Seeded Fault Testing of Military Ground Vehicles as a Pathway to Condition Based Maintenance

摘要

The performance of military ground vehicle systems is being degraded due to high operation tempo and exposure to extreme environments while performing in-theater service. To address this issue, the US Army is implementing a policy of Condition Based Maintenance which is supported by the Army Material System Analysis Activity (AMSAA). The vision of this policy is to base the maintenance of systems upon the actual condition of the system and not upon time- or distance-based schedules. This capability will be enabled by the application of usage, diagnostic and prognostic processes executed on a Health and Usage Monitoring System (HUMS) installed on these vehicle systems. A thorough understanding of the ways in which the system condition is degenerated and the ability of the HUMS to detect, identify, and communicate all conditions that require maintenance in a timely manner are key requirements of these processes. Seeded Fault Testing is the critical means of fulfilling these requirements.rnA joint Seeded Fault Testing project between AMSAA and the US Aimy Aberdeen Test Center (ATC) has been initiated to gain a thorough understanding of ground vehicle system condition degeneration and HUMS implementation of products and processes that can accurately identify and communicate it. A military vehicle underwent exhaustive testing in support of this project. The vehicle was subjected to specific use scenarios while carefully controlled faults are induced in engine, transmission, and other key mechanical subsystems that would degrade vehicle performance and degenerate system condition. The vehicle's induced faults included lowered coolant levels to simulate leakage, restriction of air flow across radiators and filters to simulate dust and debris accumulation, and lowered transmission and engine oil levels to simulate leakage and usage. The objective of this project was to use the results from the seeded fault tests to establish critical thresholds, trends, and patterns that will be the basis of the creation andrnimplementation of real-time HUMS-based algorithms that predict faults, warn operators and maintainers of imminent failures, and provide a sound foundation for ConditionrnBased Maintenance.