Cyber-physical manufacturing systems: An architecture for sensor integration, production line simulation and cloud services

none9noThe pillars of Industry 4.0 require the integration of a modern smart factory, data storage in the Cloud, access to the Cloud for data analytics, and information sharing at the software level for simulation and hardware-in-the-loop (HIL) capabilities. The resulting cyber-physical system (CPS) is often termed the cyber-physical manufacturing system, and it has become crucial to cope with this increased system complexity and to attain the desired performances. However, since a great number of old production systems are based on monolithic architectures with limited external communication ports and reduced local computational capabilities, it is difficult to ensure such production lines are compliant with the Industry 4.0 pillars. A wireless sensor network is one solution for the smart connection of a production line to a CPS elaborating data through cloud computing. The scope of this research work lies in developing a modular software architecture based on the open service gateway initiative framework, which is able to seamlessly integrate both hardware and software wireless sensors, send data into the Cloud for further data analysis and enable both HIL and cloud computing capabilities. The CPS architecture was initially tested using HIL tools before it was deployed within a real manufacturing line for data collection and analysis over a period of two months.openPrist Mariorosario; Monteriu' Andrea; Pallotta Emanuele; Cicconi Paolo; Freddi Alessandro; Giuggioloni Federico; Caizer Eduard; Verdini Carlo; Longhi SauroPrist, Mariorosario; Monteriu', Andrea; Pallotta, Emanuele; Cicconi, Paolo; Freddi, Alessandro; Giuggioloni, Federico; Caizer, Eduard; Verdini, Carlo; Longhi, Saur

Online Fault Detection: A Smart Approach for Industry 4.0

none8noThe fourth industrial age takes the manufacturing factory to a new level by introducing smart, extendible, flexible, modular and customized mass production technologies. Production lines or machines need to be integrated at the management level to be industry 4.0 compliant: in this way they can create and optimize a customer-oriented production, while constantly maintaining good performance conditions. In this context, one of the main challenges is the possibility to detect faults as fast as possible, to accurately diagnose those faults which can negatively affect the overall production cycle, and finally address them before it is too late. Due to the great importance that electric motors play in this context, an online smart algorithm for fault detection in electric motors is proposed in this paper. The effectiveness of the proposed method has been validated by applying it on an experimental benchmark, where the results show that the method is accurate and fast in detection of faults.nonePrist Mariorosario; Monteriù Andrea; Freddi Alessandro; Cicconi Paolo; Giuggioloni Federico; Caizer Eduard; Verdini Carlo; Longhi SauroPrist, Mariorosario; Monteriù, Andrea; Freddi, Alessandro; Cicconi, Paolo; Giuggioloni, Federico; Caizer, Eduard; Verdini, Carlo; Longhi, Saur

Le azioni delle banche quotate: strumenti di valutazione. Rapporto semestrale

Consiglio Nazionale delle Ricerche - Biblioteca Centrale - P.le Aldo Moro, 7, Rome / CNR - Consiglio Nazionale delle RichercheSIGLEITItal

Le azioni delle banche quotate: strumenti di valutazione. Rapporto semestrale

Consiglio Nazionale delle Ricerche - Biblioteca Centrale - P.le Aldo Moro, 7, Rome / CNR - Consiglio Nazionale delle RichercheSIGLEITItal

Cyber-Physical Manufacturing Systems for Industry 4.0: Architectural Approach and Pilot Case

The pillars of Industry 4.0 require a modern smart factory to be integrated, store data into the Cloud, access the Cloud for data analytics and share information at software level for simulation and Hardware-In-the-Loop capabilities. The resulting Cyber-Physical System is often called Cyber-Physical Manufacturing System, and it becomes fundamental to cope with the increased system complexity and the desired performances. However, since a lot of old production systems are based on monolitic architectures, with limited external communication ports and reduced local computational capabilities, it is very difficult to make such production lines compliant to Industry 4.0 pillars. Wireless Sensor Network is a solution for the smart connection of a production line to a Cyber-Physical System architecture, data processing through Cloud Computing. The scope of this research work is to propose an intermediate layer within the architecture that allows each device, production line and machine to be independently connected despite the adopted protocol. The solution is based on OSGi Framework, which is able to seamlessly integrate both hardware and software wireless sensors, send data into the Cloud for further data analysis, and grant both Hardware-In-the-Loop and Cloud Computing capabilities. A general description of the architecture is here proposed, together with preliminary results on a real manufacturing line for data collection and analysis over a period of two months

Machine learning-as-a-service for consumer electronics fault diagnosis: A comparison between matlab and azure ML

none10noToday, the improvement of the product value in consumer goods, such as new services to increase the positive customer experience, is the subject of many research activities. In a context where the product complexity becomes ever greater and the product life-cycle is always shorter, the use of intelligent tools for supporting all phases of the product life-cycle is very important. One of the aspects that is taking interest is to support the consumer in fault management. This analysis are well-known practices in the industrial, automotive fields, etc. but less used for consumer electronics. This paper analizes a Cloud service based on a Machine Learning (ML) approach used to provide fault detection capabilities to household appliances equipped with electric motors and compare the results with on premise ML algorithms provided research tools. The purpose of this paper is to perform a preliminary comparison of ML algorithm performances provided by two software, namely Microsoft Azure (cloud solution) and MATLAB (on premise solution), on a study case. In detail, the vibration data of an asynchronous motor installed in an oven extractor hood for commercial restaurant kitchen have been analyzed. To this end, two classification algorithms have been selected to implement fault diagnosis techniques.nonePrist M.; Longhi S.; Monteriu A.; Freddi A.; Pallotta E.; Ciabattoni L.; Cicconi P.; Giuggioloni F.; Caizer E.; Verdini C.Prist, M.; Longhi, S.; Monteriu, A.; Freddi, A.; Pallotta, E.; Ciabattoni, L.; Cicconi, P.; Giuggioloni, F.; Caizer, E.; Verdini, C