**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.

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. This paper describes an analytical process that starts with the derivation of an expression to evaluate the desirability of future control outcomes, and eventually produces control routines that use uncertain prognostic information to optimize derived risk metrics. A case study on the design of fault-adaptive control for a skid-steered robot will illustrate some of the fundamental challenges of prognostics-based control design.

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.

The loading of spacecraft propellants is a complex, risky operation. Therefore, diagnostic solutions are neces- sary to quickly identify when a fault occurs, so that recov- ery actions can be taken or an abort procedure can be initi- ated. Model-based diagnosis solutions, established using an in-depth analysis and understanding of the underlying physi- cal processes, offer the advanced capability to quickly detect and isolate faults, identify their severity, and predict their ef- fects on system performance. We develop a physics-based model of a cryogenic propellant loading system, which de- scribes the complex dynamics of liquid hydrogen filling from a storage tank to an external vehicle tank, as well as the in- fluence of different faults on this process. The model takes into account the main physical processes such as highly non- equilibrium condensation and evaporation of the hydrogen vapor, pressurization, and also the dynamics of liquid hydro- gen and vapor flows inside the system in the presence of he- lium gas. Since the model incorporates multiple faults in the system, it provides a suitable framework for model-based di- agnostics and prognostics algorithms. Using this model, we analyze the effects of faults on the system, derive symbolic fault signatures for the purposes of fault isolation, and per- form fault identification using a particle filter approach. We demonstrate the detection, isolation, and identification of a number of faults using simulation-based experiments.

As new technologies are developed to handle the complexities of the Next Generation Air Transportation System (NextGen), it is increasingly important to address both current and future safety concerns along with the operational, environmental, and efficiency issues within the National Airspace System (NAS). In recent years, the Federal Aviation Administration’s (FAA) safety offices have been researching ways to utilize the many safety databases maintained by the FAA, such as those involving flight recorders, radar tracks, weather, and many other high-volume sensors, in order to monitor this unique and complex system. Although a number of current technologies do monitor the frequency of known safety risks in the NAS, very few methods currently exist that are capable of analyzing large data repositories with the purpose of discovering new and previously unmonitored safety risks. While monitoring the frequency of known events in the NAS enables mitigation of already identified problems, a more proactive approach of finding unidentified issues still needs to be addressed. This is especially important in the proactive identification of new, emergent safety issues that may result from the planned introduction of advanced NextGen air traffic management technologies and procedures. Development of an automated tool that continuously evaluates the NAS to discover both events exhibiting flight characteristics indicative of safety-related concerns as well as operational anomalies will heighten the awareness of such situations in the aviation community and serve to increase the overall safety of the NAS. This paper discusses the extension of previous anomaly detection work to identify operationally significant flights within the highly complex airspace encompassing the New York area of operations, focusing on the major airports of Newark International (EWR), LaGuardia International (LGA), and John F. Kennedy International (JFK). In addition, flight traffic in the vicinity of Denver International (DEN) airport/airspace is also investigated to evaluate the impact on operations due to variances in seasonal weather and airport elevation. From our previous research, subject matter experts determined that some of the identified anomalies were significant, but could not reach conclusive findings without additional supportive data. To advance this research further, causal examination using domain experts is continued along with the integration of air traffic control (ATC) voice data to shed much needed insight into resolving which flight characteristic(s) may be impacting an aircraft's unusual profile. Once a flight characteristic is identified, it could be included in a list of potential safety precursors. This paper also describes a process that has been developed and implemented to automatically identify and produce daily reports on flights of interest from the previous day.

