This chapter reviewed generic prognosis algorithmic approaches and introduced some of the basics associated with probabilistic predictions and a required architecture for performing prognostics on critical aerospace systems. Prognosis is a critical element of a HM system and has the promise to realize major benefits for cost avoidance and safety improvement for fielded systems. It also presents a number of challenges to the HM system designer, primarily due to the need to properly model damage progression and to deal with large-grain uncertainty. Long-term prediction of a fault’s evolution to the point thatmay result in a failure requires means to represent and manage the inherent uncertainty. Moreover, accurate and precise prognosis demands good models of the fault growth and statistically sufficient samples of failure data to assist in training, validating, and fine tuning prognostic algorithms. Prognosis performance metrics, robust algorithms, and test platforms that may provide needed data have been the target of HM researchers in the recent past. Many accomplishments have been reported but major challenges still remain to be addressed. To address the issue of inherent uncertainties that are the aggregate of many unknowns and can result in considerable prediction variability, the concept of adaptive prognosis was introduced. In that case, available, albeit imperfect, information is used to update elements of the prognostic model. Only one of many approaches for accomplishing this was briefly introduced, namely, the particle filter. Other statistical update techniques include Bayesian updating, constrained optimization, and Kalman filtering. The design process is not a trivial process by which features and models are chosen for integration such that the best possible prediction on RUL still is obtained. It takes substantial effort to design systems so that measured data can be fused and used in conjunction with physics-based models to estimate current and future damage states. This is exacerbated when multiple models are employed that may use different feature inputs. The prognosis system must also be capable of intelligently calibrating a priori initial conditions (e.g., humidity, strain, and temperature) and random variable characteristics in an automated yet lucid process.

As fault diagnosis and prognosis systems in aerospace applications become more capable, the ability to utilize information supplied by them becomes increasingly important. While certain types of vehicle health data can be effectively processed and acted upon by crew or support personnel, others, due to their complexity or time constraints, require either automated or semi-automated reasoning. Prognostics-enabled Decision Making (PDM) is an emerging research area that aims to integrate prognostic health information and knowledge about the future operating conditions into the process of selecting subsequent actions for the system. The newly developed PDM algorithms require suitable software and hardware platforms for testing under realistic fault scenarios. The paper describes the development of such a platform, based on the K11 planetary rover prototype. A variety of injectable fault modes are being investigated for electrical, mechanical, and power subsystems of the testbed, along with methods for data collection and processing. In addition to the hardware platform, a software simulator with matching capabilities has been developed. The simulator allows for prototyping and initial validation of the algorithms prior to their deployment on the K11. The simulator is also available to the PDM algorithms to assist with the reasoning process. A reference set of diagnostic, prognostic, and decision making algorithms is also described, followed by an overview of the current test scenarios and the results of their execution on the simulator.

With recent advancements in prognostics methodologies there has been a significant interest in maturing Prognostics and Health Management (PHM) to increase its technology readiness level for onboard deployments. Active research is underway both in industry and academia to address shortcomings in availability of run-to-failure data, accelerated aging environments, real-time prognostics algorithms, uncertainty representation and management (URM) techniques, prognostics performance evaluation, etc., to name a few. At this juncture it is highly desirable to close the loop by connecting the high level customer requirements for mission planning and execution to performance specifications for prognostics methodologies at the lower technical level. This calls for integrating the pragmatics of safety, reliability, cost, and real-time viability into the prognostics methodologies to establish a connection between top-down and bottom-up approaches currently pursued in the PHM community. In this paper we identify key areas that must be addressed to bridge these gaps and provide an overview of how these areas have been addressed in part at various levels. We also discuss on how these issues can be further developed into a comprehensive and more coherent portfolio of technologies that will ultimately lead to specifying guidelines for prognostics performance.

Prognostics and health management (PHM) is a maturing system engineering discipline. As with most maturing disciplines, PHM does not yet have a universally accepted research methodology. As a result, most component life estimation efforts are based on ad-hoc experimental methods that lack statistical rigor. In this paper, we provide a critical review of current research methods in PHM and contrast these methods with standard research approaches in a more established discipline (medicine). We summarize the developmental steps required for PHM to reach full maturity and to generate actionable results with true business impact.

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.

