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.

When it comes to identifying model parameters such as damage growth parameters in Paris law for example, Bayesian inference is a popular method. However, it involves substantial computational cost, especially with increasing number of parameters. When the prior distribution for the parameters is not narrow, non-linear regression may provide almost all the benefits of Bayesian updating at a small fraction of the computational cost. In this paper we apply this approach to the identification of damage growth parameters.

This paper reviews the application of oil debris monitoring as an effective PHM solution for wind turbine gearboxes. The paper explains the common surface fatigue damage mode of bearing and gear rolling elements and the characteristics of the destructive debris that result from this damage mode. The paper outlines a simple means of deriving accumulated debris count damage limits based upon basic gearbox component geometry and the use of moving averages for estimating rates of debris generation as a simple yet effective damage data-driven propagation model.

Components that are part of an industrial process are normally degraded after some time in use. Efficient maintenance is of fundamental importance to the safety and economics of the plant operation. Predicting the future evolution of the degradation of components and estimating their remaining useful life are central elements for the implementation of optimal maintenance strategies. To achieve this, one must first identify the parameters providing a reliable indication of component performance or technical condition and then develop models for predicting their future trend.

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.

Understanding aging mechanisms of electronic components is of extreme importance in the aerospace domain where they are part of numerous critical subsystems including avionics. In particular, power MOSFETs are of special interest as they are involved in high voltage switching circuits such as drivers for electrical motors. With increased use of electronics in aircraft control, it becomes more important to understand the degradation of these components in aircraft specific environments.

This paper presents an empirical model to describe battery behavior during individual discharge cycles as well as over its cycle life. The basis for the form of the model has been linked to the internal processes of the battery and validated using experimental data. Subsequently, the model has been used in a Particle Filtering framework to make predictions of remaining useful life for individual discharge cycles as well as for cycle life. The prediction performance was found to be satisfactory as measured by performance metrics customized for prognostics.

Timed failure propagation graph (TFPG) is a causal model that captures the causal and temporal aspects of failure propagation in a wide variety of engineering systems. In this paper we investigate the problem of failure prognosis within the TFPG model settings. The paper introduces a formal definition for system reliability based on measures of failure criticality, proximity between alarm observations, and plausibility of the estimated current system condition.

Prognostics performance evaluation has gained significant attention in the past few years. As prognostics technology matures and more sophisticated methods for prognostic uncertainty management are developed, a standardized methodology for performance evaluation becomes extremely important to guide improvement efforts in a constructive manner. This paper is in continuation of previous efforts where several new evaluation metrics tailored for prognostics were introduced and were shown to effectively evaluate various algorithms as compared to other conventional metrics.

As the aviation industry evolves toward next generation fly-by-wire vehicles, hydraulic and electrohydrostatic actuators (EHA) are replaced with their electro-mechanical counterparts. By eliminating fluid leakage problems while reducing weight and enhancing vehicle control, the feasibility of electromechanical actuators (EMA) in avionic applications has been established. However, due to the inherent nature of electronic components and systems to fail, improved diagnostic and prognostic methods are sought to keep the all-electric aircraft safe.