Application of Prognostics and Health Management in Armored Equipment Maintenance Based on Afmea

Yong-jun, Song; Li, Bingshang; Ye, Hongbing; Cao, Yanhua

doi:10.1016/j.egypro.2011.10.423

Cited by 2 publications

(2 citation statements)

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“…The HUMS onboard CBM-enabled military vehicles, such as an armoured personnel carrier (APC), comprises a sensor network, which captures and stores large amounts of status data at vehicle, system, and component levels [13]. 3 In the pre-processing step, raw measurement data collected from all sensors are filtered, fused, and analysed. Data rejection and filtration are required in this step to remove outliers and noise, to get a realistic picture of normal behaviour.…”

Section: Hums Conceptmentioning

confidence: 99%

“…This provides an opportunity to leverage the data to develop an intelligent vehicle health management system with the intention of increasing the levels of efficiency and effectiveness of individual assets and vehicle fleets, which can translate into tangible mission, maintenance, and support benefits. The overall benefits expected from an opportune exploitation of HUMS technology are improved availability, safety, and reliability of vehicles and components as well as the minimization of operational, maintenance, and life-cycle costs also in relation to a reduction in the redundancy levels [ 2 , 3 ]. The concept of HUMS was initially introduced by the National Aeronautics Space Administration (NASA) in 1992, as a technology to collect data, diagnose, predict, and mitigate faults and support the operational decisions and post-operational maintenance activities of space vehicles [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

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Vehicular Sensor Network and Data Analytics for a Health and Usage Management System

Ranasinghe

Kapoor

Gardi

et al. 2020

Sensors

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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.

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Section: Hums Conceptmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%