Automated collection of on-vehicle sensor data allows the development of artificial intelligence (AI) techniques for vehicular systems’ diagnostic and prognostic processes to better assess the state-of-health, predict faults and evaluate residual life of ground vehicle systems. One of the vital subsystems, in terms of safety and mission criticality, is the power train, (comprising the engine, transmission, and final drives), which provides the driving torque required for vehicle acceleration. In this paper, a novel health and usage monitoring system (HUMS) architecture is presented, together with dedicated diagnosis/prognosis algorithms that utilize data gathered from a sensor network embedded in an armoured personnel carrier (APC) vehicle. To model the drivetrain, a virtual dynamometer is introduced, which estimates the engine torque output for successive comparison with the measured torque values taken from the engine control unit. This virtual dynamometer is also used in conjunction with other sensed variables to determine the maximum torque output of the engine, which is considered to be the primary indicator of engine health. Regression analysis is performed to capture the effect of certain variables such as engine hours, oil temperature, and coolant temperature on the degradation of maximum engine torque. Degradations in the final drives system were identified using a comparison of the temperature trends between the left-hand and right-hand final drives. This research lays foundations for the development of real-time diagnosis and prognosis functions for an integrated vehicle health management (IVHM) system suitable for safety critical manned and unmanned vehicle applications.

In many types of Uncrewed Aircraft Systems (UAS), commonly occurring faults have the potential to negatively impact the mission or even to compromise the safety of flight. The adoption of onboard Intelligent Health and Mission Management (IHMM) functionalities is therefore essential both to ensure service continuity and to support trusted autonomous flight operations. This article proposes a new IHMM system architecture and associated software functionalities for a UAS-assisted wireless communication service. The proposed system is capable of predicting and early-detecting faults affecting relevant UAS subsystems. Such capabilities also support a real-time updating of the mission plan, allowing fast formation reconfiguration and minimizing mission performance degradations. The diagnosis of faults affecting UAS communication subsystems is accomplished using suitably selected Machine Learning (ML) techniques. A representative case study of a UAS-assisted wireless communication service is used to verify the effectiveness of the proposed techniques. The analysis of network coverage and capacity demonstrate that, in the presence of various failure events, the network maintains an acceptable level of performance by following the IHMM resolutions to reposition the remaining flight vehicles in the formation. The case study also demonstrates how a combination of ML techniques for fault prediction/early-detection and metaheuristic algorithms for UAS topology optimization can provide a complete IHMM solution, thereby ensuring a high level of integrity, continuity, availability and overall mission performance.