVR）, artifieial neural network （ANN）, and autoregressive moving average （ARMA）. Combined with the accurate online support vector regression （AOSVR） algorithm and ARMA model, a new online approach is presented to forecast fault with time series prediction. The fault trend feature can be extracted by the AOSVR with global kernel for general fault modes. Moreover, its prediction residual that represents the local high-frequency components is synchronously revised and compensated by the sliding time window ARMA model. Fault prediction with combined AOSVR and ARMA can be realized better than with the single one. Experiments on Tennessee Eastman process fault data show the new method is practical and effective.展开更多
This paper makes a study of the cause of manufacturing fault,develops the and/or- fault-tree of manufacturing quality fault for MC,and presents a new concept of faint manufacturing quality fault(FMQF)and the decision ...This paper makes a study of the cause of manufacturing fault,develops the and/or- fault-tree of manufacturing quality fault for MC,and presents a new concept of faint manufacturing quality fault(FMQF)and the decision making tree with which the fault of manufacturing system would be found out from FMQF.An approach to identification of FMQF,based on fuzzy set theory,is presented,which can be used for estimating the status of equipment with the deviation of control charts.Based on the study above,an expert system for the flexible manufacturing system's FMQF detection and prediction is built.展开更多
By studying different compressive strengths and changes in the characteristics of rocks,five variables were selected to predict faults in coal mines. Drillholes in the mined area were divided into two populations, i.e...By studying different compressive strengths and changes in the characteristics of rocks,five variables were selected to predict faults in coal mines. Drillholes in the mined area were divided into two populations, i.e., drillholes containing faults and drillholes without faults. Discriminant functions were established from the values of the five variables using Fisher's approach. Drillholes in the non-mined areas were allocated to one of the two populations by using discriminant functions. The terrenes of each drillhole were divided into 10 sections, above and below a minable coal seam. Each section has 10 layers of rocks. The population to which each drillhole in a section belongs is sorted out and the probability of each drillhole with faults obtained,i.e., a contour map of predicting the probability of faults in coal mining is shown. A comparison with the real distribution of faults shows that the precision of accurately predicting faults is greater than 70 per cent.展开更多
基金the National Natural Science Foundation of China(No.51875209)the Guangdong Basic and Applied Basic Research Foundation(No.2019B1515120060)the Open Funds of State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment。
文摘Early fault warning for nuclear power machinery is conducive to timely troubleshooting and reductions in safety risks and unnecessary costs. This paper presents a novel intelligent fault prediction method, integrated probabilistic principal component analysis(PPCA), multi-resolution wavelet analysis, Bayesian inference, and RNN model for nuclear power machinery that consider data uncertainty and chaotic time series. After denoising the source data, the Bayesian PPCA method is employed for dimensional reduction to obtain a refined data group. A recurrent neural network(RNN) prediction model is constructed, and a Bayesian statistical inference approach is developed to quantitatively assess the prediction reliability of the model. By modeling and analyzing the data collected on the steam turbine and components of a nuclear power plant, the results of the goodness of fit, mean square error distribution, and Bayesian confidence indicate that the proposed RNN model can implement early warning in the fault creep period. The accuracy and reliability of the proposed model are quantitatively verified.
文摘Accurate fault prediction can obviously reduce cost and decrease the probability of accidents so as to improve the performance of the system testing and maintenance. Traditional fault prediction methods are always offline that are not suitable for online and real-time processing. For the complicated nonlinear and non-stationary time series, it is hard to achieve exact predicting result with single models such as support vector regression （SVR）, artifieial neural network （ANN）, and autoregressive moving average （ARMA）. Combined with the accurate online support vector regression （AOSVR） algorithm and ARMA model, a new online approach is presented to forecast fault with time series prediction. The fault trend feature can be extracted by the AOSVR with global kernel for general fault modes. Moreover, its prediction residual that represents the local high-frequency components is synchronously revised and compensated by the sliding time window ARMA model. Fault prediction with combined AOSVR and ARMA can be realized better than with the single one. Experiments on Tennessee Eastman process fault data show the new method is practical and effective.
文摘This paper makes a study of the cause of manufacturing fault,develops the and/or- fault-tree of manufacturing quality fault for MC,and presents a new concept of faint manufacturing quality fault(FMQF)and the decision making tree with which the fault of manufacturing system would be found out from FMQF.An approach to identification of FMQF,based on fuzzy set theory,is presented,which can be used for estimating the status of equipment with the deviation of control charts.Based on the study above,an expert system for the flexible manufacturing system's FMQF detection and prediction is built.
基金Project 40772198 supported by the National Natural Science Foundation of China
文摘By studying different compressive strengths and changes in the characteristics of rocks,five variables were selected to predict faults in coal mines. Drillholes in the mined area were divided into two populations, i.e., drillholes containing faults and drillholes without faults. Discriminant functions were established from the values of the five variables using Fisher's approach. Drillholes in the non-mined areas were allocated to one of the two populations by using discriminant functions. The terrenes of each drillhole were divided into 10 sections, above and below a minable coal seam. Each section has 10 layers of rocks. The population to which each drillhole in a section belongs is sorted out and the probability of each drillhole with faults obtained,i.e., a contour map of predicting the probability of faults in coal mining is shown. A comparison with the real distribution of faults shows that the precision of accurately predicting faults is greater than 70 per cent.
