Predictive Maintenance in the Production of Steel Bars: A Data-Driven Approach

The ever increasing demand for shorter production times and reduced production costs require manufacturing firms to bring down their production costs while preserving a smooth and flexible production process. To this aim, manufacturers could exploit data-driven techniques to monitor and assess equipmen’s operational state and anticipate some future failure. Sensor data acquisition, analysis, and correlation can create the equipment’s digital footprint and create awareness on it through the entire life cycle allowing the shift from time-based preventive maintenance to predictive maintenance, reducing both maintenance and production costs. In this work, a novel data analytics workflow is proposed combining the evaluation of an asset’s degradation over time with a self-assessment loop. The proposed workflow can support real-time analytics at edge devices, thus, addressing the needs of modern cyber-physical production systems for decision-making support at the edge with short response times. A prototype implementation has been evaluated in use cases related to the steel industry

A microservice architecture for predictive analytics in manufacturing

Abstract This paper discusses on the design, development and deployment of a flexible and modular platform supporting smart predictive maintenance operations, enabled by microservices architecture and virtualization technologies. Virtualization allows the platform to be deployed in a multi-tenant environment, while facilitating resource isolation and independency from specific technologies or services. Moreover, the proposed platform supports scalable data storage supporting an effective and efficient management of large volume of Industry 4.0 data. Methodologies of data-driven predictive maintenance are provided to the user as-a-service, facilitating offline training and online execution of pre-trained analytics models, while the connection of the raw data to contextual information support their understanding and interpretation, while guaranteeing interoperability across heterogeneous systems. A use case related to the predictive maintenance operations of a robotic manipulator is examined to demonstrate the effectiveness and the efficiency of the proposed platform

Method development for flexibility assessment of manufacturing systems

Modern manufacturing firms must learn to operate effectively in very dynamic environments. One key aspect that firms must consider is manufacturing flexibility. Manufacturing flexibility refers to the ability of a manufacturing system to accommodate uncertainty in the production environment. But flexibility cannot be considered in the decision making process if it is not assessed in quantifiable terms. The goal of this dissertation is to develop and demonstrate a practical method to assist manufacturers in managing environmental uncertainty and in determining the appropriate level of flexibility in their production systems. A number of research efforts have been published in the litterature that deal with the scientific issue of flexibility assessment. However, most of the published research work deals with case specific engineering problems, under certain circumstances, and in most of the cases they are applicable to specific types of production system. The approach suggested by the this dissertation considers that manufacturing flexibility is determined by its sensitivity to changes. The less flexible a manufacturing system is the more flexible it should be considered. We consider flexibility during the lifecycle of a system, from initial investment to major reconfiguration. A large number of market scenarios are being considered and the lifecycle cost of the system for all scenarios is calculated. Following, statistical analysis on the lifecycle cost results provides results on the sensitivity of the system to the uncertainty of the market environment. Furthermore, in order to make the lifecycle cost values comparable amongst the different production systems, their minimum value is calculated at a minimum level with the help of a specially developed optimization algorithm. The proposed method is applied in a case study in the automotive industry. Finally, a web-based software package that implements the proposed method along with the UML (Unified Modelling Language) description of the main entities is also being described.Τα σύγχρονα συστήματα παραγωγής πρέπει να λειτουργούν αποδοτικά σε ένα δυναμικά μεταβαλλόμενο περιβάλλον. Ένα βασικό χαρακτηριστικό που πρέπει να λάβουν υπόψη τους οι μηχανικοί στη βιομηχανία είναι η ευελιξία των συστημάτων παραγωγής. Η ευελιξία αναφέρεται στην ικανότητα ενός συστήματος παραγωγής να ανταποκρίνεται και να προσαρμόζεται στις αλλαγές του περιβάλλοντος στο οποίο λειτουργεί. Αλλά η ευελιξία είναι δύσκολο να συμπεριληφθεί στη διαδικασία λήψης αποφάσεων κατά τον σχεδιασμό ή τη λειτουργία ενός συστήματος, αν δεν έχει περιγραφεί ποσοτικά. Ο σκοπός αυτής της διατριβής είναι να αναπτύξει και να επιδείξει μια μέθοδο για την εκτίμηση και ποσοτικοποίηση της ευελιξίας. Η μέθοδος μπορεί να χρησιμοποιηθεί σε πραγματικά προβλήματα στη βιομηχανία και να βοηθήση στη λήψη αποφάσεων σχετικά με τον απαραίτητο βαθμό της ευελιξίας που πρέπει να έχει ένα σύστημα παραγωγής ειδικά στην περίπτωση που υπάρχει αβεβαιότητα και είναι δύσκολο να γίνουν αξιόπιστες προβλέψεις σχετικά με τις απαιτήσεις της αγοράς. Στην διεθνή επιστημονική βιβλιογραφία έχουν καταγραφεί αρκετές εργασίες που προσεγγίζουν το πρόβλημα της εκτίμησης της ευελιξίας ενός συστήματος παραγωγής. Οι περισσότερες όμως από τις εργασίες αντιμετωπίζουν μεμονωμένα προβλήματα, κάτω από ειδικές συνθήκες, που τις περισσότερες φορές αναφέρονται και σε συγκεκριμένους τύπους συστημάτων παραγωγής. Η προτεινόμενη μέθοδος στηρίζεται στη θεώρηση ότι η ευελιξία ενός συστήματος παραγωγής καθορίζεται από την ευαισθησία του στις αλλαγές. Όσο λιγότερο ευαίσθητο ένα σύστημα είναι στις αλλαγές του περιβάλλοντος στο οποίο λειτουργεί τόσο περισσότερο ευέλικτο είναι. Η βασική αυτή θεώρηση εφαρμόζεται μέσω της μέτρησης του εύρους των διαφόρων τιμών του συνολικού κόστους ενός συστήματος παραγωγής. Οι τιμές του συνολικού κόστους υπολογίζεται για καθένα σενάριο από ένα μεγάλο αριθμό πιθανών σεναρίων αγοράς. Στη συνέχεια με στατιστική ανάλυση στις τιμές του συνολικού κόστους οδηγούμαστε σε συμπεράσματα για την ευαισθησία ενός συστήματος παραγωγής σε ένα αβέβαιο περιβάλλον λειτουργίας. Ο υπολογισμός του συνολικού κόστους λαμβάνει υπόψη του διάφορες φάσεις του συστήματος παραγωγής στον κύκλο ζωής του όπως την αρχική επένδυση και το κόστος των αλλαγών. Επιπλέον, για να μπορούν οι διαφορετικές τιμές του κόστους να είναι συγκρίσιμες θα πρέπει να υπολογιστούν με βάση κάποιον κανόνα. Η εργασία αυτή προτείνει έναν τρόπο για την εύρεση του ελάχιστου κόστους στον κύκλο ζωής ενός συστήματος παραγωγής. Η προτεινόμενη μέθοδος εφαρμόζεται σε μια περίπτωση μελέτης στην αυτοκινητοβιομηχανία και τέλος περιγράφεται ένα λογισμικό που υλοποιεί την προτεινόμενη μέθοδο καθώς και η UML (Unified Modelling Language) περιγραφή των βασικών οντοτήτων

