Process Fault Monitoring Using Data Fusion Based on Extended Kalman Filter Incorporated with Time-Delayed Measurements
نویسنده
چکیده
A model-based process fault monitoring approach is proposed in this paper which utilizes a multi-sensor data fusion technique. The fusion algorithm is based on a discrete-time extended Kalman filter (EKF). The presented EKF is modified to incorporate the asynchronous sensor measurements. The resulting approach will be evaluated for a variety of conditions including synchronous/asynchronous measurements, full-state and non full-state measurements and time-varying dynamics for monitoring single, double, triple and quadruple process faults. The simulation studies on a CSTR benchmark problem demonstrate the effectiveness of the proposed fault monitoring approach to deal with different circumstances.
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