Prognostics, which deals with predicting remaining useful life of components, subsystems, and systems, is a key tech- nology for systems health management that leads to improved safety and reliability with reduced costs. The prognostics problem is often approached from a component-centric view. However, in most cases, it is not specifically component life- times that are important, but, rather, the lifetimes of the sys- tems in which these components reside. The system-level prognostics problem can be quite difficult due to the increased scale and scope of the prognostics problem and the rela- tive lack of scalability and efficiency of typical prognostics approaches. In order to address these issues, we develop a distributed solution to the system-level prognostics prob- lem, based on the concept of structural model decomposi- tion. The system model is decomposed into independent submodels. Independent local prognostics subproblems are then formed based on these local submodels, resulting in a scalable, efficient, and flexible distributed approach to the system-level prognostics problem. We provide a formulation of the system-level prognostics problem and demonstrate the approach on a four-wheeled rover simulation testbed. The re- sults show that the system-level prognostics problem can be accurately and efficiently solved in a distributed fashion.

Failure prognosis - as a natural extension to the fault detection and isolation (FDI) problem - has become a key issue in a world where the economic impact of system reliability and cost-effective operation of critical assets is steadily increasing. Failure prognostic algorithms aim to characterize the evolution of incipient fault conditions in complex dynamic processes, thus allowing to estimate of the remaining useful life (RUL) of subsystems and components. Several examples can be used here to illustrate the range of possible applications for these algorithms: electro-mechanical systems, continuous-time manufacturing processes, structural damage analysis, and even fault tolerant software architectures. Most of them have in common the fact that they are highly complex, nonlinear, and affected by large-grain uncertainty. We introduce in this chapter an integrated failure prognosis architecture that is applicable to a variety of aircraft systems and industrial processes. We are targeting a specific rotorcraft system as a prototypical testbed for proof-of-concept. The overall architecture consists of an on-board and an off-board module for eventual on-platformimplementation purposes.

**Abstract** Prognostics solutions for mission critical systems require a comprehensive methodology for proactively detecting and isolating failures, recommending and guiding condition-based maintenance actions, and estimating in real time the remaining useful life of critical components and associated subsystems. A major challenge has been to extend the benefits of prognostics to include computer servers and other electronic components. The key enabler for prognostics capabilities is monitoring time series signals relating to the health of executing components and subsystems. Time series signals are processed in real time using pattern recognition for proactive anomaly detection and for remaining useful life estimation. Examples will be presented of the use of pattern recognition techniques for early detection of a number of mechanisms that are known to cause failures in electronic systems, including: environmental issues; software aging; degraded or failed sensors; degradation of hardware components; degradation of mechanical, electronic, and optical interconnects. Prognostics pattern classification is helping to substantially increase component reliability margins and system availability goals while reducing costly sources of "no trouble found" events that have become a significant warranty-cost issue. **Bios** Aleksey Urmanov is a research scientist at Sun Microsystems. He earned his doctoral degree in Nuclear Engineering at the University of Tennessee in 2002. Dr. Urmanov's research activities are centered around his interest in pattern recognition, statistical learning theory and ill-posed problems in engineering. His most recent activities at Sun focus on developing health monitoring and prognostics methods for EP-enabled computer servers. He is a founder and an Editor of the Journal of Pattern Recognition Research. Anton Bougaev holds a M.S. and a Ph.D. degrees in Nuclear Engineering from Purdue University. Before joining Sun Microsystems Inc. in 2007, he was a lecturer in Nuclear Engineering Department and a member of Applied Intelligent Systems Laboratory (AISL), of Purdue University, West Lafayette, USA. Dr. Bougaev is a founder and the Editor-in-Chief of the Journal of Pattern Recognition Research. His current focus is in reliability physics with emphasis on complex system analysis and the physics of failures which are based on the data driven pattern recognition techniques.

Prognostics has taken center stage in Condition Based Maintenance (CBM) where it is desired to estimate Remaining Useful Life (RUL) of a system so that remedial measures may be taken in advance to avoid catastrophic events or unwanted downtimes. Validation of such predictions is an important but difficult proposition and a lack of appropriate evaluation methods renders prognostics meaningless. Evaluation methods currently used in the research community are not standardized and in many cases do not sufficiently assess key performance aspects expected out of a prognostics algorithm. In this paper we introduce several new evaluation metrics tailored for prognostics and show that they can effectively evaluate various algorithms as compared to other conventional metrics. Four prognostic algorithms, Relevance Vector Machine (RVM), Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and Polynomial Regression (PR), are compared. These algorithms vary in complexity and their ability to manage uncertainty around predicted estimates. Results show that the new metrics rank these algorithms in a different manner; depending on the requirements and constraints suitable metrics may be chosen. Beyond these results, this paper offers ideas about how metrics suitable to prognostics may be designed so that the evaluation procedure can be standardized.

