It is an inescapable truth that no matter how well a system is designed it will degrade, and if degrading parts are not repaired or replaced the system will fail. Avoiding the expense and safety risks associated with system failures is certainly a top priority in many systems; however, there is also a strong motivation not to be overly cautious in the design and maintenance of systems, due to the expense of maintenance and the undesirable sacrifices in performance and cost effectiveness incurred when systems are over designed for safety.

The ability to utilize prognostic system health information in operational decision making, especially when fused with information about future operational, environmental, and mission requirements, is becoming desirable for both manned and unmanned aerospace vehicles. A vehicle capable of evaluating its own health state and making (or assisting the crew in making) decisions with respect to its system health evolution over time will be able to go further and accomplish more mission objectives than a vehicle fully dependent on human control.

Development of robust structural health monitoring (SHM) sensors and hardware alone is not sufficient to achieve desired benefits such as improved asset availability and reduced sustainment costs. For SHM systems to be practically deployed as part of an integrated system health management (ISHM), tools must be created for SHM life-cycle management (LCM). To that end, SHM-LCM software has been developed to expedite the adoption of SHM into ISHM. The SHM-LCM software is a flexible application intended to manage the cradle-to-grave life-cycle of an SHM system for generic applications.

Among systems that provide sensor data of their performance, one approach to prognostic estimation is forecasting, i.e. prediction of measurable parameters and comparison of predicted values against established operational limits. Forecasting can be attempted statistically, or can be based on rigorous physical simulation. However, combining these approaches is difficult where system mode behavior or timing of system activities is uncertain, limiting the accuracy or applicability of a forecast.

This paper presents a novel set of uncertainty measures to quantify the impact of input uncertainty on nonlinear prognosis systems. A Particle Filtering-based method is also presented that uses this set of uncertainty measures to quantify, in real time, the impact of load, environmental, and other stresses for long-term prediction. Furthermore, this work shows how these measures can be used to implement a novel feedback correction loop aimed to suggest modifications, at a system input level, with the purpose of extending the remaining useful life of a faulty nonlinear, non-Gaussian system.

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.

Operation and maintenance of offshore wind farms will be more difficult and expensive than equivalent onshore wind farms. Accessibility for routine servicing and maintenance will be a concern: there may be times when the offshore wind farm is inaccessible due to sea, wind and visibility conditions. Additionally, maintenance tasks are more expensive than onshore due to: distance of the offshore wind farm from shore, site exposure, and the need for specialized lifting equipment to install and change out major components.

One of the most prominent technical challenges to effective deployment of health management systems is the vast difference in user objectives with respect to engineering development. In this paper, a detailed survey on the objectives of different users of health management systems is presented. These user objectives are then mapped to the metrics typically encountered in the development and testing of two main systems health management functions: diagnosis and prognosis.

Prognostic performance evaluation has gained significant attention in the past few years.Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end-user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few. The research community has used a variety of metrics largely based on convenience and their respective requirements.

This paper proposes a model based approach for prognosis of DC-DC power converters. We briefly review the prognosis process, and present an overview of different approaches that have been developed. We study the effects of capacitor degradation on DC-DC converter performance by developing a combination of a thermal model for ripple current effects and a physics of failure model of the thermal effects on capacitor degradation. The derived degradation model of the capacitor is reintroduced into the DC-DC converter model to study changes in the system performance using Monte Carlo methods.