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Tour d’ho­ri­zon des main­te­nances dans l’ex­ploi­ta­tion de réseau

Par La rédaction - 2 avril 2019
Temps de lecture : 9min

Les procé­dures de main­te­nance des réseaux s’ins­crivent avant tout sur l’axe du temps. Au-delà de la main­te­nance cura­tive, qui répond à l’ur­gence du moment, les main­te­nances préven­tives et prédic­tives reposent sur l’an­ti­ci­pa­tion. Une anti­ci­pa­tion empi­rique pour l’une et algo­rith­mique pour l’autre. En quoi la donnée réfé­ren­cée est-elle essen­tielle pour mener à bien ces main­te­nances proac­tives ? Décryp­tage.

À chaque type de main­te­nance des réseaux sa tempo­ra­lité

Main­te­nance cura­tive : contrer les inci­dents en temps réel

Comme son nom l’in­dique, la main­te­nance cura­tive (ou correc­tive) est liée à des inci­dents consta­tés en temps réel. Ces situa­tions d’ur­gence peuvent être diverses : détec­tion d’un inci­dent, acci­dent, panne, anoma­lie par des indi­vi­dus (exploi­tants, person­nel, témoin, etc.) ou des capteurs connec­tés.

Pour faire remon­ter les données et enclen­cher une procé­dure de trai­te­ment, les infra­struc­tures ferro­viaires ont de plus en plus recours à l’IoT (Inter­net des objets). Objec­tif : accé­lé­rer les délais d’in­ter­ven­tion. En effet, pour réta­blir la situa­tion, la prise en charge se déroule dans un laps de temps respec­tant le délai contrac­tuel d’in­ter­ven­tion : dans une situa­tion dange­reuse, ces délais doivent être immé­diats.

L’objec­tif de tout exploi­tant de réseau est de réduire la part de main­te­nance cura­tive, parti­cu­liè­re­ment onéreuse. Le recours au cura­tif doit donc rester excep­tion­nel et répondre à des impon­dé­rables (par exemple, une voiture percu­tant une armoire élec­trique). L’an­ti­ci­pa­tion maxi­male des inter­ven­tions de type cura­tif va permettre d’ali­men­ter la main­te­nance prédic­tive.

◇ Faci­li­ter l’ex­ploi­ta­tion des patri­moines et leur main­te­nance ◇

 

Main­te­nance prédic­tive : une approche empi­rique

La main­te­nance prédic­tive permet d’an­ti­ci­per certaines pannes en analy­sant les durées de vie (ou de fonc­tion­ne­ment) des équi­pe­ments spéci­fiés par les construc­teurs. Il s’agit, en d’autres termes, d’ana­ly­ser le passé pour envi­sa­ger les actes de main­te­nance des réseaux à program­mer. Une démarche empi­rique, qui met en place un calen­drier de plani­fi­ca­tion repo­sant sur le temps écoulé et les expé­riences passées.

Néan­moins, cette main­te­nance néces­site le déve­lop­pe­ment d’un tableau de bord métier repo­sant sur des outils de busi­ness intel­li­gence. Outils qui néces­sitent eux-mêmes la créa­tion d’en­tre­pôts de données adap­tés aux problé­ma­tiques métier.

Au lieu de consul­ter ces données grâce à des requêtes complexes, il faudra donc créer des outils permet­tant d’iden­ti­fier les éléments par leur signi­fi­ca­tion métier. Par exemple, dans le cas d’un réseau d’eau, on accor­dera de l’im­por­tance aux notions de « débit » et de « secteur ». Cet outil permet­tra alors de créer une visua­li­sa­tion sous forme graphique (abscisse tempo­relle, repré­sen­ta­tion spatia­le…) afin de présen­ter la donnée de manière simple, acces­sible par chacun.

Aujourd’­hui, l’élé­ment central de la main­te­nance des réseaux est la connais­sance de son patri­moine qui passe inévi­ta­ble­ment par un réfé­ren­tiel. En second plan, il faut inté­grer l’histo­rique de main­te­nance. À partir de cet histo­rique, les algo­rithmes de machine lear­ning vont pouvoir anti­ci­per les pannes futures.

Intel­li­gence arti­fi­cielle et main­te­nance prédic­tive : où en est-on ?

