In this work we investigate two main applications of the detection and isolation of partial (soft) and total (hard) failures in the reaction wheel (RW) actuators of the satellite attitude control systems (ACS) and in the Heating Ventilation and Air Conditioning (HVAC) valve actuators respectively. The fault detection diagnosis and isolation (FDDI) is accomplished using a probabilistic approach based on the interactive multiple models (IMM) schemes embedded with Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF) estimation techniques. Towards this objective, the healthy modes of the ACS and HVAC systems under different operating conditions as well as a number of different fault scenarios including changes and anomalies in the temperature, power supply bus voltage, and unexpected current variations in the actuators of each axis of the satellite, or leakage, stuck-open and stuck-close fault modes in the HVAC actuator valves are considered. We describe and develop a bank of interacting multiple model Extended Kalman Filters (IMM_EKF) or Unscented Kalman Filters (IMM_UKF) to detect and isolate the above mentioned reaction wheel and valve failures in the ACS and HVAC systems. Also, it should be emphasized that the proposed IMM_EKF and IMM_UKF techniques are implemented based on high-fidelity highly nonlinear models of a commercial ITHACO RWA and discharge air temperature (DAT) cooling or heating coils. Compared to other fault detection diagnosis and isolation (FDDI) strategies developed in the control systems literature, the proposed FDDI schemes is shown, through extensive numerical simulations by using MATLAB and SIMULINK software packages, to be more accurate, less computationally demanding, and more robust with the potential of extending to a number of other engineering applications. Also, the proposed algorithms deal directly with the nonlinear dynamics of the system, the