Towards Predictive Maintenance for Flexible Manufacturing Using FIWARE

Industry 4.0 has shifted the manufacturing related processes from conventional processes within one organization to collaborative processes across different organizations. For example, product design processes, manufacturing processes, and maintenance processes across different factories and enterprises. This complex and competitive collaboration requires the underlying system architecture and platform to be flexible and extensible to support the demands of dynamic collaborations as well as advanced functionalities such as big data analytics. Both operation and condition of the production equipment are critical to the whole manufacturing process. Failures of any machine tools can easily have impact on the subsequent value-added processes of the collaboration. Predictive maintenance provides a detailed examination of the detection, location and diagnosis of faults in related machineries using various analyses. In this context, this paper explores how the FIWARE framework supports predictive maintenance. Specifically, it looks at applying a data driven approach to the Long Short-Term Memory Network (LSTM) model for machine condition and remaining useful life to support predictive maintenance using FIWARE framework in a modular fashion

Personality Traits in Miners with Past Occupational Elemental Mercury Exposure

In this study, we evaluated the impact of long-term occupational exposure to elemental mercury vapor (Hg(0)) on the personality traits of ex-mercury miners. Study groups included 53 ex-miners previously exposed to Hg(0) and 53 age-matched controls. Miners and controls completed the self-reporting Eysenck Personality Questionnaire and the Emotional States Questionnaire. The relationship between the indices of past occupational exposure and the observed personality traits was evaluated using Pearson’s correlation coefficient and on a subgroup level by machine learning methods (regression trees). The ex-mercury miners were intermittently exposed to Hg(0) for a period of 7–31 years. The means of exposure-cycle urine mercury (U-Hg) concentrations ranged from 20 to 120 μg/L. The results obtained indicate that ex-miners tend to be more introverted and sincere, more depressive, more rigid in expressing their emotions and are likely to have more negative self-concepts than controls, but no correlations were found with the indices of past occupational exposure. Despite certain limitations, results obtained by the regression tree suggest that higher alcohol consumption per se and long-term intermittent, moderate exposure to Hg(0) (exposure cycle mean U-Hg concentrations > 38.7 < 53.5 μg/L) in interaction with alcohol remain a plausible explanation for the depression associated with negative self-concept found in subgroups of ex-mercury miners. This could be one of the reason for the higher risk of suicide among miners of the Idrija Mercury Mine in the last 45 years

6 A Service-oriented, Semantic Approach to Data Integration for an Internet of Things Supporting Autonomous Cooperating Logistics Processes

objects capable of taking autonomous decisions based on decentral information processing resonates strongly with research in the field of autonomous cooperating logistics processes. The characteristics of the IT landscape underlying autonomous cooperating logistics processes pose a number of challenges towards data integration. The heterogeneity of the data sources, their highly distributed nature along with their availability, make the application of traditional approaches problematic. The field of semantic data integration offers potential solutions to address these issues. This contribution aims to examine in what way an adequate approach towards data integration may be facilitated on that basis. It subsequently proposes a service-oriented, ontology-based mediation approach to data integration for an Internet of Things supporting autonomous cooperating logistics processes. 6.1 Introduction and Background The concepts and technologies of the Internet of Things are rapidly becoming significant to challenges arising in the field of logistics. With today’s globalised markets in a state of accelerating structural change, planning and control strategie

A Review of RFID in Supply Chain Management: 2000–2015

This paper presents a systematic literature review of papers that were published in academic journals on the applications of radio frequency identification (RFID) in supply chain management between the years 2000 and 2015. As the literature on RFID is not confined to specific disciplines or repositories, this paper proposes a discipline-based framework for classifying RFID literature. Five main classification categories are used in this paper: technology, supply chain management, research methodology, application industries, and social aspects. The paper then focuses on the category of supply chain management and reviews 1187 articles that were published between 2000 and 2015 in rated journals. All the papers reviewed are further classified into eight subclasses under this category of supply chain management. The review yields useful insights into the anatomy of RFID literature in supply chain management, enhances evidence-based knowledge, and contributes to informing practice, policymaking and future research. The review reveals that even presently, despite technical and cost challenges, enormous potential exists for the application of RFID in several areas of supply chain management and the prospects are likely to grow into the future. Since RFID solutions have emerged primarily over only the past 20 years, significant research opportunities exist and would need to be addressed to continue to support the technology’s maturation, evaluation, adoption, implementation, and diffusion