Paper Info
Title
Streaming Analytics Processing In Manufacturing Performance Monitoring And Prediction
Abstract
Having the capability to process live streaming data is now the fundamental requisite for the successful realization of Industrial Internet of Things (IoT) and poses huge benefits in terms of increased operational efficiency, lesser costs and diminished risk to the industrial world. The advent of IoT and big data analytics technology offers further opportunities in manufacturing business models and asset management. For industrial manufacturing processes that are typically fast-paced and ridden with sophisticated set of conditions, such on-the-fly, real-time, fine-tuning adjustment suggestions of a predictive nature are challenging to describe. However, when provided properly, streaming analytics is greatly useful in the pursuit of improved industrial performance. We developed a streaming analytics system that used to evaluate stable manufacturing efficiency of multiple production lines simultaneously. This paper illustrates an use case from semiconductor manufacturing industry in Taiwan to present the data-driven applicability of streaming analytics system that enables companies to collect a large number of real-time, heterogeneous plant data with steps of text extraction, causal correlation, statistical modeling, as well as real-time monitoring and anomaly detection, to improve overall equipment effectiveness (OEE) of industrial manufacturing.
Year
DOI
Venue
2017
10.1109/BigData.2017.8258312
2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)
Keywords
DocType
ISSN
big data, streaming analytics, overall equipment effectiveness
Conference
2639-1589
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Yi-Hsin Wu100.34
Sheng-De Wang272068.13
Li-Jung Chen300.34
Cheng-Juei Yu400.34