An Integrated Learning and Filtering Approach for Fault Diagnosis of a Class of Nonlinear Dynamical Systems

Area of Science:

• Control Systems Engineering
• Nonlinear System Analysis
• Fault Diagnosis and Fault Tolerance

Background:

• Continuous-time nonlinear systems are susceptible to process and sensor faults.
• Modeling uncertainties and measurement noise complicate fault diagnosis.
• Existing methods may struggle with integrated fault detection and identification.

Purpose of the Study:

• To develop an integrated approach for fault diagnosis in nonlinear systems.
• To address challenges posed by modeling uncertainties and measurement noise.
• To accurately detect, isolate, and identify both process and sensor faults.

Main Methods:

• An integrated filtering and adaptive approximation-based approach is proposed.
• Learning techniques are used to adapt to modeling uncertainties.
• Filtering is employed to mitigate measurement noise.
• Separate estimation models for process and sensor faults are utilized.
• Adaptive isolation thresholds and exclusion-based logic are implemented for fault identification.

Main Results:

• Tight detection thresholds are derived by integrating learning and filtering.
• Effective dampening of measurement noise is achieved.
• Accurate estimation of potential faults is performed by dedicated models.
• Rigorous derivation of fault detectability and identification conditions is presented.
• Simulation results validate the proposed approach's effectiveness.

Conclusions:

• The proposed integrated approach enhances fault diagnosis in nonlinear systems.
• The method robustly handles modeling uncertainties and measurement noise.
• It enables accurate detection, isolation, and identification of process and sensor faults.