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This paper describes the author's experiences in the design, validation and field-testing of a low cost, online oil condition monitor for diesel driven Army ground vehicles. This online oil condition monitor utilizes a multi-frequency approach to electrochemical impedance spectroscopy to interrogate and evaluate fluid health in near real time. A dual microcontroller processing architecture embedded in the sensor itself executes an oil-health evaluation algorithm and provides estimates of lubricant remaining useful life, as well as identification of the primary mode of degradation of the fluid. These data are transmitted off the sensor via J1939 compliant CAN messages. In this paper the unique application requirements, which formed the foundation of the development process, are discussed, and the technical and design challenges associated with producing a military grade smart-sensor at a sufficiently low price point for widespread adoption in the ground vehicle market are detailed.

Lithium-ion (LI) batteries are rapidly becoming a viable choice for military and civil electric vehicles (EV), hybrid electric vehicles (HEVs), unmanned systems, and other applications, mainly because they contain higher energy density, provide higher cycle life, offer better resistance to memory effects, and weigh less than other potential technologies. These same benefits have also led to widespread integration of lithium-ion products into the portable electronics markets. However, lithium-ion batteries carry their own disadvantages, including degradation at deep discharge, capacity loss at high temperatures, and susceptibility to catastrophic failure from venting (especially during charging), shorting, etc. that can have dire consequences on the platform. Another concern with EV/HEV applications is that many cells (packaged as battery packs/modules) are needed to provide sufficient power.

In cooperation with the major propulsion engine manufacturers, the authors are developing and demonstrating a unique very high frequency (VHF) vibration monitoring system that integrates various vibro-acoustic data with intelligent feature extraction and fault isolation algorithms to effectively assess engine gearbox and generator health. The system is capable of reporting on the early detection and progression of faults by utilizing piezoelectric, optical, and acoustic frequency measurements for improved, incipient anomaly detection. These gas turbine engine vibration monitoring technologies will address existing operation and maintenance goals for current military system and prognostics health management algorithms for advanced engines.