A Deep Learning Model for Predictive Maintenance in Cyber-Physical Production Systems Using LSTM Autoencoders

Condition monitoring of industrial equipment, combined with machine learning algorithms, may significantly improve maintenance activities on modern cyber-physical production systems. However, data of proper quality and of adequate quantity, modeling both good operational conditions as well as abnormal situations throughout the operational lifecycle, are required. Nevertheless, this is difficult to acquire in a non-destructive approach. In this context, this study investigates an approach to enable a transition from preventive maintenance activities, that are scheduled at predetermined time intervals, into predictive ones. In order to enable such approaches in a cyber-physical production system, a deep learning algorithm is used, allowing for maintenance activities to be planned according to the actual operational status of the machine and not in advance. An autoencoder-based methodology is employed for classifying real-world machine and sensor data, into a set of condition-related labels. Real-world data collected from manufacturing operations are used for training and testing a prototype implementation of Long Short-Term Memory autoencoders for estimating the remaining useful life of the monitored equipment. Finally, the proposed approach is evaluated in a use case related to a steel industry production process

An Agent-Based System for Automated Configuration and Coordination of Robotic Operations in Real Time—A Case Study on a Car Floor Welding Process

This paper investigates the feasibility of using an agent-based framework to configure, control and coordinate dynamic, real-time robotic operations with the use of ontology manufacturing principles. Production automation agents use ontology models that represent the knowledge in a manufacturing environment for control and configuration purposes. The ontological representation of the production environment is discussed. Using this framework, the manufacturing resources are capable of autonomously embedding themselves into the existing manufacturing enterprise with minimal human intervention, while, at the same time, the coordination of manufacturing operations is achieved without extensive human involvement. The specific framework was implemented, tested and validated in a feasibility study upon a laboratory robotic assembly cell with typical industrial components, using real data derived from a car-floor welding process

Deep Learning for Estimating the Fill-Level of Industrial Waste Containers of Metal Scrap: A Case Study of a Copper Tube Plant

Advanced digital solutions are increasingly introduced into manufacturing systems to make them more intelligent. Intelligent Waste Management Systems in industries allow for data collection and analysis to make better-informed decisions, monitor and manage processes remotely, and improve waste management. In many industries, scrap is collected in large waste containers located on the factory floor, usually close to its source. In most cases, monitoring of waste containers’ fill levels is either manually performed by visual inspection by the operators working in close proximity or by employing intrusive mechanical systems such as weight sensors. This work presents a computer vision system that uses Deep Learning (DL) and Convolutional Neural Network (CNN) for the automated estimation of the fill level in industrial waste containers of metal scrap. The training method and parameters as well as the classification performance of VGG16 CNN that was retrained upon images collected in the field, are presented in detail. The proposed method has been validated upon an industrial case study from the copper tube production industry in which the fill level of two waste containers is estimated. A total of 9772 images were captured for the first container and 11,234 images for the second container. The VGG16 model achieved an accuracy from 77.5% to 95% on the testing dataset. The industrial case study demonstrates that the proposed computer vision system has sufficient accuracy for classifying the fill levels of metal scrap containers which allows for the development of waste management applications in industrial environments

Ontology-based framework enabling smart Product-Service Systems: Application of sensing systems for machine health monitoring

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