Data-Driven Maintenance: Combining Predictive Maintenance and Mixed Reality-supported Remote Assistance

Wolfartsberger, Josef; Zenisek, Jan; Wild, Norbert

doi:10.1016/j.promfg.2020.04.022

Cited by 17 publications

(7 citation statements)

References 8 publications

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“…By leveraging large and complex data sets, companies can gain valuable insights into their operations, identify trends and patterns, and make informed decisions that improve efficiency and increase customer satisfaction (Chen et al, 2012). However, companies need to be aware of the challenges and limitations of big data analytics and take a structured approach to leverage these technologies to achieve their goals (Wolfartsberger et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Future Maintenance and Service Innovation Using Industrial Big Data Analytics in The United States

2023

CEIS

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The data that matters to executives in the United States industry includes machine performance data, maintenance and service data, and production data. This data improves machine performance, reduces downtime, and increases efficiency. The individuals who need this data include executives, maintenance and service personnel, and production managers. The methods of ensuring that the critical data reaches the users have industrial big data analytics, implementing a robust data management system, and training personnel.The use of big data analytics in various industries has been growing rapidly over the past few years. The industrial sector has seen significant benefits from implementing big data analytics. This research paper explores the potential of future maintenance and service innovation in the United States industrial sector using big data analytics, the benefits of being data-driven, and implementing a data-driven process strategy in the United States industrial sector. The paper explains the key data sources, storage, and processing techniques currently used in the industry to gather and analyze data. The paper also identifies the challenges and methodologies in leveraging big data analytics to drive maintenance and service operations innovation. The study will focus on identifying the data that matters most to executives in the industry, determining who needs it, and exploring methods for ensuring the critical data is effectively communicated to its intended users. Additionally, the paper will examine recent advances and terminologies in big data analytics, the methodology for designing innovation-based industries, presents the theoretical background and hypotheses, and examine limitations and future research opportunities in the field.

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Section: Discussionmentioning

confidence: 99%

Future Maintenance and Service Innovation Using Industrial Big Data Analytics in The United States

2023

CEIS

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“…The lack of realworld data makes it difficult to test the system under realistic conditions and to evaluate its performance and accuracy. Researchers have proposed various methods for simulating real-world scenarios to overcome this challenge, such as using virtual environments and testbeds [26,35,37,42,96,109,210,[225][226][227][228][229][230][231].…”

Section: Challenges and Limitations Of Using Ai For Pdm Autonomymentioning

confidence: 99%

Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends

Ucar,

Karakose,

Kırımça

2024

Applied Sciences

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Predictive maintenance (PdM) is a policy applying data and analytics to predict when one of the components in a real system has been destroyed, and some anomalies appear so that maintenance can be performed before a breakdown takes place. Using cutting-edge technologies like data analytics and artificial intelligence (AI) enhances the performance and accuracy of predictive maintenance systems and increases their autonomy and adaptability in complex and dynamic working environments. This paper reviews the recent developments in AI-based PdM, focusing on key components, trustworthiness, and future trends. The state-of-the-art (SOTA) techniques, challenges, and opportunities associated with AI-based PdM are first analyzed. The integration of AI technologies into PdM in real-world applications, the human–robot interaction, the ethical issues emerging from using AI, and the testing and validation abilities of the developed policies are later discussed. This study exhibits the potential working areas for future research, such as digital twin, metaverse, generative AI, collaborative robots (cobots), blockchain technology, trustworthy AI, and Industrial Internet of Things (IIoT), utilizing a comprehensive survey of the current SOTA techniques, opportunities, and challenges allied with AI-based PdM.

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“…Using the results of this analysis, smart glasses are evenly applied in the field of healthcare, and are stably assisting users; however, it is difficult to employ smart glasses in a direct evaluation, and, therefore, technological development is required. The industry field includes technical research on maintenance [47,48], safety [11,49], and work support [50,51] (Table 3). Among them, in the safety field, the following studies have been conducted: Beak et al [11] developed a smart glass-based wearable personal proximity warning system (PWS) for the safety of pedestrians at construction and mining sites.…”

Section: Smart Glasses Research Trendmentioning

confidence: 99%

“…MaintenanceWolfartsberger et al[47] 2020 Development of technology that can support maintenance work on the job site Siltanen and Heinonen[48] 2020Safety Chang et al [49] 2018 Design and implementation of drowsiness fatigue monitoring system to improve road safety Baek and Choi [11] 2020 Development of smart glass-based proximity warning system for pedestrians at a mining site Work support Kirks et al [50] 2019 Development of distributed control system based on smart glasses Gensterblum [51] 2020 Examine the possibility that commercial drones and smart glasses are disruptive technologies in the construction industry.…”

mentioning

confidence: 99%

Applications of Smart Glasses in Applied Sciences: A Systematic Review

Kim

Choi

2021

Applied Sciences

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The aim of this study is to review academic papers on the applications of smart glasses. Among 82 surveyed papers, 57 were selected through filtering. The papers were published from January 2014 to October 2020. Four research questions were set up using the systematic review method, and conclusions were drawn focusing on the research trends by year and application fields; product and operating system; sensors depending on the application purpose; and data visualization, processing, and transfer methods. It was found that the most popular commercial smart glass products are Android-based Google products. In addition, smart glasses are most often used in the healthcare field, particularly for clinical and surgical assistance or for assisting mentally or physically disabled persons. For visual data transfer, 90% of the studies conducted used a camera sensor. Smart glasses have mainly been used to visualize data based on augmented reality, in contrast with the use of mixed reality. The results of this review indicate that research related to smart glasses is steadily increasing, and technological research into the development of smart glasses is being actively conducted.

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Data-Driven Maintenance: Combining Predictive Maintenance and Mixed Reality-supported Remote Assistance

Cited by 17 publications

References 8 publications

Future Maintenance and Service Innovation Using Industrial Big Data Analytics in The United States

Future Maintenance and Service Innovation Using Industrial Big Data Analytics in The United States

Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends

Applications of Smart Glasses in Applied Sciences: A Systematic Review

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