This paper describes how damage propagation can be modeled within the modules of aircraft gas turbine engines. To that end, response surfaces of all sensors are generated via a thermo-dynamical simulation model for the engine as a function of variations of flow and efficiency of the modules of interest. An exponential rate of change for flow and efficiency loss was imposed for each data set, starting at a randomly chosen initial deterioration set point. The rate of change of the flow and efficiency denotes an otherwise unspecified fault with increasingly worsening effect. The rates of change of the faults were constrained to an upper threshold but were otherwise chosen randomly. Damage propagation was allowed to continue until a failure criterion was reached. A health index was defined as the minimum of several superimposed operational margins at any given time instant

Abstract—Prognostics is crucial to providing reliable condition-based maintenance decisions. To obtain accurate predictions of component life, a variety of sensors are often needed. However, it is typically difficult to add enough sensors for reliable prognosis, due to system constraints such as cost and weight. Model-based prognostics helps to offset this problem by exploiting domain knowledge about 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. We develop a model-based prognostics methodology using particle filters, and investigate the benefits of a model-based approach when sensor sets are diminished. We apply our approach to a detailed physics-based model of a pneumatic valve, and perform comprehensive simulation experiments to demonstrate the robustness of model-based approaches under limited sensing scenarios using prognos-tics performance metrics. I.

Model-based prognostics captures system knowl-edge in the form of physics-based models of com-ponents, and how they fail, in order to obtain ac-curate predictions of end of life (EOL). EOL is predicted based on the estimated current state dis-tribution of a component and expected profiles of future usage. In general, this requires sim-ulations of the component using the underlying models. In this paper, we develop a simulation-based prediction methodology that achieves com-putational efficiency by performing only the min-imal number of simulations needed in order to accurately approximate the mean and variance of the complete EOL distribution. This is performed through the use of the unscented transform, which predicts the means and covariances of a distribu-tion passed through a nonlinear transformation. In this case, the EOL simulation acts as that non-linear transformation. In this paper, we review the unscented transform, and describe how this con-cept is applied to efficient EOL prediction. As a case study, we develop a physics-based model of a solenoid valve, and perform simulation experi-ments to demonstrate improved computational ef-ficiency without sacrificing prediction accuracy. 1

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. 1

In this paper, two probabilistic prognosis updating schemes are compared. One is based on the classical Bayesian approach and the other is based on newly developed maximum relative entropy (MRE) approach. The algorithm performance of the two models is evaluated using a set of recently developed prognostics-based metrics. Various uncertainties from measurements, modeling, and parameter estimations are integrated into the prognosis framework as random input variables for fatigue damage of materials. Measures of response variables are then used to update the statistical distributions of random variables and the prognosis results are updated using posterior distributions. Markov Chain Monte Carlo (MCMC) technique is employed to provide the posterior samples for model updating in the framework. Experimental data are used to demonstrate the operation of the proposed probabilistic prognosis methodology. A set of prognostics-based metrics are employed to quantitatively evaluate the prognosis performance and compare the proposed entropy method with the classical Bayesian updating algorithm. In particular, model accuracy, precision, robustness and convergence are rigorously evaluated in addition to the qualitative visual comparison. Following this, potential development and improvement for the prognostics-based metrics are discussed in detail. 1.

Prognostics and Health Management (PHM) is a multidisci-plinary field aiming at maintaining physical systems in their optimal functioning conditions. The system under study is assumed to be monitored by sensors from which are obtained measurements reflecting the system’s health state. A health index (HI) is estimated to feed a data-driven PHM solution developed to predict the remaining useful life (RUL). In this paper, the values taken by an HI are assumed imprecise (IHI). An IHI is interpreted as a planar figure called polygon and a case-based reasoning (CBR) approach is adapted to estimate the RUL. This adaptation makes use of computational geom-etry tools in order to estimate the nearest cases to a given testing instance. The proposed algorithm called RULCLIP-PER is assessed and compared on datasets generated by the NASA’s turbofan simulator (C-MAPSS) including the four turbofan testing datasets and the two testing datasets of the PHM’08 data challenge. These datasets represent 1360 test-ing instances and cover different realistic and difficult cases considering operating conditions and fault modes with un-known characteristics. The problem of feature selection, health index estimation, RUL fusion and ensembles are also tackled. The proposed algorithm is shown to be efficient with few pa-rameter tuning on all datasets. 1.

