Energy efficiency of energy\u2013intensive industries as well as local energy distribution networks and multi-energy systems goes through the definition and implementation of innovative monitoring and control systems that can transform the collected data into correlated and usable information by means of a sustainable, well-designed, and upgradable energy management information system (EMIS).<\/p>\n\n\n\n
Furthermore, Energy efficiency is strictly linked to Operational Excellence through the implementation of a Prognostic and Health Management System (PHMS) capable to monitor critical productive assets, facilities and areas, to detect anomalies, identify fault causes and predict remaining useful life in order to support decision in real time driven by the state-of-the-art condition-based technology techniques and strategies for reliability centered maintenance.<\/p>\n\n\n\n
Both EMIS and PHMS combine software, hardware, and data to support people in managing energy at the process, system, facility, and enterprise levels. Their function can be grouped into four layers, devoted to:<\/p>\n\n\n\n
Layer1 includes the collection of the sensor data needed for the analysis, through proper IoT technologies and platforms, their integration and their adaptation to the format required by the subsequent operations. Layer2 requires the use of innovative Data Mining techniques, and the use of Machine Learning (ML) models is strictly required to develop – in Layer3 – smart monitoring functions devoted to both efficiency analysis and condition monitoring for fault detection, predictive maintenance, and control. ML-based technologies allow to implement efficiently the targets presented in layer2 and in particular they allows to define tools for:<\/p>\n\n\n\n
In the project, the algorithms that will be developed for diagnostics, prognostics and Energy Efficiency, focus to: <\/p>\n\n\n\n
Development of a digital twin of the product through the identification of cross-correlations to support Design Failure Mode Effect Analysis, Design for Manufacturability, critical to quality measurements identification and range setting and advanced root cause analysis support.
The Product Digital Twin is based on the concept of linking all the measures of test report of the item with the measurements of tests carried out at a sub-assembly level, associated to the relative data of inventory to guarantee the operational performance required by the product.
A task of the project will be, starting from the information collected and evaluated by EMIS\/PHMS, the development of methods and techniques for the implementation of Machine Learning algorithms on image recognition, defect identification, clustering and advanced statistical process control capable to exploit data retrieved by automated optical inspection machines.
Aim of the method is to develop a quality control process that detects defects and performs assessments based on machine learning models determining whether the target unit has any defects present that will negatively affect the resulting product.<\/p>\n","protected":false},"parent":0,"template":"","class_list":["post-1936","projects","type-projects","status-publish","hentry"],"acf":[],"yoast_head":"\n