New Capacitive Thermal Age Tag for Predicting Remaining Thermal Life of Multiple Products

This paper proposes use of a new capacitive thermal age sensor that inherently integrates time and temperature without batteries or electronic memory to predict the remaining thermal life of a wide range of monitored products. The sensor is a tiny capacitor comprising a polymeric dielectric between two conductive plates. Capacitance of the sensor increases during thermal aging due to shrinkage of the polymer. Additives such as catalysts adjust the activation energy (Ea) of capacitance change with thermal age.A thermal age tag, incorporating two capacitive sensors of different activation energy, can be used to determine the effective temperature (Teff) of a complex thermal environment at wide range of product degradation activation energies. Correlation of the thermal age of the tag at the monitored product’s degradation activation energy to product thermal aging data provides estimated remaining thermal life of the product. The thermal age tag requires no batteries or electronic memory required in data-logging approaches resulting in reduced size, weight and cost. These passive tags are potentially maintenance free for the life of the product.This paper describes the development of a universal thermal age (UTA) tag incorporating capacitive thermal age sensors and preliminary co-aging trials with a variety of selected polymeric products to demonstrate feasibility of this approach.

Use of Passive Age Sensors for Projecting Remaining Thermal Life of Materials

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.