Abstract
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.
Highlights
The recent drive to increase efficiency in vehicle systems has led to an increased interest in developing vehicle health management systems to improve condition-based maintenance (CBM) [1]programs across air, space, and ground platforms
This section describes the development of physics-based models and identification of health indicators somedescribes of the safety-critical power of train subsystems of the armoured personnel carrier (APC), the drive train, This for section the development physics-based and namely, identification of health indicators for some of the safety-critical power train subsystemsmodels of the APC, namely, the drive train, the engine, for andsome the final drives
The operation of the power train with an indication of the parameters being monitored by the sensor with an indication of the parameters being monitored by the sensor network
Summary
The recent drive to increase efficiency in vehicle systems has led to an increased interest in developing vehicle health management systems to improve condition-based maintenance (CBM) [1]. The development of health and usage monitoring systems (HUMS) has been the principle enabler in support of CBM This involves embedding a network of sensors on-board the vehicle that harvest health data across various subsystems and stores it for further processing. Other relevant studies in this area include an integrated framework for health assessment and fault classification of the drive train of wind turbines by Zhao et al (2013) [11] and an online fault diagnosis method for a power train in fuel cell vehicles by Yun et al (2008) [12] Within these studies, the importance of the positioning on-board sensors in essential subsystem components is highlighted, those that have a high probability failure rate. A study by Dong et al (2019) [13] presents the requirements, selection principles, and future development trends of sensors to support the health management of armoured vehicles as well difficulties in sensor installation, taking the vehicle gear box as a case study
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
