Kalman filters in non-uniformly sampled multirate systems: For FDI and beyond

This paper consists of two parts. The first part is the development of a datadriven Kalman filter for a non-uniformly sampled multirate (NUSM) system, including identification of the state space model and estimation of noise covariance matrices of the NUSM system. Algorithms for both one-step prediction and filtering are developed, and analysis of stability and convergence is conducted in the NUSM framework. The second part of the paper investigates a Kalman filter-based methodology for unified detection and isolation of sensor, actuator, and process faults in the NUSM system with analysis on fault detectability and isolability. Case studies using data collected from a pilot scale experimental plant and numerical examples are provided to justify the practicality of the proposed theory.

• Type: Journal
• Year: 2008
• Where: AUTOMATICA
• Authors: Weihua Li, Sirish L. Shah, Deyun Xiao
• Keywords: AUTOMATICA 2008, Kalman Filter-based Methodology, Noise Covariance Matrices, State Space Model