A framework for fault detection and diagnostics of articulated collaborative robots based on hybrid series modelling of Artificial Intelligence algorithms

Smart factories build on cyber-physical systems as one of the most promising technological concepts. Within smart factories, condition-based and predictive maintenance are key solutions to improve competitiveness by reducing downtimes and increasing the overall equipment effectiveness. Besides, the growing interest towards operation flexibility has pushed companies to introduce novel solutions on the shop floor, leading to install cobots for advanced human-machine collaboration. Despite their reliability, also cobots are subjected to degradation and functional failures may influence their operation, leading to anomalous trajectories. In this context, the literature shows gaps in what concerns a systematic adoption of condition-based and predictive maintenance to monitor and predict the health state of cobots to finally assure their expected performance. This work proposes an approach that leverages on a framework for fault detection and diagnostics of cobots inspired by the Prognostics and Health Management process as a guideline. The goal is to habilitate first-level maintenance, which aims at informing the operator about anomalous trajectories. The framework is enabled by a modular structure consisting of hybrid series modelling of unsupervised Artificial Intelligence algorithms, and it is assessed by inducing three functional failures in a 7-axis collaborative robot used for pick and place operations. The framework demonstrates the capability to accommodate and handle different trajectories while notifying the unhealthy state of cobots. Thanks to its structure, the framework is open to testing and comparing more algorithms in future research to identify the best-in-class in each of the proposed steps given the operational context on the shop floor

Structural and rheological properties of medium-chain triacylglyceride oleogels

This research studied the effect of gelator molecules on structural properties of oleogels containing medium-chain triacylglycerides (MCTs). To this aim, a large selection of gelators (rapeseed wax RAW, rice wax RW, sunflower wax SW, beeswax BW, monoglycerides MG, and \u3b3-oryzanol and \u3b2-sitosterol mixture \u3b3+\u3b2) at increasing concentrations (5\u201315% w/w) was considered. Results showed that RAW was not able to structure MCT at any concentration. However, addition of 5% (w/w) of MG, SW and BW resulted to a self-standing gel. Regarding \u3b3+\u3b2 and RW, gel structures were generated at 10% (w/w). By increasing the concentration, a reinforcement of the network strength was highlighted by the progressive increase of the rheological parameters. The strongest oleogel obtained by \u3b3+\u3b2 at 10% (w/w) and further BW and RW at 15% (w/w) concentration. These findings could provide interesting information in the choice of the best performing MCT structuring agent for intended food applications

Simulation-supported framework for job shop scheduling with genetic algorithm

The Job Shop Scheduling Problem (JSSP) is recognized to be one of the most difficult scheduling problems, being NP-complete. During years, many different solving techniques were developed: some techniques are focused on the development of optimization algorithms, whilst others are based on simulation models. Since the 80s, it was recognized that a combination of the two could be of big advantage, matching advantages from both sides. However, this research stream has not been followed to a great extent. The goal of this study is to propose a novel scheduling tool able to match these two really different techniques in one common framework in order to fill this gap in literature. The base of the framework is composed by a genetic algorithm (GA) and a simulation model is introduced into the evaluation of the fitness function, due to the inability of GAs in taking into account the real performances of a production system. An additional purpose of this research is to improve the collaboration between academic and industrial worlds on the topic, through an application of the novel scheduling framework to an industrial case. The implementation to the industrial case also suggested an improvement of the tool: The introduction of the stochasticity into the proposed scheduling framework in order to consider the variable nature of the production systems

the neutrophil activating protein of helicobacter pylori crosses endothelia to promote neutrophil adhesion in vivo

