UKF-based fault detection for nonlinear stochastic systems with strong noise and communication protocols
Yang Feng1, Ming Gao1, Wuxiang Huai2
1College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, 266580, China.
ISA Transactions
|August 12, 2025
Summary
This study introduces an unscented Kalman filter for fault detection in noisy nonlinear systems, improving accuracy with communication protocols and advanced noise reduction techniques.
Area of Science:
- Control Systems Engineering
- Signal Processing
- Stochastic Systems
Background:
- Nonlinear stochastic systems are susceptible to noise and communication delays.
- Effective fault detection is crucial for system reliability and safety.
- Existing methods struggle with strong noise and complex communication protocols.
Purpose of the Study:
- To develop a robust fault detection method for nonlinear stochastic systems.
- To address challenges posed by strong noise and communication protocols.
- To quantitatively analyze fault detectability and its influencing factors.
Main Methods:
- Utilized the unscented Kalman filter for state estimation under communication protocols (Round-Robin, weighted Try-Once-Discard).
- Designed a novel residual generator and evaluation function using the exponentially weighted moving average method.
- Applied stochastic analysis theory to establish quantitative inequalities for fault detectability analysis.
Main Results:
- Successfully achieved state estimation for nonlinear systems with communication protocols.
- Developed a method to counteract strong noise impacts, reducing false alarms and missed detections.
- Derived relationships between fault amplitude, weight coefficient, false alarm rate, and missed detection rate.
Conclusions:
- The proposed unscented Kalman filter-based fault detection method is effective for nonlinear stochastic systems.
- The method demonstrates robustness against strong noise and communication protocols.
- Validated through simulations and experiments on a rotary steerable drilling tool system.
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