Probing an intelligent predictive maintenance approach with deep learning and augmented reality for machine tools in IoT-enabled manufacturing

Liu, Changchun; Zhu, Haihua; Tang, Dunbing; Nie, Qingwei; Zhou, Tong; Wang, Liping; Song, Yong

doi:10.1016/j.rcim.2022.102357

Cited by 62 publications

(17 citation statements)

References 42 publications

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“…Heterogeneous sensors integrated with AR visualization can improve operator safety in complex and dangerous plants. Liu et al [32] proposed a multi-service collaborative machine intelligence predictive maintenance approach for fault prediction that combines CNN and LSTM. MR guides the visualization through the maintenance process, integrating massive amounts of data into the machine.…”

Section: Mixed Reality For Smart Aerospace Engineeringmentioning

confidence: 99%

Applications of Mixed Reality for Smart Aviation Industry: Opportunities and Challenges

Tran¹,

Jiang²,

Williams³

2023

Modern Development and Challenges in Virtual Reality

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Nowadays, mixed reality has improved operational efficiency and enhanced passenger experience in the aviation industry. Integrated with advanced machine learning and artificial intelligence techniques, mixed reality can easily deal with tons of aviation data to support decision-making processes in this industry. The chapter presents the state-of-the-art applications of mixed reality in smart aviation industry. Opportunities and challenges of integrating mixed reality with advanced machine learning and artificial intelligence techniques into the aviation industry are introduced. This chapter focuses on how the integrated mixed reality can improve the quality and reliability of maintenance, operation, piloting, training, and product design in smart aerospace engineering. It also describes autonomous, self-service, and data visualization systems in smart airports to enhance passenger experience. Finally, this chapter discusses airline’s digital-based responses to the COVID-19 crisis.

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Section: Mixed Reality For Smart Aerospace Engineeringmentioning

confidence: 99%

Applications of Mixed Reality for Smart Aviation Industry: Opportunities and Challenges

Tran¹,

Jiang²,

Williams³

2023

Modern Development and Challenges in Virtual Reality

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“…Also, security is one of the main concerns often precluding the adoption of digital technologies beyond factory walls. In this context, the capability to develop, integrate and offer software also acquires a bigger dimension, as the new services would be based on software applications [73].…”

Section: Risks Associated With Digital Servitization In the Machine T...mentioning

confidence: 99%

Analyzing the risks of digital servitization in the machine tool industry

Chávez

Unamuno

Johansson

et al. 2023

Robotics and Computer-Integrated Manufacturing

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“… Zinn et al (2021) presented a MARL system based on DQN and actor–critic to learn the distributed fault-tolerant control policies for automated production systems during fault recovery to increase availability. Liu et al (2022) proposed a multi-agent DQN approach to make maintenance scheduling decisions for personnel and also production control during maintenance. This work incorporates a CNN-LSTM-based architecture for the DQN, while the impacts of agents are neglected.…”

Section: Applicationsmentioning

confidence: 99%

A review of the applications of multi-agent reinforcement learning in smart factories

Bahrpeyma

Reichelt

2022

Front. Robot. AI

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The smart factory is at the heart of Industry 4.0 and is the new paradigm for establishing advanced manufacturing systems and realizing modern manufacturing objectives such as mass customization, automation, efficiency, and self-organization all at once. Such manufacturing systems, however, are characterized by dynamic and complex environments where a large number of decisions should be made for smart components such as production machines and the material handling system in a real-time and optimal manner. AI offers key intelligent control approaches in order to realize efficiency, agility, and automation all at once. One of the most challenging problems faced in this regard is uncertainty, meaning that due to the dynamic nature of the smart manufacturing environments, sudden seen or unseen events occur that should be handled in real-time. Due to the complexity and high-dimensionality of smart factories, it is not possible to predict all the possible events or prepare appropriate scenarios to respond. Reinforcement learning is an AI technique that provides the intelligent control processes needed to deal with such uncertainties. Due to the distributed nature of smart factories and the presence of multiple decision-making components, multi-agent reinforcement learning (MARL) should be incorporated instead of single-agent reinforcement learning (SARL), which, due to the complexities involved in the development process, has attracted less attention. In this research, we will review the literature on the applications of MARL to tasks within a smart factory and then demonstrate a mapping connecting smart factory attributes to the equivalent MARL features, based on which we suggest MARL to be one of the most effective approaches for implementing the control mechanism for smart factories.

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Probing an intelligent predictive maintenance approach with deep learning and augmented reality for machine tools in IoT-enabled manufacturing

Cited by 62 publications

References 42 publications

Applications of Mixed Reality for Smart Aviation Industry: Opportunities and Challenges

Applications of Mixed Reality for Smart Aviation Industry: Opportunities and Challenges

Analyzing the risks of digital servitization in the machine tool industry

A review of the applications of multi-agent reinforcement learning in smart factories

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