Degradation Data-Driven Remaining Useful Life Estimation in the Absence of Prior Degradation Knowledge

Recent developments in prognostic and health management have been targeted at utilizing the observed degradation signals to estimate residual life distributions. Current degradation models mainly focus on a population of “identical” devices or an individual device with population information, not a single component in the absence of prior degradation knowledge. However, the fast development of science and technology provides us with many kinds of new systems, and we just have the real-time monitoring information to analyze the reliability for them. The fusion algorithm presented herein addresses this challenge by combining the excellent modeling ability of Bayesian updating method for the multilevel data and the prominent estimation ability of ECM algorithm for incomplete data. Residual life distributions and posterior distributions are first calculated through the Bayesian updating method based on random initial a priori distributions. Then the a priori distributions are revised and improved for future predictions by the ECM algorithm. Once a new signal is observed, we can reuse the fusion algorithm to improve the accuracy of residual life distributions. The applicability of this fusion algorithm is validated by a set of simulation experiments

Major challenges in prognostics: study on benchmarking prognostic datasets

Even though prognostics has been defined to be one of the most difficult tasks in Condition Based Maintenance (CBM), many studies have reported promising results in recent years. The nature of the prognostics problem is different from diagnostics with its own challenges. There exist two major approaches to prognostics: data-driven and physics-based models. This paper aims to present the major challenges in both of these approaches by examining a number of published datasets for their suitability for analysis. Data-driven methods require sufficient samples that were run until failure whereas physics-based methods need physics of failure progression

Prognostics with autoregressive moving average for railway turnouts

Turnout systems are one of the most critical systems on railway infrastructure. Diagnostics and prognostics on turnout system have ability to increase the reliability & availability and reduce the downtime of the railway infrastructure. Even though diagnostics on railway turnout systems have been reported in the literature, reported studies on prognostics in railway turnout system is very sparse. This paper presents autoregressive moving average model based prognostics on railway turnouts. The model is applied to data collected from real turnout systems. The failure progression is obtained manually using the exponential degradation model. Remaining Useful Life of ten turnout systems have been reported and results are very promising

Methodology for designing accelerated aging tests for predicting life of photovoltaic arrays

A methodology for designing aging tests in which life prediction was paramount was developed. The methodology builds upon experience with regard to aging behavior in those material classes which are expected to be utilized as encapsulant elements, viz., glasses and polymers, and upon experience with the design of aging tests. The experiences were reviewed, and results are discussed in detail

Stochastic modelling, Bayesian inference, and new in vivo measurements elucidate the debated mtDNA bottleneck mechanism

Dangerous damage to mitochondrial DNA (mtDNA) can be ameliorated during mammalian development through a highly debated mechanism called the mtDNA bottleneck. Uncertainty surrounding this process limits our ability to address inherited mtDNA diseases. We produce a new, physically motivated, generalisable theoretical model for mtDNA populations during development, allowing the first statistical comparison of proposed bottleneck mechanisms. Using approximate Bayesian computation and mouse data, we find most statistical support for a combination of binomial partitioning of mtDNAs at cell divisions and random mtDNA turnover, meaning that the debated exact magnitude of mtDNA copy number depletion is flexible. New experimental measurements from a wild-derived mtDNA pairing in mice confirm the theoretical predictions of this model. We analytically solve a mathematical description of this mechanism, computing probabilities of mtDNA disease onset, efficacy of clinical sampling strategies, and effects of potential dynamic interventions, thus developing a quantitative and experimentally-supported stochastic theory of the bottleneck.Comment: Main text: 14 pages, 5 figures; Supplement: 17 pages, 4 figures; Total: 31 pages, 9 figure

Applying the General Path Model to Estimation of Remaining Useful Life

The ultimate goal of most prognostic systems is accurate prediction of the remaining useful life of individual systems or components based on their use and performance. This class of prognostic algorithms is termed Effects-Based or Type III Prognostics. Traditional individual-based prognostics involve identifying an appropriate degradation measure to characterize the system's progression to failure. A functional fit of this parameter is then extrapolated to a pre-defined failure threshold to estimate the remaining useful life of the system or component. This paper proposes a specific formulation of the General Path Model with dynamic Bayesian updating as one effects-based prognostic algorithm. The method is illustrated with an application to the prognostics challenge problem posed at PHM '08

Metal matrix composite micromechanics: In-situ behavior influence on composite properties

Recent efforts in computational mechanics methods for simulating the nonlinear behavior of metal matrix composites have culminated in the implementation of the Metal Matrix Composite Analyzer (METCAN) computer code. In METCAN material nonlinearity is treated at the constituent (fiber, matrix, and interphase) level where the current material model describes a time-temperature-stress dependency of the constituent properties in a material behavior space. The composite properties are synthesized from the constituent instantaneous properties by virtue of composite micromechanics and macromechanics models. The behavior of metal matrix composites depends on fabrication process variables, in situ fiber and matrix properties, bonding between the fiber and matrix, and/or the properties of an interphase between the fiber and matrix. Specifically, the influence of in situ matrix strength and the interphase degradation on the unidirectional composite stress-strain behavior is examined. These types of studies provide insight into micromechanical behavior that may be helpful in resolving discrepancies between experimentally observed composite behavior and predicted response