Computational Studies on the Self-Sensing Inverse Problem Enhanced with Sensor Data Fusion

Many equipment operators and inspectors today make use of time-based maintenance strategies to ensure proper working order of engineering systems and structures. This approach can be costly and inefficient because inspections and maintenance are not targeted, and equipment downtime can be lengthy during this process. Condition-based maintenance, on the other hand, involves monitoring the condition of critical components or structures so that targeted maintenance is performed only when the parts being monitored exhibit signs indicating the end of its operational life. Shifting toward more efficient condition-based maintenance strategies for mechanical systems requires material state awareness (MSA) of the critical components and structures the system is comprised of. MSA of a structure involves attaining a thorough understanding of the structure's material properties, current mechanical state, and damage modes in order to estimate its remaining lifetime. Currently, many non-destructive evaluation (NDE) sensors and techniques are currently being developed and fielded for embedded sensing and condition monitoring applications, including in civil and aerospace structures. Among them, the self-sensing inverse problem (SSIP) is an emerging method that possesses great potential for providing MSA for piezoresistive materials. Piezoresistive materials exhibit a change in electrical conductivity when subject to strain, making them a prime candidate for manufacturing structures that have the innate ability to transduce its mechanical state. The SSIP is a mathematical method that recovers the continuous displacement and strain field of the deformed piezoresistive material from measured resistivity (or conductivity) changes. Being able to obtain the full-field displacements and strains of a component or structure is a key insight into its mechanical state, enabling accurate stress and failure analyses that can prove invaluable for condition-based maintenance. Computational and experimental demonstrations of the SSIP to date ...