This paper proposes use of passive thermal age sensors and empirical correlation models to project remaining useful life of thermally degradable products and materials. Thermal age sensors, comprising a selected polymeric matrix and conductive fillers, change resistance as the matrix thermally degrades in the same thermal environment as the monitored product or material. Thermal age sensor resistance represents the integrated time-temperature condition of the sensor at its characteristic activation energy. Empirical models correlate sensor resistance to a selected property of the material utilizing multi-temperature thermal aging data of the monitored material. These correlation models project the current condition of the selected product property, or, if end-of-life properties are specified, these models project the percentage of remaining design life of the material. Several applications of this approach are discussed utilizing thermal age sensors attached to monitored materials. An approach utilizing two thermal age sensors is introduced that allows a single tag to predict selected properties of many different materials. PHM tags utilizing passive thermal age sensors do not require an internal source of electrical power or internal memory, eliminating the need for batteries and significantly reducing data management issues. This approach can be expanded to a wide range of products and materials when sufficient thermal age data is available.
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PHM, thermal age, thermal age sensor, empirical models, thermal age tags, product condition, condition monitoring, predictive maintenance, product life, shelf life, thermal degradation, RFID, passive RFID, remaining life, polymeric products, conductive composites, data logger
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