An ANN Based Decision Support System Fostering Production Plan Optimization Through Preventive Maintenance Management

Cinus, Marco; Confalonieri, Matteo; Barni, Andrea; Valente, Anna

doi:10.1007/978-3-319-33747-0_44

Cited by 3 publications

(2 citation statements)

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“…The problem was formulated and solved in the semi-Markov decision process framework in order to minimize the long-run expected average cost per unit time. Cinus et al [46] propose a decision support system that processes sensor data and Key Performance Indicators (KPIs) using an artificial neural network (ANN)-based knowledge system and integrates the maintenance actions within the weekly production schedule. Mourtzis et al [47,48] propose an augmented reality mobile application, interfaced with a shop-floor scheduling tool, in order to enable the operator to decide on immediately calling AR remote maintenance or scheduling maintenance tasks for later along with production tasks.…”

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confidence: 99%

A Review of Data-Driven Decision-Making Methods for Industry 4.0 Maintenance Applications

et al. 2021

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Decision-making for manufacturing and maintenance operations is benefiting from the advanced sensor infrastructure of Industry 4.0, enabling the use of algorithms that analyze data, predict emerging situations, and recommend mitigating actions. The current paper reviews the literature on data-driven decision-making in maintenance and outlines directions for future research towards data-driven decision-making for Industry 4.0 maintenance applications. The main research directions include the coupling of decision-making with augmented reality for seamless interfacing that combines the real and virtual worlds of manufacturing operators; methods and techniques for addressing uncertainty of data, in lieu of emerging Internet of Things (IoT) devices; integration of maintenance decision-making with other operations such as scheduling and planning; utilization of the cloud continuum for optimal deployment of decision-making services; capability of decision-making methods to cope with big data; incorporation of advanced security mechanisms; and coupling decision-making with simulation software, autonomous robots, and other additive manufacturing initiatives.

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confidence: 99%

A Review of Data-Driven Decision-Making Methods for Industry 4.0 Maintenance Applications

et al. 2021

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“…24 Sun et al, 2007;Muller et al, 2008;Xu, and Hu, 2008;Islam, and Khan, 2010;Zhu et al, 2010;Besnard, and Bertling, 2010;Zhu et al, 2011;Tian et al, 2012;Castro et al, 2012;Moghaddass et al, 2014;Le et al, 2014;Hong et al, 2014;Song et al, 2014;Tang et al, 2015a;Tang et al, 2015b;Lin et al, 2015;Park et al, 2016;Drumheller et al, 2017;Animah, and Shafiee, 2017;Zan et al, 2018 Joint Optimization 16 Zhou et al, 2007;Njike et al, 2009;Rausch, and Liao, 2010;Gulledge et al, 2010;Nodem et al, 2011;Wang, 2011;Portioli-Staudacher, and Tantardini, 2012;Lee, and Ni, 2013;Kouedeu et al, 2015;Gan et al, 2015;Jafari, and Makis, 2015;Cinus et al, 2016;Gu et al, 2017…”

Section: Reliability-and Degradation-based Decision Makingmentioning

confidence: 99%

Proactive computing in industrial maintenance decision making

Bousdekis¹,

Μπουσδέκης²

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Η προδραστική πληροφορική οδηγούμενη από γεγονότα αφορά τη χρήση πληροφοριακών συστημάτων οδηγούμενων από γεγονότα που έχουν την ικανότητα να εξαλείφουν ή να αμβλύνουν την επίδραση μελλοντικών ανεπιθύμητων γεγονότων ή να αξιοποιούν μελλοντικές ευκαιρίες με βάση δεδομένα αισθητήρων πραγματικού χρόνου και τεχνολογίες λήψης αποφάσεων. Η διοίκηση συντήρησης μπορεί να επωφεληθεί από την προδραστική πληροφορική για να αντιμετωπίσει τις προκλήσεις στο πλαίσιο της Βιομηχανίας 4.0 (Industry 4.0).Για το σκοπό αυτό, η παρούσα διατριβή συνδυάζει τους ερευνητικούς τομείς της Βιομηχανίας 4.0, της Διοίκησης Συντήρησης και της Προδραστικής Πληροφορικής. Με αυτό τον τρόπο, ανοίγει το δρόμο για την επόμενη γενιά διοίκησης συντήρησης στο πλαίσιο της Βιομηχανίας 4.0, την Προδραστική Συντήρηση (Proactive Maintenance). Το επίκεντρο της διατριβής είναι η λήψη προδραστικών αποφάσεων. Συνεπώς, προτείνει μεθόδους προδραστικών αποφάσεων για βιομηχανική συντήρηση, αλγόριθμους για συνεχή βελτίωση της λήψης προδραστικών αποφάσεων μέσω της προτεινόμενης προσέγγισης Ανατροφοδότηση Υποβοηθούμενη από Αισθητήρες (Sensor-Enabled Feedback - SEF) και αλγόριθμους για την επίγνωση πλαισίου. Για να γίνει αυτό, αξιοποιεί μεθόδους και τεχνικές επιχειρησιακής έρευνας, ανάλυσης δεδομένων και μηχανικής μάθησης.Οι προαναφερθέντες αλγόριθμοι έχουν ενσωματωθεί σε ένα προδραστικό πληροφοριακό σύστημα για τη λήψη αποφάσεων το οποίο ολοκληρώθηκε με άλλα εργαλεία για την υλοποίηση όλων των βημάτων του πλαισίου της Προδραστικής Συντήρησης. Το σύστημα εγκαταστάθηκε και αξιολογήθηκε σε πραγματικό βιομηχανικό περιβάλλον, ενώ πραγματοποιήθηκε περαιτέρω αξιολόγηση με εκτεταμένα πειράματα προσομοίωσης.

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Prediction of Radio Signal Failures of Communication Based Train Operating Systems by Machine Learning Methods

Arslan,

Tiryaki

2024

Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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The use of rail systems in urban public transportation has become a necessity for reasons such as time saving, travel comfort and operating costs, especially in cities with high population and road traffic. Communication based train operating systems (CBTC) are used for the safe use of urban rail systems and the maximum capacity of the railway line. In this study, in line with the data collected from the trains on a railway line operated with CBTC, the status of the radio signals that enable the wireless communication of the trains with the trackside signaling equipment was evaluated by machine learning methods, and the situations that may have negative effects on the train operations of the problems at the signal level were evaluate. The problems on the antennas which receives signals from trackside above trains, the poor connection related with fiber optical and LAN cables, the trackside transmitter antenna orientation problems causes decrease on signal levels. It is aimed that to take actions about the problematic signal levels without any negative impact on the passenger comfort and the operation yet. The radio signal losses cause unexpected trains stops and delays. A decision support model has been developed that will offer early solution suggestions to system maintainers in order to intervene first. In conclusion, since it is the first study related with failure prediction by using radio signal levels data on railway signaling system, this study presents an important innovation in terms of literature.

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An ANN Based Decision Support System Fostering Production Plan Optimization Through Preventive Maintenance Management

Cited by 3 publications

References 10 publications

A Review of Data-Driven Decision-Making Methods for Industry 4.0 Maintenance Applications

A Review of Data-Driven Decision-Making Methods for Industry 4.0 Maintenance Applications

Proactive computing in industrial maintenance decision making

Prediction of Radio Signal Failures of Communication Based Train Operating Systems by Machine Learning Methods

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