Prognostics is an emerging concept in condition basedmaintenance(CBM)ofcriticalsystems.Alongwith developing the fundamentals of being able to confidently predict Remaining Useful Life (RUL), the technology calls for fielded applications as it inches towards maturation. This requires a stringent performance evaluation so that the significance of the concept can be fully exploited. Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end-user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few issues. Instead, the research community has used a variety of metrics based largely on convenience with respect to their respective requirements. Very little attention has been focused on establishing a common ground to compare different efforts. This paper surveys the metrics that are already used for prognostics in a variety of domains including medicine, nuclear, automotive, aerospace, and electronics. It also considers other domains that involve prediction-related tasks, such as weather and finance. Differences and similarities between these domains and health maintenancehave been analyzed to help understand what performance evaluation methods may or may not be borrowed. Further, these metrics have been categorized in several ways that may be useful in deciding upon a suitable subset for a specific application. Some important prognostic concepts have been defined using a notational framework that enables interpretation of different metrics coherently. Last, but not the least, a list of metrics has been suggested to assess critical aspects of RUL predictions before they are fielded in real applications.

Prognostics performance evaluation has gained significant attention in the past few years. *As prognostics technology matures and more sophisticated methods for prognostic uncertainty management are developed, a standardized methodology for performance evaluation becomes extremely important to guide improvement efforts in a constructive manner. This paper is in continuation of previous efforts where several new evaluation metrics tailored for prognostics were introduced and were shown to effectively evaluate various algorithms as compared to other conventional metrics. Specifically, this paper presents a detailed discussion on how these metrics should be interpreted and used. Several shortcomings identified, while applying these metrics to a variety of real applications, are also summarized along with discussions that attempt to alleviate these problems. Further, these metrics have been enhanced to include the capability of incorporating probability distribution information from prognostic algorithms as opposed to evaluation based on point estimates only. Several methods have been suggested and guidelines have been provided to help choose one method over another based on probability distribution characteristics.

Prognostics is an emerging concept in condition based maintenance (CBM) of critical systems. Along with developing the fundamentals of being able to confidently predict Remaining Useful Life (RUL), the technology calls for fielded applications as it inches towards maturation. This requires a stringent performance evaluation so that the significance of the concept can be fully exploited. Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end-user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few issues. Instead, the research community has used a variety of metrics based largely on convenience with respect to their respective requirements. Very little attention has been focused on establishing a common ground to compare different efforts. This paper surveys the metrics that are already used for prognostics in a variety of domains including medicine, nuclear, automotive, aerospace, and electronics. It also considers other domains that involve prediction-related tasks, such as weather and finance. Differences and similarities between these domains and health maintenance have been analyzed to help understand what performance evaluation methods may or may not be borrowed. Further, these metrics have been categorized in several ways that may be useful in deciding upon a suitable subset for a specific application. Some important prognostic concepts have been defined using a notational framework that enables interpretation of different metrics coherently. Last, but not the least, a list of metrics has been suggested to assess critical aspects of RUL predictions before they are fielded in real applications.

This article presented a discussion on uncertainty representation and management for model-based prog- nostics methodologies based on the Bayesian tracking framework and specifically for a Kalman filter appli- cation to electronics components. In particular, it explores the implication of modeling remaining useful life prediction as a stochastic process and how it relates to remaining useful life computation by statistical models, to uncertainty representation and management, and to the role of prognostics in decision-making. A discussion on how uncertainty propagates from the health state estimation process through the health state forecasting process is provided. Remaining useful life computation steps under uncertainty are pre- sented and analytical results on uncertainty quantification are provided under a simplified scenario. A proper propagation of uncertainty through the RUL prediction step as well as its correct interpretation are key to developing decision-making methodologies that make use of the remaining useful life prediction estimates and their corresponding uncertainties in order to make actionable choices that will optimize reliability, operations or safety in view of the prognostics information.

