RECLAIM: Toward a New Era of Refurbishment and Remanufacturing of Industrial Equipment

Refurbishment and remanufacturing are the industrial processes whereby used products or parts that constitute the product are restored. Remanufacturing is the process of restoring the functionality of the product or a part of it to “as-new” quality, whereas refurbishment is the process of restoring the product itself or part of it to “like-new” quality, without being as thorough as remanufacturing. Within this context, the EU-funded project RECLAIM presents a new idea on refurbishment and remanufacturing based on big data analytics, machine learning, predictive analytics, and optimization models using deep learning techniques and digital twin models with the aim of enabling the stakeholders to make informed decisions about whether to remanufacture, upgrade, or repair heavy machinery that is toward its end-of-life. The RECLAIM project additionally provides novel strategies and technologies that enable the reuse of industrial equipment in old, renewed, and new factories, with the goal of saving valuable resources by recycling equipment and using them in a different application, instead of discarding them after use. For instance, RECLAIM provides a simulation engine using digital twin in order to predict maintenance needs and potential faults of large industrial equipment. This simulation engine keeps the virtual twins available to store all available information during the lifetime of a machine, such as maintenance operations, and this information can be used to perform an economic estimation of the machine's refurbishment costs. The RECLAIM project envisages developing new technologies and strategies aligned with the circular economy and in support of a new model for the management of large industrial equipment that approaches the end of its design life. This model aims to reduce substantially the opportunity cost of retaining strategies (both moneywise and resourcewise) by allowing relatively old equipment that faces the prospect of decommissioning to reclaim its functionalities and role in the overall production system

Decision Making with STPA through Markov Decision Process, a Theoretic Framework for Safe Human-Robot Collaboration

During the last decades, collaborative robots capable of operating out of their cages are widely used in industry to assist humans in mundane and harsh manufacturing tasks. Although such robots are inherently safe by design, they are commonly accompanied by external sensors and other cyber-physical systems, to facilitate close cooperation with humans, which frequently render the collaborative ecosystem unsafe and prone to hazards. We introduce a method that capitalizes on partially observable Markov decision processes (POMDP) to amalgamate nominal actions of the system along with unsafe control actions posed by the System Theoretic Process Analysis (STPA). A decision-making mechanism that constantly prompts the system into a safer state is realized by providing situation awareness about the safety levels of the collaborative ecosystem by associating the system safety awareness with specific groups of selected actions. POMDP compensates the partial observability and uncertainty of the current state of the collaborative environment and creates safety screening policies that tend to make decisions that balance the system from unsafe to safe states in real time during the operational phase. The theoretical framework is assessed on a simulated human–robot collaborative scenario and proved capable of identifying loss and success scenarios

Συστημική προσέγγιση ασφάλειας για συνεργατικές εφαρμογές ανθρώπου και ρομπότ

Τhis dissertation aims to provide a real-time decision-making mechanism tool, which capitalizes on the determined unsafe control actions extracted from the STPA, in order to prompt the system into the selection of an action, based on the current and past observations and actions, that will transit it to a next state that belongs to a higher safety level. Therefore, the proposed HRC safety method is targeted as a step forward towards a generic safe HRC framework for various robotic platforms, which perform collaborative tasks with human operators. We employ an abstraction for clustering the plethora of the normal and unsafe states of the system, and we formulate the problem as a partial observable Markov decision process to determine a policy graph that operates in real-time and deduces the safe and unsafe workflows of the system. Thus, this dissertation provides a theoretical framework for reliable and safe HRC in unconstrained environments and shows great potential in practical applications. Moreover, the proposed safety method offers a new perspective to view and implement safe HRC by combining various methodologies in different research fields, including observation theory, graph policy, systems, and control theory.Αυτή η διατριβή στοχεύει να παράσχει έναν μηχανισμό λήψης αποφάσεων, όπως ένα εργαλείο σε πραγματικό χρόνο, το οποίο αξιοποιεί τις καθορισμένες μη ασφαλείς ενέργειες ελέγχου που εξάγονται από τη μέθοδο STPA, προκειμένου να ωθήσει το σύστημα στην επιλογή μιας ενέργειας, με βάση τις τρέχουσες και προηγούμενες παρατηρήσεις και ενέργειες, που θα το μεταφέρουν σε μια επόμενη κατάσταση που ανήκει σε υψηλότερο επίπεδο ασφάλειας. Χρησιμοποιούμε μια αφαίρεση για τη ομαδοποίηση της πληθώρας των κανονικών και μη ασφαλών καταστάσεων του συστήματος και διατυπώνουμε το πρόβλημα ως μια μερική παρατηρήσιμη διαδικασία απόφασης Makrov προκειμένου να προσδιορίσουμε ένα γράφημα που λειτουργεί σε πραγματικό χρόνο και συνάγει ασφαλές και το μη ασφαλές ροές εργασιών του συστήματος. Έτσι, η εργασία σε αυτή τη διατριβή δεν παρέχει μόνο ένα θεωρητικό πλαίσιο για αξιόπιστο και ασφαλές HRC σε μη περιορισμένα περιβάλλοντα, αλλά δείχνει επίσης μεγάλες δυνατότητες σε πρακτικές εφαρμογές. Επιπλέον, η προτεινόμενη μέθοδος ασφάλειας παρέχει μια νέα προοπτική για την προβολή και την εφαρμογή ασφαλούς HRC συνδυάζοντας διάφορες μεθοδολογίες σε διαφορετικά ερευνητικά πεδία, συμπεριλαμβανομένης της θεωρίας παρατήρησης, της ομαλότητας των γραφημάτων και των συστημάτων και της θεωρίας ελέγχου

Using an Atlas-Based Approach in the Analysis of Gene Expression Maps Obtained by Voxelation

Part 8: First Workshop on Algorithms for Data and Text Mining in Bioinformatics (WADTMB 2012)International audienceThe integration of gene expression datasets with gene function information provides valuable insights in unraveling the molecular mechanisms of the brain. In this paper, gene expression maps, acquired by the technique of voxelation, are analyzed using an atlas-based framework and the extracted spatial information is employed to organize genes in significant clusters. Moreover, gene function enrichment analysis of clusters enables exploring the relationships among brain regions, gene expressions and gene functions. Our work confirms the hypothesis that genes of similar spatial expression patterns display similar functions indicating that our methodology could assist in the functional identification of unannotated genes