This paper is concerned with the development of a health monitoring system for a pneumatic valve employed in pressure regulation systems. The proposed method is based on the statistical analysis of deviations of the controlled pressure signal from a baseline behavior. For this purpose, the Probability Integral Transform is employed to calculate an index of dissimilarity between the distributions of monitored and baseline data. The proposed method was applied to field records of 15 units, which were monitored during eight months.

Reliability is a key parameter for the development of safe and effective military vehicles with a reasonable life cycle cost. One innovative technology that is being promoted in the Department of Defense is the use of Health and Usage Monitoring Systems and remaining life prognostics to improve reliability and availability. The feasibility of using data collected from a limited set of existing and simple add-on sensors to make fatigue damage estimations on a complexly loaded component within a military wheeled vehicle system was investigated.

This paper describes the results of the author’s independent research on a Prognostics and Health Management (PHM) effort. Currently, the health assessment of any relatively complex electronic equipment is usually based on in-situ environment monitoring and the associated reliability computations of the failures experienced. Many complex systems incorporate a Power-On Self-Test (POST), or Built-In-Test (BIT), which tests most of the internal functionality and health of the Unit Under Test (UUT). For this paper, POST and BIT are synonymous and only BIT will be used.

Integrated System Health Management (ISHM) methodologies seek to enhance traditional component-level health management techniques to assess, diagnose, maintain, and prolong the health of complex target systems with large numbers of physical components that interact with each other in increasingly complex ways. Model-based reasoning solutions have the potential and demonstrate the promise to address some key challenges for ISHM.

The integration of structural health monitoring (SHM) systems into in-service applications has been hindered by the implied infrastructure, specifically wires for power and data from each sensor an acquisition and/or processing unit. Prior research by the present investigators has demonstrated a patented method of point-of-measurement datalogging for SHM, thereby greatly reducing the required quantity of cable by locally converting analog signals into digital data that can be placed on a common sensor-bus.

Prognostics has the potential to be very valuable in many industries. This is especially the case in the petroleum industry where the costs of tool failure are extremely high and continue to increase. Previous attempts have been made to predict the remaining useful life of drilling tools. While the developed methods were shown to be able to accurately predict the remaining useful life, the data requirement was such that they had limited or no viability in "real world" operations.