A fault detection, isolation, and estimation approach is proposed in this paper based on Interactive Multimodel (IMM) fusion\nfiltering and Strong Tracking Filtering (STF) for asynchronous multisensors dynamic systems. Time-varying fault is considered\nand a candidate fault model is built by augmenting the unknown fault amplitude directly into the system state for each kind of\npossible faultmode. By doing this, the dilemma of predetermining the fault extent asmodel design parameters in traditional IMMbased\napproaches is avoided. After that, the time-varying fault amplitude is estimated based on STF using its strong ability to track\nabrupt changes and robustness against model uncertainties. Through fusing information from multiple sensors, the performance\nof fault detection, isolation, and estimation is approved. Finally, a numerical simulation is performed to demonstrate the feasibility\nand effectiveness of the proposed method
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