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

The genomic landscape of ANCA-associated vasculitis: Distinct transcriptional signatures, molecular endotypes and comparison with systemic lupus erythematosus

IntroductionAnti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitides (AAVs) present with a complex phenotype and are associated with high mortality and multi-organ involvement. We sought to define the transcriptional landscape and molecular endotypes of AAVs and compare it to systemic lupus erythematosus (SLE).MethodsWe performed whole blood mRNA sequencing from 30 patients with AAV (granulomatosis with polyangiitis/GPA and microscopic polyangiitis/MPA) combined with functional enrichment and network analysis for aberrant pathways. Key genes and pathways were validated in an independent cohort of 18 AAV patients. Co-expression network and hierarchical clustering analysis, identified molecular endotypes. Multi-level transcriptional overlap analysis to SLE was based on our published data from 142 patients.ResultsWe report here that “Pan-vasculitis” signature contained 1,982 differentially expressed genes, enriched in leukocyte differentiation, cytokine signaling, type I and type II IFN signaling and aberrant B-T cell immunity. Active disease was characterized by signatures linked to cell cycle checkpoints and metabolism pathways, whereas ANCA-positive patients exhibited a humoral immunity transcriptional fingerprint. Differential expression analysis of GPA and MPA yielded an IFN-g pathway (in addition to a type I IFN) in the former and aberrant expression of genes related to autophagy and mRNA splicing in the latter. Unsupervised molecular taxonomy analysis revealed four endotypes with neutrophil degranulation, aberrant metabolism and B-cell responses as potential mechanistic drivers. Transcriptional perturbations and molecular heterogeneity were more pronounced in SLE. Molecular analysis and data-driven clustering of AAV uncovered distinct transcriptional pathways that could be exploited for targeted therapy.DiscussionWe conclude that transcriptomic analysis of AAV reveals distinct endotypes and molecular pathways that could be targeted for therapy. The AAV transcriptome is more homogenous and less fragmented compared to the SLE which may account for its superior rates of response to therapy

A Cloud-to-Edge Approach to Support Predictive Analytics in Robotics Industry

Data management and processing to enable predictive analytics in cyber physical systems holds the promise of creating insight over underlying processes, discovering anomalous behaviours and predicting imminent failures threatening a normal and smooth production process. In this context, proactive strategies can be adopted, as enabled by predictive analytics. Predictive analytics in turn can make a shift in traditional maintenance approaches to more effective optimising their cost and transforming maintenance from a necessary evil to a strategic business factor. Empowered by the aforementioned points, this paper discusses a novel methodology for remaining useful life (RUL) estimation enabling predictive maintenance of industrial equipment using partial knowledge over its degradation function and the parameters that are affecting it. Moreover, the design and prototype implementation of a plug-n-play end-to-end cloud architecture, supporting predictive maintenance of industrial equipment is presented integrating the aforementioned concept as a service. This is achieved by integrating edge gateways, data stores at both the edge and the cloud, and various applications, such as predictive analytics, visualization and scheduling, integrated as services in the cloud system. The proposed approach has been implemented into a prototype and tested in an industrial use case related to the maintenance of a robotic arm. Obtained results show the effectiveness and the efficiency of the proposed methodology in supporting predictive analytics in the era of Industry 4.0

