Machine Health Management in Smart Factory: A Review

Author(s): Gil-Yong Lee, Mincheol Kim, Ying-Jun Quan, Min-Sik Kim, Joon Young Thomas Kim, Hae-Sung Yoon, Sangkee Min, Dong-Hyeon Kim, Jeong-Wook Mun, Jin Woo Oh, In Gyu Choi, Chung-Soo Kim, Won-Shik Chu, Jinkyu Yang, Binayak Bhandari, Choon-Man Lee, Jeong-Beom Ihn, and Sung Hoon Ahn


Publication: Journal of Mechanical Science and Technology


Citation: Journal of Mechanical Science and Technology, Vol. 32, No.3, pp. 987-1009, 2018.

In this paper, we present a review of machine health managements for the smart factory. As the Industry 4.0 leads current factory automation and intelligent machines, the machine health management for diagnostic and prognostic purposes are essential, and their importance is getting more significant for the realization of the smart factory in the Industry 4.0. After brief introductions to important concepts and definitions composing smart factory and Industry 4.0, the developments in maintenance strategies towards Prognostics and health management (PHM) of machines are summarized. The review of machine health managements is followed, classifying the references by the monitoring components, types of measurements, as well as PHM tools and algorithms. 94 existing articles are reviewed and summarized in this regard. The implementations of machine health managements within the smart factory are discussed in terms of data connectivity, communications, Cyber-physical system (CPS) and virtual factory, relating them to Internet of things (IoT), cloud computing, and big data management.