GNSS-Based Monitoring Methods for Mining Headframes

This study introduces an innovative GNSS-based monitoring system designed to evaluate deformation in mining headframes, effectively addressing the limitations of traditional methods, such as inadequate real-time capabilities and complex data processing requirements. The research was conducted at the Liuzhuang Mine in Anhui Province, China, where a monitoring network was established, consisting of one reference station and eight GNSS stations strategically positioned on sheave platforms and structural supports. Over a period of 66 days, high-frequency 3D deformation data were collected and processed using advanced methodologies, including cubic spline interpolation, generalized extreme studentized deviate (GESD) outlier removal, and Gaussian filtering. Spatiotemporal analysis, employing the “base state with amendments” model, indicated that 90% of the deformations (ΔX, ΔY, ΔH) were confined within ±8 mm, with more significant fluctuations observed near the sheave wheels due to mechanical stress. Correlation analysis identified the distance to the sheave wheel as the primary factor influencing horizontal deformation, with Pearson correlation coefficients exceeding 0.67, while vertical settlement remained stable. Risk thresholds, derived from statistical fluctuations, demonstrated that 99.2% of the data fell within safe limits during validation. In comparison to traditional approaches, the GNSS system delivers enhanced precision, real-time functionality, and a decreased field workload. This study presents a scalable framework for assessing headframe safety and guides the optimization of sensor placement in analogous mining settings. It is proposed that future integration with multi-source sensors, such as inertial navigation systems, will further augment monitoring robustness.