Bayesian Actuator Fault Estimation for Random Control Systems
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Solution Overview
Problem
Current methods for actuator fault diagnosis in automatic control systems cannot estimate the amplitude, position, or nature of faults in random systems, limiting their effectiveness for troubleshooting.
Innovation Solution
The method employs Bayesian learning to model actuator faults using a random walking model, representing joint posterior probability distributions and iteratively updating estimates of system state variables and actuator faults, allowing for real-time fault estimation and determination of fault occurrence, amplitude, and position.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional fault diagnosis methods are used, then fault detection capability is provided, but fault estimation information (amplitude, position, nature) cannot be obtained
Solution Approach 1:
The patent introduces a Bayesian estimator as an intermediary component between the fault detection system and the troubleshooting process. This estimator processes the detected fault signals and generates comprehensive fault estimation information including amplitude, position, and nature of faults, thereby resolving the information loss without requiring complete redesign of the diagnosis system.
Solution Approach 2:
The patent replaces traditional mechanical fault diagnosis approaches with a computational Bayesian estimation system. Instead of relying on physical inspection and simple detection thresholds, the system uses probabilistic modeling and iterative estimation algorithms to extract detailed fault characteristics, substituting mechanical diagnostic methods with intelligent computational analysis.
2Measurement precision
If fault estimation is implemented, then troubleshooting information is provided, but measurement precision requirements increase
Solution Approach 1:
The patent performs preliminary actions by establishing Bayesian prior distributions for fault parameters before actual fault occurrence. These priors encode existing knowledge about typical fault characteristics, which then guide the estimation process and improve measurement precision even with limited observation data, reducing the difficulty of accurate fault parameter detection.
Solution Approach 2:
The patent implements iterative feedback estimation where the Bayesian estimator continuously updates fault parameter estimates based on new measurements and previous estimates. This feedback mechanism refines measurement precision over time, allowing the system to achieve accurate fault parameter estimation even in challenging measurement conditions by leveraging historical information.
Data Source
AI summary
The present disclosure relates to estimation methods of actuator faults based on Bayesian learning. An actuator fault is modeled based on a random walking model, and a joint posterior probability distribution of a system state variable and the actuator fault is represented using two mutually independent hypothesis distributions based on a variational Bayesian theory; a system state variable and an actuator fault of a system at a moment are predicted at a moment; and a predicted system state variable and a predicted actuator fault are iteratively updated at the moment according to the Bayesian theory to output an estimated value of the system state variable at the moment, a variance of the estimated value and an estimated value of the actuator fault at the moment and a variance of the estimated value.