A Remote Maintenance Support Method for Complex Equipment Based on Layered-MVC-B/S Integrated AR Framework

Augmented reality (AR)-based assisted maintenance methods are effective in completing simple equipment maintenance tasks. However, complex equipment typically requires multi-location remote collaboration due to structural complexity, multiple fault states, and high maintenance costs, significantly increasing maintenance difficulty. This paper therefore proposes a remote maintenance support method for complex equipment based on layered-MVC-B/S integrated AR framework (IAR-RMS). First, clearly define the maintenance content and workflow for multi-person remote collaboration and conduct an in-depth analysis of process control within the task workflow to avoid incomplete or unsystematic maintenance guidance information and processes. Second, analyze collaborative management from the perspectives of maintenance role conflicts and maintenance operation conflicts and implement on-demand permission control and operation sequence management to ensure the timeliness and user-friendliness of multi-person collaboration. Then, integrate the layered architecture, MVC, and B/S architecture to construct a remote maintenance support (RMS) model based on an integrated architecture system, ensuring the reliability and timeliness of the model. Finally, demonstrate the main functional modules of the RMS task process, and use power system disassembly and assembly as an experiment to validate the effectiveness and generalizability of the proposed IAR-RMS method. The results indicate that the proposed IAR-RMS method can effectively realize maintenance support tasks in multi-person remote collaboration scenarios.

Authors

Institutions

Publication Details

Journal
Sensors
Published
2025-09-23
DOI
https://doi.org/10.3390/s25195935
Citations
1
Primary Topic
Augmented Reality Applications
Type
article
Field-Weighted Citation Impact
1.28
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Remote Maintenance Support Method for Complex Equipment Based on Layered-MVC-B/S Integrated AR Framework

Xuhang Wang, Jiayu Chen, Dong Zhou, Qinhua Lu
1 citations
Sensors
Augmented Reality Applications
1.28
article

A Remote Maintenance Support Method for Complex Equipment Based on Layered-MVC-B/S Integrated AR Framework

Xuhang Wang, Jiayu Chen, Dong Zhou, Qinhua Lu
article en
1 citations

Abstract

Augmented reality (AR)-based assisted maintenance methods are effective in completing simple equipment maintenance tasks. However, complex equipment typically requires multi-location remote collaboration due to structural complexity, multiple fault states, and high maintenance costs, significantly increasing maintenance difficulty. This paper therefore proposes a remote maintenance support method for complex equipment based on layered-MVC-B/S integrated AR framework (IAR-RMS). First, clearly define the maintenance content and workflow for multi-person remote collaboration and conduct an in-depth analysis of process control within the task workflow to avoid incomplete or unsystematic maintenance guidance information and processes. Second, analyze collaborative management from the perspectives of maintenance role conflicts and maintenance operation conflicts and implement on-demand permission control and operation sequence management to ensure the timeliness and user-friendliness of multi-person collaboration. Then, integrate the layered architecture, MVC, and B/S architecture to construct a remote maintenance support (RMS) model based on an integrated architecture system, ensuring the reliability and timeliness of the model. Finally, demonstrate the main functional modules of the RMS task process, and use power system disassembly and assembly as an experiment to validate the effectiveness and generalizability of the proposed IAR-RMS method. The results indicate that the proposed IAR-RMS method can effectively realize maintenance support tasks in multi-person remote collaboration scenarios.

SensorsVol. 25(19)
Beihang University (CN), Nanjing University of Aeronautics and Astronautics (CN)
Openalex Percentile: Top 16%
Augmented Reality Applications
1.28
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.