A Single-Device Environment-Adaptive Mixed Reality Framework for Real-Time Industrial Fault Diagnosis

Xueyi Li; Bo Kang; Jing Tang; Qi Li; Tianyang Wang; Minwei Zhang

doi:10.37965/jdmd.2026.1107

Authors

Xueyi Li College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
Bo Kang College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
Jing Tang College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
Qi Li Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China https://orcid.org/0000-0001-7105-2818
Tianyang Wang Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
Minwei Zhang Beijing Zhongyuan Ruixun Technology Co., Ltd., Beijing 100085, China

DOI:

https://doi.org/10.37965/jdmd.2026.1107

Keywords:

adaptive SLAM; fault diagnosis; industrial inspection; predictive interaction; real-time equipment status monitoring; single-device mixed reality

Abstract

In industrial environments, monitoring and fault diagnosis of mechanical equipment face challenges such as spatial localization drift and delays in real-time data rendering, especially in complex settings with low illumination, weak textures, and strong interference. Traditional methods struggle to effectively integrate monitoring data with physical entities, increasing cognitive load and reducing diagnostic accuracy. To address these issues, we propose the Single-Device Mixed Reality (SEMR) framework, a novel solution that enhances industrial equipment monitoring and fault diagnosis. The framework integrates three key mechanisms: an environment-aware model that adjusts the confidence of Simultaneous Localization and Mapping (SLAM) to ensure precise spatial registration, a Kalman filter-based motion prediction to reduce rendering delays, and a fault-tolerant gaze interaction system for hands-free operation. Experimental results demonstrate that SEMR reduces the spatial registration error by 52.1%, from 14.2 cm to 6.8 cm, and decreases latency during dynamic inspections by 26.7%, improving diagnostic accuracy and real-time performance. The proposed method provides a cost-effective and reliable solution for enhancing industrial fault diagnosis and equipment monitoring, particularly in challenging environments.

Conflict of Interest Statement

The authors declare no conflicts of interest.