iDSS-Decision Support System for Incident Detection and Maintenance in Industries

The Integrated Decision-Support-System for Incident Detection and Maintenance in Industries-iDSS uses semantic modelling of manufacturing knowledge spaces and processes, of shop floor actors and machinery, of daily production activities and flaws of Factories of the Future. Furthermore, it collects evidence from real-time data, as well as evidence in the form of detected incidents, coupled with prescribed response strategies. All this information are processed and analysed, so as to offer feedback to the shop floor in terms of actionable knowledge and recommendations, including maintenance operations and schedules. Consequently, it addresses simultaneously challenges emerging from the shop floor production activities and manufacturing operations, including maintenance requirements and incident detection, using a combination of real-time data, manufacturing knowledge spaces and incident response algorithms and strategies. The component is being developed and demonstrated first at the Industrial Lab. The first version was available in the end of M20 of the project (08.2016). The Integrated Decision-Support-System constitutes an infrastructure that comprises in total the following three components: 1) Incident Management Tools:This subcomponent represents a substantial role in the everyday activities of actors involved in use case scenarios in all pilot demonstrations supporting workers’ safety and comfort. The main achievement of the module is the detection of proactive and reactive incidents on the shop floor. The system detects and alerts the possibility of a risky condition (proactive incident) and incidents after their occurrence (reactive) based on real-time data. In both cases, the tool performs the corresponding predefined countermeasures. In the following video a short description of the incident detection tools is provided: https://www.youtube.com/watch?v=7OoevNAIP6A 2) Maintenance Toolkit:This subcomponent represents a substantial role in the everyday activities of actors involved in use case scenarios in all pilot demonstrations. The basic responsibility of the module is to monitor and supervise in real time the production processes and diagnose possible problems, flaws or malfunctions while triggering events for activating maintenance procedures or safety mechanisms 3) Real-time Visual & Data Analytics Module: This module is responsible for correlating, combining and analysing heterogeneous data acquired from the smart sensor network in order to evaluate the shop floor and production operations, supporting comparative assessment of worker’s situation and suggesting role-based actions. It also presents different state-related views of the supervised shop floor. It provides the capability to compare and assess the situation of the workers in a visual and comprehensive way taking into account properties of human cognition, perception and reasoning. It combines, correlates and visualizes large, complex and heterogeneous data providing a multi-factorial exploration in the spatio-temporal domain, assisting end-users/managers to detect patterns, templates and crucial points that are difficult to detect otherwise.