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.

Machine remaining useful life (RUL) prediction is a key part of Condition-Based Maintenance (CBM), which provides the time evolution of the fault indicator so that maintenance can be performed to avoid catastrophic failures. This paper proposes a new RUL prediction method based on adaptive neuro-fuzzy inference systems (ANFIS) and high-order particle filtering, which predicts the time evolution of the fault indicator and computes the probability density function (pdf) of RUL.

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.

In this paper, effects preceding a latch-up fault in insulated gate bipolar transistors (IGBTs) are studied as they manifest within an electric motor drive system. Primary failure modes associated with IGBT latch-up faults are reviewed. Precursors to latch-up, primarily an increase in turn-off time and junction temperature, are examined for the IGBT. In addition, the relationship between junction temperature and turn-off time is explained by examining the semiconductor properties of an IGBT.

Abstract Most fault adaptive control research addresses the preservation of system stability or functionality in the presence of a specific failure (fault). This paper examines the fault adaptive control problem for a generic class of incipient failure modes, which do not initially affect system stability, but will eventually cause a catastrophic failure to occur. The risk of catastrophic failure due a component fault mode is some monotonically increasing function of the load on the component.

Anomaly detection is the identification of abnormal system behavior, in which a model of normality is constructed, with deviations from the model identified as “abnormal”. Complex high-integrity systems typically operate normally for the majority of their service lives, and so examples of abnormal data may be rare in comparison to the amount of available normal data. Anomaly detection is particularly suited for Intelligent Fault diagnosis of such systems since it allows previously-unseen or poorly understood modes of failure to be correctly identified.

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic schemes (both model-based and data-driven) that attempt to forecast machinery health by constructing health propagation models for the underlying systems. In particular, algorithms that use the data-driven approach learn models directly from the data, rather than using a hand-built model based on human expertise.

Accurate and reliable fault diagnosis and prognosis of safety or mission critical components/ subsystems in complex engineering systems present major challenges to the Condition-Based Maintenance (CBM) or Prognostic and Health Management (PHM) designer. A crucial step in the development of CBM/PHM strategies relates to the designer’s ability to understand and model the incipient failure or fault modes and mechanisms. A single fault growth model might not be often capable to capture a sequence of fault behaviors.

Complex engineering systems consist of many subsystems. Each of the subsystems is composed of a large number of components. While faults arise at component level, sensing capabilities are limited to subsystem level, and system operations and maintenance practices are scheduled based on system level paremeters. This paper presents a hierarchical architecture to analyze the effects of system level parameters on component level faults of dominant failure modes of a complex system. An aeropropulsion system of turbofan type has been used as the application domain.

Opportunities exist to apply nonlinear filtering to model-based prognostics in order to provide a systematic way of dealing with the propagation of system damage at some future time, whenever imprecise diagnostic information is obtained. Central to the prognostics problem is the ability to properly capture and manage uncertainties when predicting remaining useful life of a particular component of interest. The goal of this paper is to present a foundation for prediction and filtering of the failure process using nonlinear prognostic models and exact (finite-dimensional) filters.