Helicobacter pylori induces an acute inflammatory response followed by a chronic infection of the human gastric mucosa characterized by infiltration of neutrophils/polymorphonuclear cells (PMNs) and mononuclear cells. The H. pylori neutrophil-activating protein (HP-NAP) activates PMNs, monocytes, and mast cells, and promotes PMN adherence to the endothelium in vitro. By using intravital microscopy analysis of rat mesenteric venules exposed to HP-NAP, we demonstrated, for the first time in vivo, that HP-NAP efficiently crosses the endothelium and promotes a rapid PMN adhesion. This HP-NAP-induced adhesion depends on the acquisition of a high affinity state of β2 integrin on the plasma membrane of PMNs, and this conformational change requires a functional p38 MAPK. We also show that HP-NAP stimulates human PMNs to synthesize and release a number of chemokines, including CXCL8, CCL3, and CCL4. Collectively, these data strongly support a central role for HP-NAP in the inflammation process in vivo: indeed, HP-NAP not only recruits leukocytes from the vascular lumen, but also stimulates them to produce messengers that may contribute to the maintenance of the flogosis associated with the H. pylori infection

Role of simulation in industrial engineering: focus on manufacturing systems

Simulation has recently grown its importance thanks to the Industry 4.0, based on CPS (Cyber-Physical Systems). Especially, simulation is becoming more central for improved decision-making. The article provides a literature analysis of peer-reviewed surveys about simulation applications in industrial engineering in manufacturing. In particular, a three-axis framework, called 3D-SAM, is proposed to classify the applications and to critically analyse them. The framework can be used as a first input for the Cognition Level of CPS (5C architecture for CPS development) in order to develop integrated simulation models within a Decision Support System (DSS)

A Conceptual Model of the IT Ecosystem for Asset Management in the Global Manufacturing Context

This research proposes a new conceptual model of the IT ecosystem required in the scope of global asset management. To accomplish this aim, the functionalities required by maintenance management are integrated with those required by Asset Management needs, thus extending the current scope of work of extant IT systems to a lifecycle management perspective. The allocation of the functionalities to three asset control levels (operational, tactical, strategic) is propaedeutic to derive the IT ecosystem structure based on three main software families. The model has been built along a collaborative project with a world leading company in the food sector. Lessons learnt on the proposed IT ecosystem for a centralized AM over geographically dispersed production plants are reported

Information as a key dimension to develop industrial asset management in manufacturing

Purpose: The purpose of this work is to investigate industrial asset management (AM) in manufacturing. After depicting gaps for AM in this sector, the role of information as a key dimension is considered to realise a summary of challenges and advices for future development. Design/methodology/approach: The work is grounded on an extensive systematic literature review. Considering the eligible documents, descriptive statistics are provided and a content analysis is performed, both based on a sector-independent normative-based framework of analysis. Findings: AM principles, organisation and information are the dimensions defined to group ten areas of interest for AM in manufacturing. Information is the major concern for an effective AM implementation. Moreover, Internet of Things and big data management and analytics, as well as data modelling and ontology engineering, are the major technologies envisioned to advance the implementation of AM in manufacturing. Research limitations/implications: The identified challenges and advices for future development may serve to stimulate further research on AM in manufacturing, with special focus on information and data management. The sector-independent normative-based framework may also enable to analyse AM in different contexts of application, thus favouring cross-sectorial comparisons. Originality/value: Industries with higher operational risk, like Oil&Gas and infrastructure, are advanced in AM, while others, like some in manufacturing, are laggard in this respect. This literature review is the first of a kind addressing AM in manufacturing and depicts the state-of-the-art to pave the way for future research and development

Data taxonomy to manage information and data in Maintenance Management

Nowadays Maintenance Management (MM) is covering a primary role for competitiveness in manufacturing. The advent of Asset Management (AM), in which MM is a core function, enlarges the scope MM was used to. Besides, digitalization has brought a vast amount of information and data sources that MM may exploit to improve its processes and asset-related decision-making. This evolution of MM has brought a lot of opportunities but also various criticalities about information and data management. Data models are envisioned to provide significant support to this end. However, a common reference data taxonomy is needed for the correct development of data models. This work aims at exploring how the data taxonomy could help in addressing the current criticalities by synthesizing most information and data classes that support MM. The data taxonomy, along with other elements, like data models, effectively support companies in improving the management of their information and data. The usefulness of a data taxonomy is proved thanks to action research in a company within the automotive sector aiming at improving the MM process