MLoC: A Cloud Framework adopting Machine Learning for Industrial Automation

Yu-Lun Huang, Wen Lin Sun, Kai Wei Yeh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


By leveraging the modern machine learning algorithms, we can build up more Artificial Intelligence (AI) systems, like self-driving cars, smart factories and financial analysis systems, to improve our daily life. In addition to building up an AI system, several prerequisites are required to drive the system, including data collection, data storage, machine learning models, training dataset, parameters tuning, and so on. To obtain the benefit of scalability and flexibility, most AI systems are built on a cloud platform, which shares resources with others in the same infrastructure. Though the above concept is trivial, the implementation faces big challenges when realizing it. In this paper, an easy-to-use cloud framework for machine learning as well as its implementation guideline is presented for building up a cloud-based development platform. We conduct several experiments on analyzing and monitoring the health condition of bearings of motors. We compare and analyze the feasibility of the proposed framework.

Original languageEnglish
Title of host publication2019 12th Asian Control Conference, ASCC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9784888983006
StatePublished - Jun 2019
Event12th Asian Control Conference, ASCC 2019 - Kitakyushu-shi, Japan
Duration: 9 Jun 201912 Jun 2019

Publication series

Name2019 12th Asian Control Conference, ASCC 2019


Conference12th Asian Control Conference, ASCC 2019

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