摘要：This study investigated an adaptive control, fault diagnostics and prognostics of the anode voltage
regulator system at an ion implantation accelerator. The system was modeled as a 4th order AutoRegressive with
eXogenous (ARX) model, controlled by a Fuzzy Logic Controller (FLC). This model was then used as a basis
for constructing and updating a fault diagnosis module and a failure prognostics module. To maintain the
system’s performance, the controller’s response was continuously re-adjusted through an optimization scheme.
A Failure Mode and Effect Analysis (FMEA) was conducted resulting on five failure modes of the regulator
system. Fault data were generated in MATLAB simulation to train a random forest fault classification engine.
The optimal random forest classifier was 20 decision trees with a fault diagnostics accuracy of 98.06%. A Hidden
Markov Model (HMM) was constructed as the system’s fault progression model based on the interaction between
environmental conditions and controller actions. 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.
其他摘要：This study investigated an adaptive control, fault diagnostics and prognostics of the anode voltage regulator system at an ion implantation accelerator. The system was modeled as a 4th order AutoRegressive with eXogenous (ARX) model, controlled by a Fuzzy Logic Controller (FLC). This model was then used as a basis for constructing and updating a fault diagnosis module and a failure prognostics module. To maintain the system’s performance, the controller’s response was continuously re-adjusted through an optimization scheme. A Failure Mode and Effect Analysis (FMEA) was conducted resulting on five failure modes of the regulator system. Fault data were generated in MATLAB simulation to train a random forest fault classification engine. The optimal random forest classifier was 20 decision trees with a fault diagnostics accuracy of 98.06%. A Hidden Markov Model (HMM) was constructed as the system’s fault progression model based on the interaction between environmental conditions and controller actions. 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.
