An Evaluative Study on IoT Ecosystem for Smart Predictive Maintenance (IoT-SPM) in Manufacturing: Multiview Requirements and Data Quality

Abstract

With the recent advances of the Internet of Things (IoT), innovative techniques and concepts have emerged, such as digital twins and industrial 4.0. As one of the essential parts of a digital twin, IoT-based smart predictive maintenance (IoT-SPM) is a key enabling technology for smart manufacturing. This paper introduces digital twins and their relationship to IoT-SPM and proposes a reference IoT-SPM, aiming to provide a comprehensive and systematic outlook for IoT-SPM field. Thus, it can be used as a guide map for interested readers. To give a complete picture of the IoT-SPM ecosystem in industrial 4.0 systems, this paper conducts an analysis from multi-view perspectives, starting with the architecture, followed by platforms and component. The key components or requirements of an IoT-SPM ecosystem are identified and outlined, including the IoT and cyber-physical system (CPS) as the cornerstone technologies, IoT monitoring data as the base, big data platforms as the backbone, an upgraded computing paradigm as the catalyst, and machine learning-based data analysis as the main processor. This paper also focuses on the issues surrounding IoT data when applying analytic models to a real-world industrial IoT system. Then, the current progresses relating IoT and IoT-SPM are depicted, and a research gap on IoT data quality is identified. In particular, regarding the identified IoT data quality problems, this paper qualitatively evaluates and discusses the existing solutions. These discussions lead to several open research issues and future directions.