Diagnosis and prognosis are necessary tasks for system reconfiguration and fault-adaptive control in complex systems. Diagnosis consists of detec- tion, isolation and identification of faults, while prognosis consists of prediction of the remain- ing useful life of systems. This paper presents an integrated model-based distributed diagnosis and prognosis framework, where system decomposi- tion is used to perform the diagnosis and prog- nosis tasks in a distributed way. We show how different submodels can be automatically con- structed to solve the local diagnosis and prog- nosis problems. We illustrate our approach us- ing a simulated four-wheeled rover for different fault scenarios. Our experiments show that our approach correctly performs fault diagnosis and prognosis in a robust manner.

Diagnosis and prognosis are necessary tasks for system re- configuration and fault-adaptive control in complex systems. Diagnosis consists of detection, isolation and identification of faults, while prognosis consists of prediction of the remain- ing useful life of systems. This paper presents a novel inte- grated framework for model-based distributed diagnosis and prognosis, where system decomposition is used to enable the diagnosis and prognosis tasks to be performed in a distributed way. We show how different submodels can be automati- cally constructed to solve the local diagnosis and prognosis problems. We illustrate our approach using a simulated four- wheeled rover for different fault scenarios. Our experiments show that our approach correctly performs distributed fault diagnosis and prognosis in an efficient and robust manner.

As software and software intensive systems are becoming increasingly ubiquitous, the impact of failures can be tremendous. In some industries such as aerospace, medical devices, or automotive, such failures can cost lives or endan- ger mission success. Software faults can arise due to the inter- action between the software, the hardware, and the operating environment. Unanticipated environmental changes lead to software anomalies that may have significant impact on the overall success of the mission. Latent coding errors can at any time during system operation trigger faults despite the fact that usually a significant effort has been expended in verification and validation (V&V) of the software system. Nevertheless, it is becoming increasingly more apparent that pre-deployment V&V is not enough to guarantee that a com- plex software system meets all safety, security, and reliabil- ity requirements. Software Health Management (SWHM) is a new field that is concerned with the development of tools and technologies to enable automated detection, diagnosis, prediction, and mitigation of adverse events due to software anomalies, while the system is in operation. The prognos- tic capability of the SWHM to detect and diagnose failures before they happen will yield safer and more dependable systems for the future. This paper addresses the motivation, needs, and requirements of software health management as a new discipline and motivates the need for SWHM in safety critical applications.

A prognostic system makes it possible to anticipate loss of functionality before it occurs with sufficient lead time to take actions that mitigate the impact of this loss. We focus on the forms of mitigation within the flight vehicle that influence the operational dynamics but do not directly amend the mission plan. Thus, we focus upon the reconfiguration of the feedback control strategy for the flight system. The high degree of complexity in the design and dynamics of modern aircraft is typically handled using a hierarchical control scheme in which there are several levels of control at increasing levels of responsibility: the component level, the subsystem level, and the system level. Our reconfiguration strategy involves mitigating problems that are detected at the component level at both the level in which the fault is detected and higher levels as well. There are, thus, two subproblems to the reconfiguration: (a) an adaptive control problem at the lower level to extend component life and derive new component performance limits, and (b) a supervisory control problem at the higher level to adapt the system controller to maximize system capability while respecting the performance limitations. Since our reconfiguration occurs in the context of a dynamic system, we need to respect the stability implications of the reconfiguration. To address this, we apply bandwidth analyses at the component level and the systems level in a robust performance context. A conservative criterion for stability is to impose rate limits for reconfiguration that insure that undesired, and possibly unmodeled, modes of behavior are not driven by reconfiguration activities. For specific hardware, extensions beyond this conservative approach may be warranted (e.g. to catch faulty behavior) and validated on a case-by-case basis, essentially by extending the component modeling to include a model of behavior under certain types of reconfiguration.

The success of model-based approaches to systems health management depends largely on the quality of the underly- ing models. In model-based prognostics, it is especially the quality of the damage progression models, i.e., the models describing how damage evolves as the system operates, that determines the accuracy and precision of remaining useful life predictions. Several common forms of these models are gen- erally assumed in the literature, but are often not supported by physical evidence or physics-based analysis. In this paper, using a centrifugal pump as a case study, we develop differ- ent damage progression models. In simulation, we investigate how model changes influence prognostics performance. Re- sults demonstrate that, in some cases, simple damage progres- sion models are sufficient. But, in general, the results show a clear need for damage progression models that are accurate over long time horizons under varied loading conditions.

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. This risk of catastrophic failure due a component fault mode is some monotonically increasing function of the load on the component. Assuming that a probabilistic prognostic model is available to evaluate the risk of incipient fault modes growing into catastrophic failure conditions, then fundamentally the fault adaptive control problem is to adjust component loads to minimize risk of failure, while not overly degrading nominal performance. A methodology is proposed for posing this problem as a finite horizon constrained optimization, where constraints correspond to maximum risk of failure and maximum deviation from nominal performance. Development of the methodology to handle a general class of overactuated systems is given. Also, the fault adaptive control methodology is demonstrated on an application example of practical significance, an electro-mechanical actuator (EMA) consisting of three DC motors geared to the same output shaft. Similar actuator systems are commonly used in aerospace, transportation, and industrial processes to actuate critical loads, such as aircraft control surfaces. The fault mode simulated in the system is a temperature dependent motor winding insulation degradation.