Προσαρμοστικός σχεδιασμός κι έλεγχος σε κυβερνο-φυσικά συστήματα παραγωγής

The objective of this work is the study of the production systems under the scope of cyber-physical production systems (CPPSs) and in terms of enabling their reconfiguration towards increased automation and flexibility to volatile working conditions as well as market demands. Towards that end, methods corresponding to different layers of the ISA95 automation pyramid, mapped to the layers of the 5C architecture for cyber-physical systems, have been connected integrating different layers of the architecture and tested in the under investigation CPPSs. First, the dynamic closed-loop control of a CPPS has been studied for enabling and controlling safe human-robot collaborative assembly operations. An initial implementation is evaluated in a specific use case and its results are compared regarding the use of one or more sensors. The comparison is performed in terms of system response time to detect human presence within a predefined safety zone. Afterwards and considering the manual assembly operations, their adaptive planning and reconfiguration of the assembly station are discussed. The implementation of the digital-twin approach is presented to digitally close the loop between physical and virtual system thus enabling a cost-effective improvement of the planning, commissioning as well as the entire lifecycle of human-based production processes. A case study from the warehouse, intra-factory logistics operation, in the white goods industry, demonstrates the feasibility of the proposed approach. Also, as part of the third case study, a holistic framework for reconfigurable cyber-physical production systems is discussed, enabled by container technologies. The presented approach enhances flexibility in a cyber-physical production system (CPPS) through the dynamic reconfiguration of the automation system and the production schedule, based on occurring events. The proposed solution has been implemented on a software framework and applied in a small scale CPPS coming from the automotive industry. Finally, the contribution of the integration 5C layers for the implementation, deployment, and reconfiguration of CPPS functionalities converting conventional manufacturing processes to smart ones has been assessed via a set of CPPS indicators in terms of contribution to their automation level.Αντικείμενο αυτής της εργασίας είναι η μελέτη των συστημάτων παραγωγής ως κυβερνο-φυσικών συστημάτων επικεντρώνοντας στον προσαρμοστικό σχεδιασμό/προγραμματισμό κι έλεγχό τους. Απώτερος σκοπός είναι η δυνατότητα αναδιαμόρφωσής τους για την αύξηση της αυτοματοποίησης και της ευελιξίας στις συνθήκες εργασίας καθώς και σε μεταβαλλόμενες απαιτήσεις παραγωγής. Για αυτό το λόγο, μέθοδοι, που μεταφράζονται σε διαφορετικά επίπεδα της πυραμίδας αυτοματισμού, μέσω της αρχιτεκτονικής 5C για κυβερνο-φυσικά συστήματα, έχουν διερευνηθεί κι ενσωματωθεί σε υπό-μελέτη κυβερνο-φυσικά συστήματα. Πρώτον, μελετάται ο δυναμικός έλεγχος κλειστού βρόχου ενός κυβερνο-φυσικού συστήματος σχετικά με την ασφαλή συνεργασία ανθρώπου-ρομπότ σε ένα περιβάλλον εργασίας. Μια αρχική εφαρμογή αξιολογείται σε μια συγκεκριμένη περίπτωση χρήσης και συγκρίνονται τα αποτελέσματά της σχετικά με τη χρήση ενός ή περισσοτέρων αισθητήρων. Η σύγκριση πραγματοποιείται με βάση τον χρόνο απόκρισης του συστήματος για την ανίχνευση της ανθρώπινης παρουσίας εντός μιας προκαθορισμένης ζώνης ασφαλείας. Στη συνέχεια, εξετάζονται οι χειρωνακτικές εργασίες συναρμολόγησης, συζητείται ο προσαρμοστικός προγραμματισμός και ο επαναπροσδιορισμός του σταθμού συναρμολόγησης. Η εφαρμογή ψηφιακού διδύμου παρουσιάζεται για να κλείσει ψηφιακά το βρόχο μεταξύ φυσικού και εικονικού συστήματος, επιτρέποντας έτσι μια αποτελεσματική, από πλευράς κόστους, βελτίωση του σχεδιασμού, της ανάθεσης καθώς και ολόκληρου του κύκλου ζωής των διαδικασιών παραγωγής με βάση τον άνθρωπο. Μια μελέτη περίπτωσης σε χώρο αποθήκης καταδεικνύει τη σκοπιμότητα και αποτελεσματικότητα της προτεινόμενης προσέγγισης. Επίσης, στο πλαίσιο της τρίτης μελέτης, συζητείται ένα ολιστικό πλαίσιο για τα αναδιαμορφώσιμα κυβερνο-φυσικά συστήματα παραγωγής, που υλοποιούνται από τις τεχνολογίες των υπηρεσιών πακέτων λογισμικού (containers). Η παρουσιαζόμενη προσέγγιση ενισχύει την ευελιξία σε ένα κυβερνο-φυσικό σύστημα παραγωγής μέσω της δυναμικής αναδιάρθρωσης του συστήματος αυτοματισμού και του χρονοδιαγράμματος παραγωγής, με βάση τα συμβάντα. Η προτεινόμενη λύση υλοποιήθηκε σε μια εφαρμογή λογισμικού και εφαρμόστηκε σε ένα κυβερνο-φυσικό σύστημα παραγωγής μικρής κλίμακας που προέρχεται από την αυτοκινητοβιομηχανία. Τέλος, η συμβολή των στρωμάτων ολοκλήρωσης της αρχιτεκτονικής 5C για την υλοποίηση και την ανάπτυξη λειτουργιών CPPS, που μετατρέπει τις συμβατικές διαδικασίες παραγωγής σε έξυπνες, έχει αξιολογηθεί μέσω ενός συνόλου δεικτών επιπέδου ευελιξίας μέσω του αυτοματισμού και δεικτών χαρακτηριστικών των κυβερνο-φυσικών συστημάτων παραγωγής

Καταγραφή των αμυντικών ενεργειών των τερματοφυλάκων στην τελική φάση των ομίλων του Παγκόσμιου Κυπέλλου της Βραζιλίας το 2014

