A Systematic Literature Review of Virtual and Augmented Reality Applications for Maintenance in Manufacturing

Buettner, Ricardo; Breitenbach, Johannes; Wannenwetsch, Kai; Ostermann, Isabel; Priel, Rene

doi:10.1109/compsac54236.2022.00099

Cited by 4 publications

(1 citation statement)

References 55 publications

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“…The number of assets and volume of data we are dealing with creates substantial hurdles from a data processing, networking, and human cognition perspective, all of which are addressed in this paper. Furthermore, VR-based systems can reduce the time to learn tasks, and aid in training, and are already being utilized to aid in maintenance for manufacturing environments (Buettner et. al.…”

Section: Reasoning/comparison To Other Toolsmentioning

confidence: 99%

Data Analytics and Visualization Application for Asset Health Monitoring

Carley¹,

Fuller²,

Bond

et al. 2022

PHM_CONF

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Much of the research on predictive maintenance has focused on statistical and machine learning techniques, while there has been significantly less focus on the human computer interaction or visualization aspects of PHM. Human computer interaction and visualization techniques can quickly help identify interesting data sub-domains, both time periods and sensors, provided the data can be queried, retrieved and displayed in a timely manner. Augmenting visualization and interaction with a visual, aggregative fleet-based query system adds a further dimension, highlighting the ability of the fleet to carry out its mission. In the age of big data, effectively visualizing data from an asset with a multitude of sensors can be difficult, and that difficulty is compounded as you add additional assets. In this paper, we propose a scalable framework that is capable of visualizing past, current, and prognosticated health from the individual sensor up to the fleet or group level. In addition to viewing near real time sensor data, maintenance logs, fault information, and data aggregations will be merged with the sensor data to make the analysis and visualizations more valuable. This framework is scalable in regard to how much data can be collected, stored, and processed, as well as to the different organizational levels within a fleet of assets. The framework is built as a web-application primarily using the following visualizations: a collapsible tree graph for asset information; 2D charts for temporal sensor data, fault data, and maintenance data; and 3D digital twins of critical components. The current dataset used to build and demonstrate the capability of the web-application has data from over 3000 vehicles and contains approximately 9TB of data. Vehicle information such as model, make, sub-component, and fleet organization are presented in a configurable, collapsible tree structure. This allows the user to visualize the fleet and to select the asset and sensor combinations needed to display the temporal sensor data that will answer the question at hand. Information regarding each vehicle’s health status is displayed then aggregated and displayed for each of the higher tree nodes. A 3D digital twin is also available to highlight sensor locations and current health status of the component. These component models can be viewed and manipulated with and without a virtual reality headset. As health status monitoring for asset subcomponents are developed, they can be added to the system, allowing for complete health status reporting.

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Section: Reasoning/comparison To Other Toolsmentioning

confidence: 99%