Intelligent predictive maintenance control using augmented reality

Kostolani, Michal; Muŕın, Just́ın; Kozák, Štefan

doi:10.1109/pc.2019.8815042

Cited by 22 publications

(6 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [105][106][107][108], the technologies of AR, VR, MR, digital intelligent assistant, digital twin, and IoT sensors have been leveraged. The IoT sensor has enabled real-time data collection from various sensors attached to equipment in PdM.…”

Section: State-of-the-art Techniques For Predictive Maintenancementioning

confidence: 99%

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

Ucar,

Karakose,

Kırımça

2024

Applied Sciences

View full text Add to dashboard Cite

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.

show abstract

Section: State-of-the-art Techniques For Predictive Maintenancementioning

confidence: 99%

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

Ucar,

Karakose,

Kırımça

2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Moreover, efforts to change complex 2D circuit diagrams of electrical and mechanical devices into 3D forms using AR techniques have been insufficient. In the railway vehicle field, AR has been limited applied to predictive maintenance and development of a framework that supports a maintenance process [18], [19]. The pneumatic flows of the brake system for railway vehicle are complex and invisible to the naked eye.…”

Section: B Research Backgroundmentioning

confidence: 99%

Development and Evaluation of Augmented Reality Learning Content for Pneumatic Flow: Case Study on Brake Operating Unit of Railway Vehicle

Kwon

Kim

2023

IEEE Access

View full text Add to dashboard Cite

The Brake operating unit (BOU) of a railway vehicle is one of the important systems for controlling the braking of the train. Because this system uses compressed air, it is difficult to understand and train the system. The existing education method involves learning the pneumatic flow of various control air in a 2D pneumatic circuit diagram based on a maintenance manual. However, in the actual braking system, it was difficult to learn effectively because the air flows in 3D. In order to solve these problems, the improvement of the training technique using the new 3D augmented reality (AR) was performed. In this study, to increase the learning effect of air brake flow, a technique for simultaneously displaying the pneumatic flow in 2D circuit diagram and 3D model was proposed. First, the distance ratio for simultaneous display can be determined using the proposed streamline matching variable calculation algorithm (SLMVC) that uses position and animation duration as input variables. Second, to avoid the complexity of using the 24 variables of the Particle System module in Unity, an existing universal 3D platform, a continuously emission property correction algorithm (CEPC) that can output particle objects as a streamline using only 4 properties (e.g., start lifetime, start speed, emission rate over time, start delay). As a result, the following 6 different types of BOU air pressure could be simultaneously displayed in 2D and 3D (e.g., AC, BC, SR, SBR, AS1, AS2). Therefore, maintenance staff can effectively learn complex pneumatic flow. To verify the usability of the developed content, a survey using the NASA-TLX technique was conducted targeting 60 maintenance staff. As a result of the comparison between Group A using the existing maintenance manual and Group B using the developed AR content, the perceived workload decreased by 28%. In particular, the frustration part decreased by 64% and the performance part decreased by 62%, indicating that the usability of AR content was very good.INDEX TERMS Railway vehicle, augmented reality, brake operating unit, pnematic flow, particle effect.The associate editor coordinating the review of this manuscript and approving it for publication was Jesus Felez .

show abstract

“…Mateusz Marzec et al [101] investigated various machine-learning techniques and proposed a procedure to automatize the intelligent predictive maintenance process. There are many research studies regarding the design and implementation of IPdM approaches in the literature and the majority of them characterized the models based on the following technologies [100,[102][103][104][105][106][107][108]:…”

Section: Intelligent Predictive Maintenance (Ipdm)mentioning

confidence: 99%

Intelligent Predictive Maintenance (IPdM) in Forestry: A Review of Challenges and Opportunities

Maktoubian

Taskhiri

Turner

2021

Forests

View full text Add to dashboard Cite

The feasibility of reliably generating bioenergy from forest biomass waste is intimately linked to supply chain and production processing costs. These costs are, at least in part, directly related to assumptions about the reliability and cost-efficiency of the machinery used along the forestry bioenergy supply chain. Although mechanization in forestry operations has advanced in the last 20 years, it is evident that challenges remain in relation to production capability, standardization of wood quality, and supply guarantee from forestry resources because of the age and reliability of the machinery. An important component in sustainable bioenergy from biomass supply chains will be confidence in consistent production costs linked to guarantees about harvest and haulage machinery reliability. In this context, this paper examines the issue of machinery maintenance and advances in machine learning and big data analysis that are contributing to improved intelligent prediction that is aiding supply chain reliability in bioenergy from woody biomass. The concept of “Industry 4.0” refers to the integration of numerous technologies and business processes that are transforming many aspects of conventional industries. In the realm of machinery maintenance, the dramatic increase in the capacity to dynamically collect, collate, and analyze data inputs including maintenance archive data, sensor-based monitoring, and external environmental and contextual variables. Big data analytics offers the potential to enhance the identification and prediction of maintenance (PdM) requirements. Given that estimates of costs associated with machinery maintenance vary between 20% and 60% of the overall costs, the need to find ways to better mitigate these costs is important. While PdM has been shown to help, it is noticeable that to-date there has been limited assessment of the impacts of external factors such as weather condition, operator experiences and/or operator fatigue on maintenance costs, and in turn the accuracy of maintenance predictions. While some researchers argue these data are captured by sensors on machinery components, this remains to be proven and efforts to enhance weighted calibrations for these external factors may further contribute to improving the prediction accuracy of remaining useful life (RUL) of machinery. This paper reviews and analyzes underlying assumptions embedded in different types of data used in maintenance regimes and assesses their quality and their current utility for predictive maintenance in forestry. The paper also describes an approach to building ‘intelligent’ predictive maintenance for forestry by incorporating external variables data into the computational maintenance model. Based on these insights, the paper presents a model for an intelligent predictive maintenance system (IPdM) for forestry and a method for its implementation and evaluation in the field.

show abstract

Intelligent predictive maintenance control using augmented reality

Cited by 22 publications

References 4 publications

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

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

Development and Evaluation of Augmented Reality Learning Content for Pneumatic Flow: Case Study on Brake Operating Unit of Railway Vehicle

Intelligent Predictive Maintenance (IPdM) in Forestry: A Review of Challenges and Opportunities

Contact Info

Product

Resources

About