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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”.