IEEE Standards for Prognostics and Health Management

Sheppard, John W.; Kaufman, M.A.; Wilmer, T.J.

doi:10.1109/maes.2009.5282287

Cited by 99 publications

(49 citation statements)

References 14 publications

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“…By using (6), it is possible to compute φ ðqÞ ¼ ðφ k1 ; …; φ kh Þ k ¼ 1;…;q ; the realization of random vector Φ, for different degradations ω.…”

Section: Syndrome Distributionsmentioning

confidence: 99%

“…In the case of systems actually marketed, it is necessary to make a distinction between individual syndrome distributions (ISDs) and fleet syndrome distributions (FSDs). The former is computed by propagating only random uncertainties, i.e., with Γ a deterministic vector with nominal values in (6). The latter is computed by propagating both random and systematic uncertainties.…”

Section: Syndrome Distributionsmentioning

confidence: 99%

“…While different forms of the PHM process can be found, the most commonly used, at least in the industry, is the openstructure architecture for conditioned based maintenance (OSA-CBM) scheme [4]. Although PHM is a quite recent discipline, it has reached a certain maturity with the development of its own standards, as shown in [5,6]. It has also been frequently applied and has demonstrated good results, first in its original field of application, structural health monitoring (SHM) [7], and later, in other fields, such as bearing monitoring [8] and battery life prediction [9].…”

Section: Introductionmentioning

confidence: 99%

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A combined sensitivity analysis and kriging surrogate modeling for early validation of health indicators

Lamoureux

Mechbal

Massé

2014

Reliability Engineering & System Safety

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. b s t r a c tTo increase the dependability of complex systems, one solution is to assess their state of health continuously through the monitoring of variables sensitive to potential degradation modes. When computed in an operating environment, these variables, known as health indicators, are subject to many uncertainties. Hence, the stochastic nature of health assessment combined with the lack of data in design stages makes it difficult to evaluate the efficiency of a health indicator before the system enters into service. This paper introduces a method for early validation of health indicators during the design stages of a system development process. This method uses physics-based modeling and uncertainties propagation to create simulated stochastic data. However, because of the large number of parameters defining the model and its computation duration, the necessary runtime for uncertainties propagation is prohibitive. Thus, kriging is used to obtain low computation time estimations of the model outputs. Moreover, sensitivity analysis techniques are performed upstream to determine the hierarchization of the model parameters and to reduce the dimension of the input space. The validation is based on three types of numerical key performance indicators corresponding to the detection, identification and prognostic processes. After having introduced and formalized the framework of uncertain systems modeling and the different performance metrics, the issues of sensitivity analysis and surrogate modeling are addressed. The method is subsequently applied to the validation of a set of health indicators for the monitoring of an aircraft engine's pumping unit.

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“…By using (6), it is possible to compute φ ðqÞ ¼ ðφ k1 ; …; φ kh Þ k ¼ 1;…;q ; the realization of random vector Φ, for different degradations ω.…”

Section: Syndrome Distributionsmentioning

confidence: 99%

Section: Syndrome Distributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A combined sensitivity analysis and kriging surrogate modeling for early validation of health indicators

Lamoureux

Mechbal

Massé

2014

Reliability Engineering & System Safety

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“…There exists in the literature some discussions about prognostics in the framework of CBM open standards [5,[7][8][9] proposed by the OpenO&M initiative [10] but in this paper only the one of the ISO is addressed. The standard published by the ISO deals with the main guidelines and aspects one has to take into account in order to perform failure prognostics on engineering systems.…”

Section: Standard Iso 13381-1mentioning

confidence: 99%

The ISO 13381-1 standard's failure prognostics process through an example

Diego

Medjaher

Zerhouni

2010

2010 Prognostics and System Health Management Conference

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Industrial failure prognostics can be considered as the key process of any condition-based maintenance solution. However, contrary to fault diagnostics which is a mature research and industrial work, failure prognostics is a new field for which few applications exist. In the last decade, the interest for this activity has led to some open and industrial standards where the main objective is to provide users with a guidelines allowing them to perform failure prognostics for a large class of industrial systems. However, these standards, rightly, do not emphasize on any particular example to illustrate their content. The present paper aims at explaining the process of failure prognostics, presented in the standard ISO 13381-1, through an electromechanical example. The purpose is to help beginner researchers in the field of industrial failure prognostics to assimilate the main tasks of the process proposed by the standard. The prognostics process is chosen because it represents the key task among the rest of topics proposed and published by the standard. Thus, the comprehension of this part is important to develop prognostics methods and algorithms based on the solid recommendations given by the international organization for standardization.

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“…The general principle guiding optimal Maintenance is to make all the necessary actions that guarantee that an asset continues to perform the required functions in the proper way [17,18]. In other words, the main goal of maintenance is to maximize the lifetime of an asset.…”

Section: Introductionmentioning

confidence: 99%

Condition Based Maintenance in Railway Transportation Systems Based on Big Data Streaming Analysis

Fumeo

Oneto

Anguita

2015

Procedia Computer Science

116

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Streaming Data Analysis (SDA) of Big Data Streams (BDS) for Condition Based Maintenance (CBM) in the context of Rail Transportation Systems (RTS) is a state-of-the-art field of research. SDA of BDS is the problem of analyzing, modeling and extracting information from huge amounts of data that continuously come from several sources in real time through computational aware solutions. Among others, CBM for Rail Transportation is one of the most challenging SDA problems, consisting of the implementation of a predictive maintenance system for evaluating the future status of the monitored assets in order to reduce risks related to failures and to avoid service disruptions. The challenge is to collect and analyze all the data streams that come from the numerous on-board sensors monitoring the assets. This paper deals with the problem of CBM applied to the condition monitoring and predictive maintenance of train axle bearings based on sensors data collection, with the purpose of maximizing their Remaining Useful Life (RUL). In particular we propose a novel algorithm for CBM based on SDA that takes advantage of the Online Support Vector Regression (OL-SVR) for predicting the RUL. The novelty of our proposal is the heuristic approach for optimizing the trade-off between the accuracy of the OL-SVR models and the computational time and resources needed in order to build them. Results from tests on a real-world dataset show the actual benefits brought by the proposed methodology.

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IEEE Standards for Prognostics and Health Management

Abstract: Abstract

Cited by 99 publications

References 14 publications

A combined sensitivity analysis and kriging surrogate modeling for early validation of health indicators

A combined sensitivity analysis and kriging surrogate modeling for early validation of health indicators

The ISO 13381-1 standard's failure prognostics process through an example

Condition Based Maintenance in Railway Transportation Systems Based on Big Data Streaming Analysis

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