Predictive Maintenance in Industry 4.0

Abstract

In the context of Industry 4.0, the manufacturing related processes have shifted from conventional processes within one organization to collaborative processes cross different organizations, for example, product design processes, manufacturing processes, and maintenance processes across different factories and enterprises. The development and application of Internet of things, i.e. smart devices and sensors increases availability and collection of diverse data. With new technologies, such as advanced data analytics and cloud computing provide new opportunities for flexible collaborations as well as effective optimizing manufacturing related processes, e.g. predictive maintenance. Predictive maintenance provides a detailed examination of the detection, location and diagnosis of faults in related machineries using various analyses. RAMI4.0 is a framework for thinking about the various efforts that constitute Industry 4.0. It spans the entire product life cycle & value stream axis, hierarchical structure axis and functional classification axis. The Industrial Data Space (now International Data Space) is a virtual data space using standards and common governance models to facilitate the secure exchange and easy linkage of data in business ecosystems. It thereby provides a basis for creating and using smart services and innovative business processes, while at the same time ensuring digital sovereignty of data owners. This paper looks at how to support predictive maintenance in the context of Industry 4.0? Especially, applying RAMI4.0 architecture supports the predictive maintenance using FIWARE framework, which leads to deal with data exchanging among different organizations with different security requirements as well as modularizing of related functions.

Market performance and corporate governance in banking sector Indonesia stock exchange

“…Stock return is a factor that affects investors' interest in investing in a company with a high rate of return given by the company to investors, indicating that the company has an excellent corporate performance. Hence, investors believe that the company will positively affect shares invested by investors in the capital market (Pratomo, 2001;Hadianto & Wijaya, 2010;Yamato et al, 2017;Sang et al, 2020). The company's performance is good or bad; it can be seen from the financial ratio of Earning per Share (EPS) and Price-earnings Ratio.…”

A Review of Attacks, Vulnerabilities, and Defenses in Industry 4.0 with New Challenges on Data Sovereignty Ahead

“…In Ref. [ 21 ], the authors also focus their research work around ids described as a virtual data space that uses common standards and governance models to facilitate the secure exchange and easy linkage of data across business ecosystems. It provides a foundation for the creation and use of intelligent services and innovative business processes, while ensuring the digital sovereignty of data owners.…”

A Predictive Maintenance Model for Flexible Manufacturing in the Context of Industry 4.0

“…The physical manufacturing machine equipment and systems of the manufacturing factory are expensive (Becker and Wagner 2016), and the state such as the condition, health, and operation of those machines can have a huge impact on the manufacturing chain. Any failure of the machine equipment tools can easily lead to undesired costs and disruptions such as delay and dispute in the value-added processes of the enterprises, partners, etc., due to the collaborative nature of production systems (Weber et al, 2016;Sang et al, 2020;Deloitte 2021). Essentially, any unexpected failure or inefficient process of manufacturing machine equipment may result in unintended downtimes and costs for an entire production line (Mobley 2002;Sang et al, 2020a;Deloitte 2021).…”

“…However, the demands of advanced prediction make it impossible for the traditional data-driven methods to handle the data complexity and growth (Mourtzis et al, 2016;Sang et al, 2020). Deep learning-based models have recently received great attraction as they offer several benefits such as better performance of RUL prognostics, i.e., high prognostics accuracy and automatic feature extraction (Wang et al, 2016b).…”

“…Modern collaborative industry is moving toward applying the Industry 4.0 concept for achieving effective smart solutions (Zezulka et al, 2016;Thoben et al, 2017;Sang et al, 2020). Industry 4.0 supports the flexibility required for collaborative networks by the application of advanced technologies.…”