Robust Fault Diagnosis of Mine Hoisting Rigid Guides Under Variable Operating Conditions Using Physics-Informed Features and Zero-Space Observers

In vertical mine hoisting systems, the rigid guide serves as a critical safety component whose failure may induce severe dynamic disturbances and potentially trigger cascading safety incidents. Existing data-driven diagnosis methods for rigid guides often lack robustness under variable operating conditions and require substantial labeled data. Yet in practical mine hoisting operations, variations in hoisting speed and lifting mass are inevitable, and acquiring sufficient fault samples is challenging due to safety constraints. To address these problems, this paper proposes a novel fault diagnosis framework that integrates a physics-informed feature-extraction pipeline with the zero-space observer theory. Vibration signals are processed to extract dimensionless and relative features, which are deliberately designed based on the dynamic mechanisms underlying different fault states. These features rely solely on the geometric characteristics of the waveform at the fault location, rendering them sensitive to fault types while remaining robust to variations in operating conditions. The feature set is subsequently optimized using the minimum redundancy maximum relevance (mRMR) algorithm to enhance computational efficiency, mitigate overfitting, and improve the generalization ability of the method. A set of zero-space observers is then constructed to perform efficient fault classification through geometric operations in the feature space, with each observer specifically sensitive to its corresponding health state while remaining insensitive to others. Experimental validation across multiple health states and operational variations demonstrates that the proposed method outperforms four widely used intelligent models in both classification accuracy and computational efficiency, showing strong suitability for real-world deployment in coal mining applications.