An integrated fatigue damage diagnosis and prognosis framework is proposed in this paper. The proposed methodology integrates a Lamb wave-based damage detection technique and a Bayesian updating method for remaining useful life (RUL) prediction. First, a piezoelectric sensor network is used to detect the fatigue crack size near the rivet holes in fuselage lap joints. Advanced signal processing and feature fusion is then used to quantitatively estimate the crack size. Following this, a small time scale model is introduced and used as the mechanism model to predict the crack propagation for a given future loading and an estimate of initial crack length. Next, a Bayesian updating algorithm is implemented incorporating the damage diagnostic result for the fatigue crack growth prediction. Probability distributions of model parameters and final RUL are updated considering various uncertainties in the damage prognosis process. Finally, the proposed methodology is demonstrated using data from fatigue testing of realistic fuselage lap joints and the model predictions are validated using prognostics metrics. 1.

Electrolytic capacitors are used in several applications rang-ing from power supplies for safety critical avionics equipment to power drivers for electro-mechanical actuators. Past expe-riences show that capacitors tend to degrade and fail faster when subjected to high electrical or thermal stress condi-tions during operations. This makes them good candidates for prognostics and health management. Model-based prognos-tics captures system knowledge in the form of physics-based models of components in order to obtain accurate predictions of end of life based on their current state of health and their anticipated future use and operational conditions. The focus of this paper is on deriving first principles degradation mod-els for thermal stress conditions and implementing Bayesian framework for making remaining useful life predictions. Data collected from simultaneous experiments are used to validate the models. Our overall goal is to derive accurate models of capacitor degradation, and use them to remaining useful life in DC-DC converters. 1.

Six years and more than seventy publications later this paper looks back and analyzes the development of prognostic algo-rithms using C-MAPSS datasets generated and disseminated by the prognostic center of excellence at NASA Ames Re-search Center. Among those datasets are five run-to-failure C-MAPSS datasets that have been popular due to various char-acteristics applicable to prognostics. The C-MAPSS datasets pose several challenges that are inherent to general prognos-tics applications. In particular, management of high vari-ability due to sensor noise, effects of operating conditions, and presence of multiple simultaneous fault modes are some factors that have great impact on the generalization capabil-ities of prognostics algorithms. More than seventy publica-tions have used the C-MAPSS datasets for developing data-driven prognostic algorithms. However, in the absence of per-formance benchmarking results and due to common misun-derstandings in interpreting the relationships between these datasets, it has been difficult for the users to suitably compare their results. In addition to identifying differentiating char-acteristics in these datasets, this paper also provides perfor-mance results for the PHM’08 data challenge wining entries to serve as performance baseline. This paper summarizes var-ious prognostic modeling efforts that used C-MAPSS datasets and provides guidelines and references to further usage of these datasets in a manner that allows clear and consistent comparison between different approaches. 1.

Within the last decade several new methods for prognostics have been developed and an overall understanding of the various issues involved in predictions for health management has significantly improved. However, it appears that there is still a lack of consensus on how prognostics is defined and what constitutes good performance for prognostics. This paper first differentiates prognostics from other prediction approaches before highlighting key attributes of performance for prediction methods. Then it argues that it is important to understand what factors affect the performance of a prognostic approach. Factors such as the application and end use of a prognostic output, the various methods to make predictions, purpose of performance evaluation, etc. are discussed. This paper presents a comprehensive view of various such aspects that dictate or should dictate what performance evaluation must be as far as prognostics is concerned. It is also discussed what should be used as baseline to assess performance and how to interpret commonly used comparisons of algorithm predictions to observed failure times. The primary goal of this paper is to present some arguments of how these issues can be addressed and to stimulate a discussion about meaningful evaluation of prognostic performance. These discussions are followed by a brief description of prognostics metrics proposed recently, their applicability, and limitations. This paper does not intend to suggest any metrics in particular rather highlights important aspects that must be covered by any performance evaluation method for prognostics. 1.