Systems health monitoring is essential in guar- anteeing the safe, efficient, and correct opera- tion of complex engineered systems. Diagnosis, which consists of detection, isolation and identi- fication of faults; and prognosis, which consists of prediction of the remaining useful life of com- ponents, subsystems, or systems; constitute sys- tems health monitoring. This paper presents an integrated model-based diagnostic and prognos- tic framework, where we make use of a com- mon modeling paradigm to model both the nom- inal and faulty behavior in all aspects of systems health monitoring. We illustrate our approach us- ing a simulated propellant loading system that in- cludes tanks, valves, and pumps.

Prognostics methodologies determine the health state of a system and predict the end of life and remaining useful life. This information enables operators to take maintenance related decisions, thus effectively streamlining operational and mission-level activities. Prognostics testbeds help in the prototyping, development, and maturation of prognostic technologies. In this work, we present a prognostics testbed for pneumatic valves. Pneumatic valves are critical components in many industrial processes, and the testbed will be used to showcase how remaining life prediction works in the context of cryogenic refueling operations. The testbed allows for the injection of time-varying leaks with specified damage progression profiles in order to emulate common valve faults. In addition, the testbed contains a battery used to power some components, allowing the study of the effects of battery degradation on the operation of the valves. Prognostic algorithms will utilize sensor data collected from the different transducers in order to estimate component health and make life predictions, based on mathematical models describing the underlying physics of component degradation and employing a Bayesian filtering algorithm for state-parameter estimation from which life predictions are made.

This chapter presents theoretical and practical aspects associated to the implementation of a combined model-based/data-driven approach for failure prognostics based on particle filtering algorithms, in which the current esti- mate of the state PDF is used to determine the operating condition of the system and predict the progression of a fault indicator, given a dynamic state model and a set of process measurements. In this approach, the task of es- timating the current value of the fault indicator, as well as other important changing parameters in the environment, involves two basic steps: the predic- tion step, based on the process model, and an update step, which incorporates the new measurement into the a priori state estimate. This framework allows to estimate of the probability of failure at future time instants (RUL PDF) in real-time, providing information about time-to- failure (TTF) expectations, statistical confidence intervals, long-term predic- tions; using for this purpose empirical knowledge about critical conditions for the system (also referred to as the hazard zones). This information is of paramount significance for the improvement of the system reliability and cost-effective operation of critical assets, as it has been shown in a case study where feedback correction strategies (based on uncertainty measures) have been implemented to lengthen the RUL of a rotorcraft transmission system with propagating fatigue cracks on a critical component. Although the feed- back loop is implemented using simple linear relationships, it is helpful to provide a quick insight into the manner that the system reacts to changes on its input signals, in terms of its predicted RUL. The method is able to manage non-Gaussian pdf’s since it includes concepts such as nonlinear state estimation and confidence intervals in its formulation. Real data from a fault seeded test showed that the proposed framework was able to anticipate modifications on the system input to lengthen its RUL. Results of this test indicate that the method was able to successfully suggest the correction that the system required. In this sense, future work will be focused on the development and testing of similar strategies using different input-output uncertainty metrics.

Within systems health management, prognostics focuses on predicting the remaining useful life of a system. In the model-based prognostics paradigm, physics-based models are constructed that describe the operation of a system, and how it fails. Such approaches consist of an estimation phase, in which the health state of the system is first identified, and a prediction phase, in which the health state is projected forward in time to determine the end of life. Centralized solutions to these problems are often computationally expensive, do not scale well as the size of the system grows, and introduce a single point of failure. In this paper, we propose a novel distributed model-based prognostics scheme that formally describes how to decompose both the estimation and prediction problems into computationally-independent local subproblems whose solutions may be easily composed into a global solution. The decomposition of the prognostics problem is achieved through structural decomposition of the underlying models. The decomposition algorithm creates from the global system model a set of local submodels suitable for prognostics. Computationally independent local estimation and prediction problems are formed based on these local submodels, resulting in a scalable distributed prognostics approach that allows the local subproblems to be solved in parallel, thus offering increases in computational efficiency. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the distributed approach, compare the performance with a centralized approach, and establish its scalability.

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.

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.

Prognostics and Health Management (PHM) principles have considerable promise to change the game of lifecycle cost of engineering systems at high safety levels by providing a reliable estimate of future system states. This estimate is a key for planning and decision making in an operational setting. While technology solutions have made considerable advances, the tie-in into the systems engineering process is lagging behind, which delays fielding of PHM-enabled systems. The derivation of specifications from high level requirements for algorithm performance to ensure quality predictions is not well developed. From an engineering perspective some key parameters driving the requirements for prognostics performance include: (1) maximum allowable Probability of Failure (PoF) of the prognostic system to bound the risk of losing an asset, (2) tolerable limits on proactive maintenance to minimize missed opportunity of asset usage, (3) lead time to specify the amount of advanced warning needed for actionable decisions, and (4) required confidence to specify when prognosis is sufficiently good to be used. This paper takes a systems engineering view towards the requirements specification process and presents a method for the flowdown process. A case study based on an electric Unmanned Aerial Vehicle (e-UAV) scenario demonstrates how top level requirements for performance, cost, and safety flow down to the health management level and specify quantitative requirements for prognostic algorithm performance.

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.

Reliable systems health management is an important research area of NASA. A health management system that can accurately and quickly diagnose faults in various on-board systems of a vehicle will play a key role in the success of current and future NASA missions. We introduce in this paper the ProDiagnose algorithm, a diagnostic algorithm that uses a probabilistic approach, accomplished with Bayesian Network models compiled to Arithmetic Circuits, to diagnose these systems. We describe the ProDiagnose algorithm, how it works, and the probabilistic models involved. We show by experimentation on two Electrical Power Systems based on the ADAPT testbed, used in the Diagnostic Challenge Competition (DX-09), that ProDiagnose can produce results with over 96% accuracy and < 1 second mean diagnostic time. **Reference:** B. W. Ricks, and O. J. Mengshoel. "The Diagnostic Challenge Competition: Probabilistic Techniques for Fault Diagnosis in Electrical Power Systems." Proc. of the 20th International workshop on Principles of Diagnosis (DX-09) Stockholm, Sweden, 2009 **BibTex Reference:** @inproceedings{ricks09diagnostic, author = {Ricks, B. W. and Mengshoel, O. J.}, title = {The Diagnostic Challenge Competition: Probabilistic Techniques for Fault Diagnosis in Electrical Power Systems}, booktitle = {Proc. of the 20th International Workshop on Principles of Diagnosis (DX-09)}, address = {Stockholm, Sweden}, year = {2009} }

Prognostics technologies determine the health state of a system and predict its remaining useful life. With this information, operators are able to make maintenance-related decisions, thus effectively streamlining operational and mission level activities. Experimentation on testbeds representative of critical systems is very useful for the maturation of prognostics technology; precise emulation of actual fault conditions on such a testbed further validates these technologies. In this paper we present the development of a pneumatic valve testbed, initial experimental results and progress towards the maturation and validation of component-level prognostic methods in the context of cryogenic refueling operations. The pneumatic valve testbed allows for the injection of time-varying leaks with specified damage progression profiles in order to emulate common valve faults. The pneumatic valve testbed also contains a battery used to power some pneumatic components, enabling the study of the effects of battery degradation on the operation of the valves.