Model-based prognostics exploits domain knowl- edge of the system, its components, and how they fail by casting the underlying physical phenom- ena in a physics-based model that is derived from first principles. In most applications, uncertain- ties from a number of sources cause the predic- tions to be inaccurate and imprecise even with accurate models. Therefore, algorithms are em- ployed that help in managing these uncertainties. Particle filters have become a popular choice to solve this problem due to their wide applicability and ease of implementation. We present a gen- eral model-based prognostics methodology using particle filters. In order to provide more accu- rate and precise estimates, and, therefore, more accurate and precise predictions, we investigate the use of fixed-lag filters. We develop a detailed physics-based model of a pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach. The exper- iments demonstrate the advantages that fixed-lag filters may provide in the context of prognostics, as measured by prognostics performance metrics.

This software is a Python module for estimating uncertainty in predictions of machine learning models. It is a Python package that calculates uncertainties in machine learning models using bootstrapping and residual bootstrapping. It is intended to interface with scikit-learn but any Python package that uses a similar interface should work.

This software is a Python module for estimating uncertainty in predictions of machine learning models. It is a Python package that calculates uncertainties in machine learning models using bootstrapping and residual bootstrapping. It is intended to interface with scikit-learn but any Python package that uses a similar interface should work.

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.

**PHM08 Challenge Dataset is now publicly available at the NASA Prognostics Respository + [Download](http://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-data-repository/)** **INTRODUCTION - WHY SIMULATE DEGRADATION DATA?** Of various challenges encountered in prognostics algorithm development, the non-availability of suitable validation data is most often the bottleneck in the technology certification process. Prognostics imposes several requirements on the training data in addition to what is commonly available from various applications. It not only requires data containing fault signatures but also that contains fault evolution trends with corresponding time indexes (in number of hours or number of operational cycles). In general there are three sources from which data is usually available, namely: Fielded applications, experimental test-beds, and computer simulations (see [Figure 1](https://c3.ndc.nasa.gov/dashlink/static/media/other/3_modes.bmp)). From prognostics point of view, data collection paradoxically suffers from the situation that the systems that do run to failure often did not have warning instrumentation installed, hence no or little record of what went wrong. In the other situation, those that are continuously monitored are prevented from running to failure or are subject to maintenance that eliminates the signatures of fault evolution. Conducting experiments that replicate real world situations is extremely expensive in terms of time required for a healthy system to run to failure and is often dangerous. Accelerated ageing may be useful to some extent but may not emulate normal wear patterns. Furthermore, to manage uncertainty multiple datasets must be collected to quantify variations resulting from multiple sources, which makes it all the way more unattainable. Simulations can be fast, inexpensive, and provide a number of options to design experiments, but their usefulness is contingent on the availability of high fidelity models that represent the real systems fairly well. However, once such a model is available, simulations offer the flexibility to rerun various experiments with added knowledge from the system as it becomes available. Where, availability of real fault evolution data from the fielded systems would be more desirable, generating data using a high fidelity model and integrating it with the knowledge gathered from the partial data obtained from the real systems is by far the most practical approach for prognostics algorithm development, validation, and verification. In this presentation we discuss some key elements that must be kept in mind while generating datasets suitable for prognostics. Furthermore, with the help of an example it has been shown how a dynamical system model can be supported with suitable degradation models available from respective domain knowledge to create suitable data. The example is discussed next. **APPLICATION DOMAIN** Tracking and Predicting the progressionof damage in aircraft turbo machinery has been an active area of study within the Condition Based Maintenance (CBM) community. A general approach has been to correlate flow and effciency losses to degradation signtures in various components of the engine. Once such mapping is available, the next task is to estimate this loss of flow and eficiency inferring information from measurable sensor outputs, which ultimtely is used to assess the level of degradation in the system. **SYSTEM MODEL: C-MAPSS** The C-MAPSS (Commercial Modular Aero Propulsion System Simulation) is a tool, recently released, for simulating a realistic large commercial turbofan engine. C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) that simulates a realistic large (~90,000lb) commercial turbofan engine. It allows the user to choose and design operational profiles, controllers, environmental conditions, thrust levels, etc. to simualte a scenario of interest. An extensive list of output va

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.