This dataset describes the degradation of an aircraft engine. The dataset was used for the prognostics challenge competition at the International Conference on Prognostics and Health Management (PHM08). The challenge is still open for the researchers to develop and compare their efforts against the winners of the challenge in 2008. Data sets consist of multiple multivariate time series. Each data set is further divided into training and test subsets. Each time series is from a different aircraft engine – i.e., the data can be considered to be from a fleet of engines of the same type. Each engine starts with different degrees of initial wear and manufacturing variation which is unknown to the user. This wear and variation is considered normal, i.e., it is not considered a fault condition. There are three operational settings that have a substantial effect on engine performance. These settings are also included in the data. The data are contaminated with sensor noise.

A proactive approach to identifying and alleviating life-threatening conditions in the aviation system entails a well-defined process of identifying threats, evaluating causes, assessing risks, and implementing appropriate solutions. This process is not a trivial undertaking. It requires continuous monitoring of system performance in a non-punitive culture; learning from normal operational experience; comparing actual performance to expected performance; identifying the precursor events and conditions that foreshadow most accidents; designing appropriate interventions to minimize the risk of their occurrence; and having a system in place to monitor the efficacy of the interventions.

This paper introduces a novel Prognostics-enhanced Automated Contingency Management (or ACM+P) paradigm based on both current health state (diagnosis) and future health state estimates (prognosis) for advanced autonomous systems. Including prognostics in ACM system allows not only fault accommodation, but also fault mitigation via proper control actions based on short term prognosis, and moreover, the establishment of a long term operational plan that optimizes the utility of the entire system based on long term prognostics. Technical challenges are identified and addressed by a hierarchical ACM+P architecture that allows fault accommodation and mitigation at various levels in the system ranging from component level control reconfiguration, system level control reconfiguration, to high level mission re-planning and resource redistribution. The ACM+P paradigm was developed and evaluated in a high fidelity Unmanned Aerial Vehicle (UAV) simulation environment with flight-proven baseline flight controller and simulated diagnostics and prognostics of flight control actuators. Simulation results are presented. The ACM+P concept, architecture and the generic methodologies presented in this paper are applicable to many advanced autonomous systems such as deep space probes, unmanned autonomous vehicles, and military and commercial aircraft.

Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain knowledge of the system, its components, and how they fail by casting the underlying physical phenomena in a physics-based model that is derived from first principles. Uncertainty cannot be avoided in prediction, therefore, algorithms are employed that help in managing these uncertainties. The particle filtering algorithm has become a popular choice for model-based prognostics due to its wide applicability, ease of implementation, and support for uncertainty management. We develop a general model-based prognostics methodology within a robust probabilistic framework using particle filters. As a case study, we consider a pneumatic valve from the Space Shuttle cryogenic refueling system. We develop a detailed physics-based model of the pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach and evaluate its effectiveness and robustness. The approach is demonstrated using historical pneumatic valve data from the refueling system.

The work described herein is aimed to advance prognostic health management solutions for electro-mechanical actuators and, thus, increase their reliability and attractiveness to designers of the next generation aircraft and spacecraft. In pursuit of this goal the team adopted a systematic approach by starting with EMA FMECA reviews, consultations with EMA manufacturers, and extensive literature reviews of previous efforts. Based on the acquired knowledge, nominal/off-nominal physics models and prognostic health management algorithms were developed. In order to aid with development of the algorithms and validate them on realistic data, a testbed capable of supporting experiments in both laboratory and flight environment was developed. Test actuators with architectures similar to potential flight-certified units were obtained for the purposes of testing and realistic fault injection methods were designed. Several hundred fault scenarios were created, using permutations of position and load profiles, as well as fault severity levels. The diagnostic system was tested extensively on these scenarios, with the test results demonstrating high accuracy and low numbers of false positive and false negative diagnoses. The prognostic system was utilized to track fault progression in some of the fault scenarios, predicting the remaining useful life of the actuator. A series of run-to-failure experiments were conducted to validate its performance, with the resulting error in predicting time to failure generally lesser than 10% error. While a more robust validation procedure would require dozens more experiments executed under the same conditions (and, consequently, more test articles destroyed), the current results already demonstrate the potential for predicting fault progression in this type of devices. More prognostic experiments are planned for the next phase of this work, including investigation and comparison of other prognostic algorithms (such as various types of Particle Filter and GPR), addition of new fault types, and execution of prognostic experiments in flight environment.