Abstract
Monitoring the health of automotive components is one of the challenging and important task for accident and fatigue free functioning of the vehicle. Structural health monitoring of automotive components at periodic intervals or even at real time, helps to rectify the deterioration of component life. With the help of health monitoring techniques, failure predictions can be done and suitable measures can be taken to avoid such failures. The objective of this study is to establish a methodology to predict health/life of any structural component of a vehicle in use with the use of smart material such as piezo electric materials. This objective can be achieved with the use of EMI (Electro-Mechanical Impedance) measurement technique. In the paper various case studies and Proof of Concepts are presented. In all case studies, an impedance signature of the respective structure is captured and compared before and after the damage as well as with the propagation of damage/crack in the structure. A generic methodology is presented. Currently accurate results are obtained using LCR meter which is a bulky system to be installed in vehicle. The compact systems which uses AD5933 controller cannot give accurate results after certain extent. So the limitation of the study is the in lab testing of vehicle structural damage. Currently Health Monitoring systems are widely used in aerospace and civil industries. But in automotive domain with the use of smart materials and using EMI technique for heath monitoring concept development, case study building and actual field testing with implementation of smart structure on the vehicle components is the new and different thing to improve the vehicle performance. With the use of smart structures and EMI technique used for SHM, vehicle performance is improved. Other NDE methods are not possible with actual field testing of the vehicle. So, the EMI technique can be used effectively for vehicle health monitoring. Use of smart materials in automotive for health monitoring purpose would lead to future smart vehicles.