Prognostics is an engineering discipline that focuses on estimation of the health state of a component and the prediction of its remaining useful life (RUL) before failure. Health state estimation is based on actual conditions and it is fundamental for the prediction of RUL under anticipated future usage. Failure of electronic devices is of great concern as future aircraft will see an increase of electronics to drive and control safety-critical equipment throughout the aircraft. Therefore, development of prognostics solutions for electronics is of key importance. This paper presents an accelerated aging system for gate-controlled power transistors. This system allows for the understanding of the effects of failure mechanisms, and the identification of leading indicators of failure which are essential in the development of physics-based degradation models and RUL prediction. In particular, this system isolates electrical overstress from thermal overstress. Also, this system allows for a precise control of internal temperatures, enabling the exploration of intrinsic failure mechanisms not related to the device packaging. By controlling the temperature within safe operation levels of the device, accelerated aging is induced by electrical overstress only, avoiding the generation of thermal cycles. The temperature is controlled by active thermal-electric units. Several electrical and thermal signals are measured in-situ and recorded for further analysis in the identification of leading indicators of failures. This system, therefore, provides a unique capability in the exploration of different failure mechanisms and the identification of precursors of failure that can be used to provide a health management solution for electronic devices.

Electrical power systems play a critical role in spacecraft and aircraft, and they exhibit a rich variety of failure modes. This paper discusses electrical power system fault diagnosis by means of probabilistic techniques. Specically, we discuss our development of a diagnostic capability for an electrical power system testbed, ADAPT, located at NASA Ames. We emphasize how we have tackled two challenges, regarding modelling and real-time performance, often encountered when developing diagnostic applications. We carefully discuss our Bayesian network modeling approach for electrical power systems. To achieve real-time performance, we build on recent theoretically well-founded developments that compile a Bayesian network into an arithmetic circuit. Arithmetic circuits have low footprint and are optimized for embedded, real-time systems such as spacecraft and aircraft. We discuss our probabilistic diagnostic models developed for ADAPT along with successful experimental results.

Aviation safety systems have undergone dramatic changes over the past fifty years. If you take a look at the early technology in this area, you'll see that there was a lot of work done in the area of so-called 'built-in-testing' (BIT) which essentially tests connectivity between different components. Technology has moved forward very far since that time. With the massive storage systems and advanced sensors and other communications systems, we are now able to capture and store vast quantities of health and control related data. This data is usually stored off-line for future analysis. In many cases, we also have an abundance of human-written text reports that relate to known safety issues. A key problem is to 'look' across all these data sources in order to find precursors to safety events. Although humans do look at many aspects of the data, it is difficult, if not impossible for them to integrate all the information available in a meaningful way. Other industries face this glut of data in their own way. Businesses have invested heavily in business intelligence products based on data mining that are designed to convert data into actionable information to maximize their profits or other metrics. The IVHM project is investing in data mining technologies to help sift through these massive data sets to uncover actionable information from a safety perspective. I've attached a presentation that Irv Statler and I gave at NASA HQ on this subject during an Aeronautics Technical Seminar. A video of the talk is also included. It's about 90 minutes long, so grab some popcorn. :)

This chapter described at a very high level some of the considerations that need to be made when designing algorithms for a vehicle health management application. The choices made here affect the quality of the diagnosis and prognosis (covered in Chapter 7). Therefore, the algorithmic design choices are made in conjunction with the design choices for diagnostics and prognostics to optimally support these tasks. Furthermore, additional considerations imposed by computational constraints, resource availability, algorithm maintenance, need for algorithm re-tuning, etc. will impact the solutions. It should also be noted that technological advances, both in hardware and software, impose the need for new solutions. For example, as new materials and new sensors are being developed, the algorithmic solutions will need to follow suit. In general, there seems to be a trend to have more sensor data available. While this is potentially a good thing, sensor data provides value only when it is being processed and interpreted properly, in part by the techniques described here. Testing of the methods, however, requires the “right” kind of data. Generally, there is a lack of seeded fault data which are required to train and validate algorithms. It is also important to migrate information from the component to the subsystem to the system levels so that health management technologies can be applied effectively and efficiently at the vehicle level. It may be required to perform elements described in this chapter between different levels of the vehicle architecture.