![]() ![]() Methodologies: Collect the necessary data at the appropriate times and appropriate manufacturing production levels(e.g., equipment, work-cell, line, factory) to minimize the collection of “big data.”.This information includes any necessary health, diagnostic, and prognostic datasince this information will influence the control strategy. Standards for Critical Information: Identify the information required to make an informed decision with respect tosetting and updating control points.This work will focus on developing measurement science (including test methods, performance metrics, reference data sets, and tools) to enable vendor-neutral approaches and plug-and-play solutions to address the following challenges for PHM monitoring and control: The technical idea is to advance the state of the art in monitoring and control for PHM as the foundation for improved decision-making support and further automation at levels 0 through 3 of the ISA 95 manufacturing hierarchy. Technical Idea: Manufacturers need standards and guidance to effectively design, implement, verify, and validate monitoring, diagnostic, and prognostic technologies to enhance factory floor-level decision-making. Objective: Deliver methods, protocols, and tools for robust sensing, diagnostics, prognostics, and control that enable manufacturers to respond to planned (e.g., scheduled change-overs, new productivity targets) and un-planned (e.g., faults, failures) performance changes thereby enhancing the efficiency of smart manufacturing systems. This will result in improved decision-making support and greater automation with a focus on vendor-neutral approaches and plug-and-play solutions. The goal of the project is to promote advanced sensing, PHM, and control from ISA 95 manufacturing1 levels 0 (production process) through 3 (manufacturing operations management). Some proprietary solutions exist that integrate some manufacturing systems, but they apply to systems from one vendor and are often expensive and inaccessible to many manufacturers. No open standards exist that guide and manage sensing, prognostics and health management (PHM), and control at all levels (from the component to the system to the enterprise level). The simultaneous operation of complex systems within the factory increases the difficulty to determine equipment and process degradation, and resolve failures due to ill- or undefined information flow relationships. Complex system, sub-system, and component interactions within smart manufacturing systems make it challenging to determine the specific influences on process performance, especially during disruptions. There is increasing interest to leverage the same data to generate diagnostic (what happened) and prognostic (when something will happen) intelligence at the machine, process, and system levels. Early implementations of smart manufacturing technology have enabled manufacturers to use sensor-rich equipment and process the resulting data to provide decision-makers with information on many performance-related measures (e.g., machine status and utilization) and overall process health. The Prognostics, Health Management, and Control (PHMC) project will deliver methods, protocols, and tools for robust sensing, diagnostics, prognostics, and control that enable manufacturers to respond to planned and un-planned performance changes thereby enhancing the efficiency of smart manufacturing systems. The requirement for standards and guidance becomes more critical as manufacturers become capable of collecting larger volumes of data, the health of their equipment changes, and their processes evolve to meet changing consumer demand. Manufacturers need standards and guidance for how to effectively design, implement, verify, and validate monitoring, diagnostic, and prognostic technologies to enhance factory floor-level decision-making that directly impacts maintenance and control strategies. Industry Forum: Monitoring, Diagnostics, and Prognostics for Manufacturing Operations ![]()
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