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A methodology is presented to do accelerated vibration durability test, on Electro Dynamic Shaker (EDS) by using Power Spectral Density (PSD) profile based on typical customer usage pattern. A generalized iterative procedure is developed to optimize input excitation PSD profile on EDS for simulating the exact customer usage conditions. The procedure minimizes the error between the target channels measured on road and the response channels measured on EDS. Also, response of accelerometers and strain gauges at multiple locations on the test component are arrived at based on a single input excitation using this procedure. The same is verified experimentally as well. Different parameters like strain, acceleration, etc. are simulated simultaneously. This methodology has enabled successful simulation of road conditions in lab, thereby arriving at a correlation between rig and road. The correlation obtained is based on the simulation of the same failure mode as that of the road on the rig.

Accurate durability prediction is an important requirement in today's automobile industry. To achieve the same, it is imperative to have a good estimation of time histories of strains, accelerations etc. at various locations on the vehicle structure. This is usually difficult to obtain as a typical data acquisition exercise takes lots of time, cost and effort. This paper aims to address this problem by predicting the strain time histories accurately at various locations on the vehicle chassis from a few channels of measured data using Artificial Neural Networks (ANN). The predicted strain histories were found to be quite accurate as the error in fatigue lives between the measured and the thus predicted time histories at various strain locations were found to be less than 15%. This approach was found to be very useful in collecting huge amounts of customer usage data with minimum instrumentation and small sized data loggers.