Context Driven Remaining Useful Life Estimation

Johansson, Carl-Anders; Simon, Victor; Galar, Diego

doi:10.1016/j.procir.2014.07.129

Cited by 7 publications

(3 citation statements)

References 4 publications

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“…Meanwhile, context and environment are a source of additional knowledge in many existing works. In [19], the effects of the interaction of context features on machine tools health information is investigated by extracting additional features from the environment and evaluating their impact on the system. Features extraction for prognostics, from the human impact on the system health by human error during maintenance activities as one variable contributing to system failure events, is studied in [20].…”

Section: ) Scope Of the Knowledge: System -Contextmentioning

confidence: 99%

“…Health Management [20] Qualitative Context General Knowledge [34] Quantitative System Health Management [13], [14], [18], [41] Quantitative System General Knowledge [5], [15], [22], [30], [31], [34] Quantitative Context Health Management [19] Quantitative Context General Knowledge [23] specifications. It is a promising tool to formalize the knowledge into mathematical models [35].…”

Section: Contextmentioning

confidence: 99%

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A Short Review on the Integration of Expert Knowledge in Prognostics for PHM in Industrial Applications

Gay

Iung

Voisin

et al. 2021

2021 5th International Conference on System Reliability and Safety (ICSRS)

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Optimizing maintenance is essential for industrials to stay competitive, and the development of appropriate predictive maintenance is necessary to achieve this objective. To this extent, the Prognostics and Health Management (PHM) paradigm is well established. One of the key steps of PHM is the prognostics of health states of the system. Various state-of-the-art approaches exist for prognostics, with an emerging orientation towards data-driven methods. Indeed, they have lot of potential for Industry 4.0 applications with high amount of data from sensors and control equipment. However, labelled data (i.e., failures of systems) is not always available on real-life applications where preventive maintenance is often already applied. Thus, the learning databases can be unbalanced, with few learning examples, consequently reducing the learning capacities of algorithms, as well as their generalization. One way to optimize learning on such applications is then to use Expert Knowledge, which can provide additional information on the system and its operating model. A challenging issue is herein the development of a general methodology to integrate the Expert Knowledge into data-driven methods.To face this challenge, this paper aims to propose a categorization of Expert Knowledge based on existing works to identify adapted methods that can help to integrate efficiently the available Knowledge into relevant prognostics algorithms. The proposed categorization will allow and facilitate the review and comparison of approaches and methodologies introduced in the literature and in further research. Finally, the proposed classification will be illustrated on a real case of prognostics for a hydraulic circuit from an ArcelorMittal plant.

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Section: ) Scope Of the Knowledge: System -Contextmentioning

confidence: 99%

Section: Contextmentioning

confidence: 99%

A Short Review on the Integration of Expert Knowledge in Prognostics for PHM in Industrial Applications

Gay

Iung

Voisin

et al. 2021

2021 5th International Conference on System Reliability and Safety (ICSRS)

View full text Add to dashboard Cite

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“…Thaduri et al (2014) presented a computational intelligence framework for context-aware decision making to demonstrate the utilisation of soft computing techniques in context-aware industrial areas. Johansson et al (2014) presented a concept of context-driven prognosis for RUL based on fingerprint and available operational data. A fingerprint consists of data obtained in standardised way and represents the status of a machine.…”

Section: Related Workmentioning

confidence: 99%

Context preparation for predictive analytics – a case from manufacturing industry

Schmidt

Gandhi

Wang

et al. 2017

JQME

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Purpose The purpose of this paper is to exemplify and discuss the context aspect for predictive analytics where in parallel condition monitoring (CM) measurements data and information related to the context are gathered and analysed. Design/methodology/approach This paper is based on an industrial case study, conducted in a manufacturing company. The linear axis of a machine tool has been selected as an object of interest. Available data from different sources have been gathered and a new CM function has been implemented. Details about performed steps of data acquisition and selection are provided. Among the obtained data, health indicators and context-related information have been identified. Findings Multiple sources of relevant contextual information have been identified. Performed analysis discovered the deviations in operational conditions when the same machining operation is repeatedly performed. Originality/value This paper shows the outcomes from a case study in real word industrial setup. A new visualisation method of gathered data is proposed to support decision-making process.

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