Autonomous and semi-autonomous systems are beginning to emerge in various interventions, automating important steps of the operation. These systems are expected to become standard modality and revolutionize the face of surgery.

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Abstract-A low computational cost method is proposed for detecting actuator/sensor faults. Typical model-based fault detection units for multiple sensor faults, require a bank of estimators (i.e., conventional Kalman estimators or artificial intelligence based ones). The proposed fault detection scheme uses an artificial intelligence approach for developing of a low computational power fault detection unit abbreviated as 'iFD'. In contrast to the bank-of-estimators approach, the proposed iFD unit employs a single estimator for multiple actuator/sensor fault detection. The efficacy of the proposed fault detection scheme is illustrated through a rigorous analysis of the results for a number of sensor fault scenarios on an electromagnetic suspension system. Index Terms-fault tolerant control, actuator/sensor fault detection, reconfigurable control, loop-shaping robust control design, electromagnetic suspension, maglev trains, neural networks, artificial intelligence.