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Proving grounds are an extremely efficient means of qualifying the durability of vehicle components. They accelerate damage accumulation rates so failures are detectable in a very short period of time. It is important that proving ground damage is correlated with target customer usage. It is also important to determine the most efficient use of the proving ground in order to meet project targets and minimize overall development costs. This paper describes the latest techniques for proving ground correlation and optimization. Acceleration, strain, wheel force and other types of data are collected on a vehicle as it traverses different proving ground surfaces. Comparable data are also collected from instrumented ‘customer’ vehicles. The objective of the analysis is to determine which mixture of proving ground surfaces offers the best representation of customer usage while minimizing the total test time.

Inferential sensing, as it relates to the equipment operator, can be viewed as human intuition [1]. The person operating the equipment can sense there is something wrong while their intuition tells them when and what needs troubleshooting and repair. Attempts have been made to implement this human intuition model to monitor a vehicle operation and detect abnormalities. In many approaches traditional sensors are added to the vehicle which increases cost, complexity, and another failure point. After years of developments and techniques, there are few highly reliable on-board systems that can duplicate the human intuition model since the specific failure cannot be directly measured but must be inferred from a variety of symptoms. This paper describes an engineering approach using Physics of Failure (PoF) for specific subsystems, developing the applicable fatigue models, and then collecting, monitoring, and manipulating the real-time on-vehicle data to complement the “operator intuition”.