Development of Industry 4.0 predictive maintenance architecture for broadcasting chain. (August 2021) - Sahba, Rezvaneh; Radfar, Reza; Rajabzadeh Ghatari, Ali; Pour Ebrahimi, Alireza
Abstract: Industry 4.0 Predictive Maintenance (PdM 4.0) architecture in the broadcasting chain is one of the taxonomy challenges for deploying Industry 4.0 frameworks. This paper proposes a novel PdM framework based on advanced Reference Architecture Model Industry 4.0 (RAMI 4.0) to reduce operation and maintenance costs. This framework includes real-time production monitoring, business processes, and integration based on Design Science Research (DSR) to generate an innovative Business Process Model and Notation (BPMN) meta -model. The addressed model visualizes sub-processes based on experts' and stakeholders' knowledge to reduce the cost of maintenance of audiovisual services including satellite TV, cable TV, and live audio and video broadcast services. Based on the recommendation and the concept of Industry 4.0, the proposed framework tolerates the predictable failures and further concerns in similar related industries. Some empirical experiments have been conducted by using the Islamic Republic of Iran Broadcasting's (IRIB) high-power station (located near the capital city of Iran, Tehran) to evaluate the functionality and efficiency of the proposed predictive maintenance framework. Practical outcomes demonstrate that interval times between data collection should be increased in audio and video broadcasting predictive maintenance because of the limitation of the internal processing performance of equipment. The framework also indicates the role of the Frequency Modulation (FM) transmitters' data clearance to reduce the instability and untrustworthy data during data mining. The proposed DSR method endorses using a customized RAMI 4.0 meta -model framework to adapt distributed broadcasting and communication with PdM 4.0, which increases the stability as well as decreasing maintenance costs of the broadcasting chain in comparison to state-of-the-art methodologies. Furthermore, it is shown that the proposed framework outperforms the best-evaluated methods in terms of acceptance.