Real-time Single-device Mixed Reality for Industrial Fault Diagnosis: The SEMR Framework with Adaptive SLAM and Predictive Interaction

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, this paper proposes the SEMR framework, a single-device mixed reality (MR) solution that enhances industrial equipment monitoring and fault diagnosis. The framework integrates three innovative mechanisms: an environment-aware model that dynamically 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. The SEMR framework significantly improves diagnostic accuracy, interaction stability, and system adaptability under challenging conditions, offering a cost-effective solution for real-time, on-site industrial fault diagnosis.