Title: Development of fuzzy inference system in predictive maintenance architecture for broadcasting chain
Authors: Rezvaneh Sahba; Reza Radfar; Ali Rajabzadeh Ghatari; Alireza Pour Ebrahimi
Addresses: Department of Information Technology Management, Islamic Azad University, Science and Research Branch, Tehran, Iran ' Department of Industrial Management, Islamic Azad University, Science and Research Branch, Tehran, Iran ' Department of Management and Economics, Tarbiat Modares University, Tehran, Iran ' Islamic Azad University, Karaj, Iran
Abstract: This paper offers an innovative predictive maintenance (PdM) framework based on advanced reference architecture model Industry 4.0 (RAMI 4.0) to decrease maintenance costs and time based on design science research (DSR) in real-time monitoring cases. The offered framework endures the predictable failures in similar manufacturing by utilising a combination of the fuzzy inference system (FIS) method and analytic hierarchy process (AHP) based on root cause experts' knowledge. Most very high critical alerts are related to communication link stability. Practical investigations have been developed in the Islamic Republic of Iran Broadcasting's (IRIB) high-power station to appraise the functionality and compatibility of the proposed PdM framework. Practical outcomes emphasise the importance of asset layer and communication link in PdM management.
Keywords: predictive maintenance; Industry 4.0; design science research; broadcasting; fuzzy inference system.
International Journal of Advanced Operations Management, 2021 Vol.13 No.3, pp.331 - 346
Received: 18 Nov 2020
Accepted: 24 May 2021
Published online: 24 Jan 2022 *