Optimality and Comparative Analysis of Transducers for Proving Ground Correlation for a Two-Wheeler

Durability qualification of vehicle components includes proving ground testing as a crucial step. These tests simulate customer roads with heightened severity to expedite damage accumulation. Establishing a correlation between proving grounds and actual road conditions is essential, considering both track mix and mileage. The demands of heightened competition and abbreviated product development timelines necessitate the development of an optimized test matrix. To achieve "first-time right" outcomes, cross-validation utilizing various sensor data is imperative. Using an accelerometer or linear potentiometer, the activity can be done within a shorter time interval and with optimal cost; however, this may result in a non-uniform damage correlation of the vehicle structure because of different transducer characteristics. Multiple strain gauges are generally used in parallel to verify the results, as they are directly calculated for damage output. The objective of this research investigation is to establish a systematic methodology for identifying the optimal number and selection strategy of strain gauges as primary transducers for track optimization. Three distinct sensor types were employed during the course of this study: strain gauges, accelerometers, and potentiometers. Road load data was acquired using a two-wheeler on proving ground surfaces and diverse customer roads. Based on the application of the relative damage spectrum method and best-fit algorithm, an optimal test matrix was constructed in multiple iterations with respect to number and selection scheme, and the resultant data were subsequently compared. The study revealed that segmenting the vehicle frame into multiple distinct zones and selecting at least the top four critical sets of strain gauges within each zone yielded enhanced convergence and correlation with the results obtained from other transducers. This approach significantly heightens confidence in the correlation between proving ground data and customer usage. Keywords: Mileage, Optimization, Strain Gauges, damage, Sensor, Proving Ground, Test Matrix, Transducers, two-wheelers, vehicle validation