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Control and Prognostics of Accelerator’s Anode Voltage Regulator Based on Empirical Modelling Approach

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【作者】 CHRISTIAN RobbyACHMAD BalzaKANG Hyun Gook

【Author】 CHRISTIAN Robby;ACHMAD Balza;KANG Hyun Gook;Nuclear & Quantum Engineering Dept.,Korea Advanced Institute of Science and Technology (KAIST);Physics Engineering Dept.,Gadjah Mada University;

【机构】 Nuclear & Quantum Engineering Dept.,Korea Advanced Institute of Science and Technology (KAIST)Physics Engineering Dept.,Gadjah Mada University

【摘要】 This study investigated the development of an adaptive control,fault diagnostics and prognostics of the anode voltage regulator system at ion implantation accelerator in Indonesia.A model of the regulator system was developed by using a white box analysis and the system identification methodology.Recursive regression algorithms were employed to estimate the model’s parameters.In order to capture slowly time-varying degradations,the identification algorithm was biased with adaptation gain variables.The system was identified as a 4th order Auto Regressive with e Xogenous(ARX) model structure.It was validated through residual correlation tests fulfilling the Gaussian distribution with a 3% significance level.The model was then used as a basis for constructing and updating a Fuzzy Logic Algorithm,fault diagnosis module and failure prognostics module.To maintain the system’s performance despite of degradations,the controller’s response was continuously re-adjusted through an optimization scheme.Five failure modes and the corresponding indicators were identified.Diagnosis of the active fault mode and its severity was done by analyzing the root locus of the model.Fault data were generated in the MATLAB simulation environment to train a random forest fault classification engine.The optimal random forest classifier was found to be composed of 20 decision trees with a classification accuracy of 98.06%.A fault progression model based on the interaction between environmental conditions and controller actions was proposed.The variations of these two factors were characterized to form a Hidden Markov Model(HMM).The particle filter and Bayesian inference methods were then employed to continuously update the HMM and predict the system’s Remaining Useful Lifetime(RUL).The proposed methodology was able to integrate an adaptive fuzzy logic control,prognosis and failure diagnosis altogether allowing a continual satisfactory performance of the voltage regulator system throughout its lifetime.

【Abstract】 This study investigated the development of an adaptive control,fault diagnostics and prognostics of the anode voltage regulator system at ion implantation accelerator in Indonesia.A model of the regulator system was developed by using a white box analysis and the system identification methodology.Recursive regression algorithms were employed to estimate the model’s parameters.In order to capture slowly time-varying degradations,the identification algorithm was biased with adaptation gain variables.The system was identified as a 4th order Auto Regressive with e Xogenous(ARX) model structure.It was validated through residual correlation tests fulfilling the Gaussian distribution with a 3% significance level.The model was then used as a basis for constructing and updating a Fuzzy Logic Algorithm,fault diagnosis module and failure prognostics module.To maintain the system’s performance despite of degradations,the controller’s response was continuously re-adjusted through an optimization scheme.Five failure modes and the corresponding indicators were identified.Diagnosis of the active fault mode and its severity was done by analyzing the root locus of the model.Fault data were generated in the MATLAB simulation environment to train a random forest fault classification engine.The optimal random forest classifier was found to be composed of 20 decision trees with a classification accuracy of 98.06%.A fault progression model based on the interaction between environmental conditions and controller actions was proposed.The variations of these two factors were characterized to form a Hidden Markov Model(HMM).The particle filter and Bayesian inference methods were then employed to continuously update the HMM and predict the system’s Remaining Useful Lifetime(RUL).The proposed methodology was able to integrate an adaptive fuzzy logic control,prognosis and failure diagnosis altogether allowing a continual satisfactory performance of the voltage regulator system throughout its lifetime.

  • 【会议录名称】 Proceedings of STSS/ISSNP 2015
  • 【会议名称】The second International Symposium on Socially and Technically Symbiotic Systems (STSS2015) ;The 7th International Symposium on Symbiotic Nuclear Power Systems (ISSNP2015)
  • 【会议时间】2015-08-25;2015-08-25
  • 【会议地点】日本京都;日本京都
  • 【分类号】TL507
  • 【主办单位】京都大学、哈尔滨工程大学
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