FR3108186B1 - Procédé de consolidation d'un ensemble de données pour de la maintenance prédictive et dispositif associé - Google Patents

Procédé de consolidation d'un ensemble de données pour de la maintenance prédictive et dispositif associé Download PDF

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Publication number
FR3108186B1
FR3108186B1 FR2002546A FR2002546A FR3108186B1 FR 3108186 B1 FR3108186 B1 FR 3108186B1 FR 2002546 A FR2002546 A FR 2002546A FR 2002546 A FR2002546 A FR 2002546A FR 3108186 B1 FR3108186 B1 FR 3108186B1
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France
Prior art keywords
data
phase
consolidating
predictive maintenance
equipment
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Active
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FR2002546A
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English (en)
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FR3108186A1 (fr
Inventor
Philippe Dubedat
Victor Leyronas
Yannick Caillaud
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Thales SA
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Thales SA
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Priority to FR2002546A priority Critical patent/FR3108186B1/fr
Priority to PCT/EP2021/056668 priority patent/WO2021185826A1/fr
Publication of FR3108186A1 publication Critical patent/FR3108186A1/fr
Application granted granted Critical
Publication of FR3108186B1 publication Critical patent/FR3108186B1/fr
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Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0221—Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/088—Non-supervised learning, e.g. competitive learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G06N5/046—Forward inferencing; Production systems
    • G06N5/047—Pattern matching networks; Rete networks

Abstract

Procédé de consolidation d’un ensemble de données pour de la maintenance prédictive et dispositifs associés L’invention concerne un procédé de consolidation d’un ensemble de données de maintenance d’un équipement, l’ensemble de données étant destiné à un système de prédiction propre à prédire une panne de l’équipement à partir de l’ensemble de données, le procédé de consolidation étant mis en œuvre par un système de consolidation et comportant : - une phase de formatage des données, - une phase de validation du contenu des données formatées, - une phase de détermination d’anomalies dans les données validées, les données anormales étant écartées ou corrigées, pour obtenir des données normales, la phase de détermination d’anomalies comprenant l’application de deux algorithmes d’apprentissage distincts, et - une phase de fiabilisation des données normales pour diminuer les incohérences éventuelles présentes dans les données normales pour obtenir un ensemble de données consolidées. Figure pour l'abrégé : figure 3
FR2002546A 2020-03-16 2020-03-16 Procédé de consolidation d'un ensemble de données pour de la maintenance prédictive et dispositif associé Active FR3108186B1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
FR2002546A FR3108186B1 (fr) 2020-03-16 2020-03-16 Procédé de consolidation d'un ensemble de données pour de la maintenance prédictive et dispositif associé
PCT/EP2021/056668 WO2021185826A1 (fr) 2020-03-16 2021-03-16 Procédé de consolidation d'un ensemble de données pour de la maintenance prédictive et dispositifs associés

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
FR2002546 2020-03-16
FR2002546A FR3108186B1 (fr) 2020-03-16 2020-03-16 Procédé de consolidation d'un ensemble de données pour de la maintenance prédictive et dispositif associé

Publications (2)

Publication Number Publication Date
FR3108186A1 FR3108186A1 (fr) 2021-09-17
FR3108186B1 true FR3108186B1 (fr) 2022-03-25

Family

ID=71661993

Family Applications (1)

Application Number Title Priority Date Filing Date
FR2002546A Active FR3108186B1 (fr) 2020-03-16 2020-03-16 Procédé de consolidation d'un ensemble de données pour de la maintenance prédictive et dispositif associé

Country Status (2)

Country Link
FR (1) FR3108186B1 (fr)
WO (1) WO2021185826A1 (fr)

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3107000A3 (fr) * 2015-06-17 2016-12-28 Tata Consultancy Services Limited Système et procédé permettant de détecter des valeurs aberrantes en temps réel pour un signal temps-série univarié
CN110471946A (zh) * 2019-07-08 2019-11-19 广东工业大学 一种基于网格剪枝的lof离群点检测方法及系统

Also Published As

Publication number Publication date
WO2021185826A1 (fr) 2021-09-23
FR3108186A1 (fr) 2021-09-17

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