Σκοπός της παρούσας έρευνας είναι να μελετήσει και να καταγράψει την αμυντική συμπεριφορά των τερματοφυλάκων του παγκοσμίου κυπέλλου της Βραζιλίας του 2014 κατά την φάση των ομίλων. Μέσω του προγράμματος βίντεο-ανάλυσης longo match αναλύθηκαν συνολικά 48 αγώνες από τις 32 ομάδες που συμμετείχαν στην φάση των ομίλων. Οι παράμετροι που αναλύθηκαν είναι το είδος της επίθεσης, η ζώνη της τελικής πάσας, το είδος της τελικής ενέργειας, το μέρος του σώματος με το οποίο εκτελέστηκε η ενέργεια, η γωνία εκτέλεσης της επιθετικής ενέργειας, το είδος της ενέργειας του τερματοφύλακα καθώς και η φυσική δραστηριότητα του τερματοφύλακα. Τα αποτελέσματα έδειξαν ότι οι περισσότερες επιθέσεις (1073) πραγματοποιήθηκαν σε φάση ανοιχτού παιχνιδιού (78%) και οι περισσότερες προσπάθειες για την επίτευξη τέρματος έγιναν από τον κεντρικό άξονα (82%). Στις επιθετικές ενέργειες αυτές οι τερματοφύλακες αντέδρασαν σε μεγάλο ποσοστό (30%), μπλοκάροντας την μπάλα στο κέντρο του τέρματος είτε με κίνηση προς την μπάλα, είτε από σταθερή θέση. Τα αποτελέσματα των σουτ (904) έδειξαν ότι η κύρια ενέργεια των τερματοφυλάκων σε μεγάλο βαθμό, είναι η εκτίναξη (45%), γεγονός που δηλώνει την δυσκολία που αντιμετώπισαν στα σουτ των επιθετικών. Από την παρούσα έρευνα προκύπτει το μέγεθος της συμβολής του τερματοφύλακα στο αμυντικό παιχνίδι της ομάδας. Ακόμη από τα αποτελέσματα της παρούσης μελέτης προκύπτουν συμπεράσματα για τους προπονητές και τον τρόπο διεξαγωγής της προπονητικής διαδικασίας.The aim of this investigation was to study and record the defensive actions of the goalkeepers during the World cup which took place in Brazil during 2014. The software used was the Video Analysis Longo Match, where 48 matches were analysed with 32 teams taking part in the games. The variables analysed were: the type of attack, the zone of the final shoot, the body part with which the final move was carried out, the angle of the shoot and the goalkeeper’s defense action. The results illustrated that most offensive actions (1073) took place in open play game (51%) and the most frequent attempts to score occurred from the central zone (82%). A large percentage of the reactions of goalkeepers to offensive moves was to block the ball at the center zone (30%) of the goal post, either by dive or from a stable position. The results regarding shoots (904) underlined that the main action of goalkeepers was diving (45%) indicating the difficulty they faced during the attacks. This study emphasizes that goalkeepers play a defining role in the outcome of the game. Finally the results provide valuable insight for coaches and their training techniques

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

On a containerized approach for the dynamic planning and control of a cyber - physical production system

The increased complexity of modern production systems requires sophisticated system control approaches to maintain high levels of flexibility. Furthermore, the request for customized production with the introduction of heterogeneous production resources, increases the diversity of manufacturing systems making their reconfiguration complex and time consuming. In this paper, an end-to-end approach for reconfigurable cyber-physical production systems is discussed, enabled by container technologies. The presented approach enhances flexibility in a cyber-physical production system (CPPS) through the dynamic reconfiguration of the automation system and the production schedule, based on occurring events. High-level management of manufacturing operations is performed on a centralized node while the data processing and execution control is handled at the network edge. Runtime events are generated at the edge and in smart connected devices via means of a variant of IEC61499 function blocks. Software containers manage the deployment and low-level orchestration of FBs at the edge devices. All aspects of the proposed solution have been implemented on a software framework and applied in a small scale CPPS coming from the automotive industry.CC BY-NC-ND 4.0</p

Estimating Remaining Useful Life: A Data-Driven Methodology for the White Goods Industry

A Predictive Maintenance strategy for a complex machine requires a sophisticated and non-trivial analytical stage to provide accurate and trusted predictions. It must be planned and carried out carefully to maximise the information extracted from available data. The SERENA project provided an excellent methodological framework and a solid technical and software foundation to deliver a robust and applicable Predictive Maintenance solution for the White Goods industry. The proposed data-driven methodology was applied on a real use case, that is, estimating the degradation trend of industrial foaming machines and predicting their remaining useful life. The models were built based on historical data and are applied in real-time adjourning their predictions every time new data are collected. The results are promising and highlight how the proposed methodology can be used to achieve a fairly accurate estimate of machinery degradation and plan maintenance interventions accordingly, with significant savings in terms of costs and time