Abstract
Rapid perception and processing of critical monitoring events are essential to ensure healthy operation of Internet of Manufacturing Things (IoMT)-based manufacturing processes. In this paper, we proposed a framework (active sensing and processing architecture (ASPIE)) for active sensing and processing of critical events in IoMT-based manufacturing based on the characteristics of IoMT architecture as well as its perception model. A relation model of complex events in manufacturing processes, together with related operators and unified XML-based semantic definitions, are developed to effectively process the complex event big data. A template based processing method for complex events is further introduced to conduct complex event matching using the Apriori frequent item mining algorithm. To evaluate the proposed models and methods, we developed a software platform based on ASPIE for a local chili sauce manufacturing company, which demonstrated the feasibility and effectiveness of the proposed methods for active perception and processing of complex events in IoMT-based manufacturing.
Highlights
With the interpenetration of information technology and manufacturing, the integration of manufacturing physical systems and information systems has been increasingly enhanced [1]
The event association matching process consists of three steps: (1) to establish an event association model using XML language to describe the association of multi-source manufacturing information, and to establish an XML file describing the event structure; (2) to mine association rules of events based on association data mining algorithms and to establish the correlation matching templates of complex events; and (3) to improve the performance of the algorithm in the event stream processing engine and perform the calculation of the complicated events in the production process based on template matching
To realize real-time active sensing of complex events by the Complex Event Processing (CEP) engine, we have developed a set of algorithms based on association templates to improve the speed of event detection and Sustainability 2018, 10, x FOR PEER REVIEW
Summary
With the interpenetration of information technology and manufacturing, the integration of manufacturing physical systems and information systems has been increasingly enhanced [1]. The decreasing cost of sensors and the need for intelligent manufacturing has led to the adoption of diverse types of sensors in modern factories for quality control, fault diagnosis, etc This has caused significant challenges in big data processing of such enormous amounts of heterogeneous information from such sensors: how to detect different types of events; how to match events to appropriate decision-making rules; and how to handle the real-time requirement of complex events processing. The framework and methods for handling the perception and processing of key events in manufacturing processes may provide theoretical and practical guidance for developing enterprise production management decision-making systems. Figure 2F.igTuhree a2c. tiTvheeseacntsivinegsmenosidnegl omfoIdoeMl TofeIvoeMnTts.eIvteinstsc.omIt pisosceodmopfosdeadtaocfodllaetcaticoonll,edctaiotan,trdaantsamission, and dattarapnrsomciesssisoinn,ganudnditast.a processing units
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