De nombreuses fausses infor­ma­tions sont colpor­tées sur le sujet. Dans la réalité, sur 100 projets incluant l’in­tel­li­gence arti­fi­cielle (IA) lancés pour la main­te­nance prédic­tive, 80 restent à l’état de preuve de concept. Le prin­ci­pal problème rencon­tré est le manque de cas d’usage bien docu­men­tés, conte­nant des données quali­ta­tives (souvent erro­nées, mal rensei­gnées ou absentes).

La donnée est le para­mètre fonda­men­tal de tout travail d’al­go­rithme de prédic­tion : la phase de prépa­ra­tion de la data ne peut donc être négli­gée. Comme ce travail s’ap­puie sur un histo­rique du patri­moine du réfé­ren­tiel mis à jour, il faut être en capa­cité de recons­ti­tuer l’état du réfé­ren­tiel de manière à connaître les critères et les causes d’une panne.

Une fois les données réunies et épurées, commence alors un véri­table travail d’ex­per­tise sur :

Main­te­nance pres­crip­tive : un pas de plus vers le futur

Ce type de main­te­nance des réseaux a pour objec­tif de produire des pres­crip­tions : il ne s’agit plus unique­ment de déter­mi­ner les origines et les dates de pannes, mais bien d’éva­luer les compo­santes qui pour­raient être amélio­rées.

Il convient alors de travailler sur un scéna­rio dans lequel on prévoit de mettre en place plusieurs actes de main­te­nance – notam­ment dans les sphères écono­miques et tech­niques. Objec­tif : réduire le nombre d’opéra­tions cura­tives et leurs coûts, amélio­rer la perfor­mance du réseau et le taux de rende­ment global d’un équi­pe­ment.

L’enjeu est donc de pres­crire un plan de main­te­nance des réseaux sur la base de contraintes et d’hy­po­thèses four­nies en entrée (durée, coût à de ne pas dépas­ser, etc.). L’ex­ploi­tant analy­sera alors les diffé­rents scéna­rios et optera pour le plus perti­nent pour répondre à ses objec­tifs.

Quels sont les enjeux rele­vés par les main­te­nances ?

Selon une récente étude, entre 22 et 32 % des inci­dents résultent d’une avarie de maté­riel (défaut de frei­nage, boîtes chaudes, départ d’in­cen­die, inci­dent de la signa­li­sa­tion…).

Si l’on prend l’exemple du secteur ferro­viaire, la préven­tion de ces pannes endosse plusieurs enjeux :

Dans une étude de 2017, l’Au­to­rité de la qualité de service dans les trans­ports (AQST), dépen­dant du minis­tère du Déve­lop­pe­ment durable, estime que le temps perdu par l’en­semble des voya­geurs ferro­viaires est de plus de 2 milliards de minutes chaque année. « Ceci corres­pond à une perte de valeur écono­mique pour la collec­ti­vité natio­nale de l’ordre de plus de 1,5 milliard d’eu­ros », précisent les auteurs.

◇ Main­­te­­nance des réseaux : du cura­­tif au pres­­crip­­tif, une stra­­té­­gie qui rapporte ◇

 

En 2018, une étude du cabi­net de conseil Mackin­sey démon­trait que tous les marchés pour­raient réali­ser une écono­mie de 630 milliards en réali­sant une main­te­nance prédic­tive basée sur l’in­dus­trie 4.0 et d’autres secteurs. L’objec­tif ? Détec­ter les signaux faibles grâce aux capteurs et les inté­grer dans des algo­rithmes de prédic­tion des pannes.

Pour autant, la démarche demeure mino­ri­taire car coûteuse. La solu­tion serait alors de coor­don­ner les phases de main­te­nance prédic­tive sur la program­ma­tion des travaux de voirie, afin d’an­ti­ci­per au mieux les inter­ven­tions sur secteurs à risque.

Même si la main­te­nance cura­tive reste incon­tour­nable, son coût prohi­bi­tif oblige les gestion­naires de réseaux à réduire sa part. Mais pour y parve­nir et réali­ser des main­te­nances préven­tive et prédic­tive perti­nentes, plusieurs outils fonda­men­taux sont à mettre en œuvre. Entre autres, une logique d’API pour garan­tir la qualité de la donnée, ou encore les échanges de sources de data.

Par La rédaction - 2 avril 2019

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