Data analytics-enable production visibility for Cyber-Physical Production Systems

Abstract

With the wide integration of the Cyber-Physical System (CPS) and Internet of things (IoT), the manufacturing industry has entered into an era of big data. Thus, manufacturing companies are facing challenges when conducting Big Data Analytics, including the high velocity of data generation, the enormous volume, the multifarious formats and types as well as the quality or fidelity. In this paper, a Cyber-Physical Production System (CPPS) using data analytics is proposed to enable production visibility. Firstly, this study uses data stream processing approaches to clean redundant data efficiently. Secondly, a Bayesian inference engine, which is trained by ming the historical data offline, is employed to identify the accuracy of an RFID-captured event online. Then, complex event processing is applied to fuse multi-source heterogeneous data. Finally, production progress visibility is achieved by the Business Process Management. The proposed system demonstrates that it is significant to implement real-time data collection, processing and visibility, as well as to improve production efficiency. A demonstrative case from the machinery industry is presented to validate the CPPS.