Objective In coal mines, harsh environments and complex geological conditions underground lead to frequent failures of tunnel drilling rigs. However, conventional techniques for failure diagnosis and early warning suffer from limitations in diagnostic accuracy, real-time performance, and interpretability. Therefore, these techniques are insufficient for both the rapid identification of the anomalous status of tunnel drilling rigs and the early warning of their potential failures.

Methods Considering the complex underground environments in coal mines, this study developed an intelligent failure diagnosis and early warning system for tunnel drilling rigs in coal mines, placing a particular emphasis on key technologies for failure diagnosis and early warning. For failure diagnosis, the coupling relationships among variables were quantitatively characterized by combining the operating mechanisms of key circuits and cross-correlation function (CCF) analysis. Accordingly, the failure propagation patterns were revealed, and a diagnosis strategy based on node characteristics was established. For early warning of potential failures, the operational status of drilling rigs was quantified using time domain features such as mean and standard deviation. Then, a health index was constructed using principal component analysis (PCA). Finally, a hierarchical early warning mechanism was established in combination with Z-scores and the three-sigma rule. Based on these, an intelligent failure diagnosis and early warning system for tunnel drilling rigs was established through software development based on the Qt platform and the MySQL database.

Results and Conclusions Using field tests at the Zhashui test base, the performance of the developed system was verified. The results indicate that the developed system achieved an accuracy of 96.8% and a recall of 90.5% for failure diagnosis. For early warning, this system yielded a failure detection rate of 94.6% and a false alarm rate of 1.5%, indicating a small number of false alarms. Compared to conventional passive protection technologies, the intelligent failure diagnosis and early warning system for tunnel drilling rigs developed in this study can accurately identify the operational status of tunnel drilling rigs and assess their health degrees in real time, thereby enabling timely early warning of potential faults. This study provides a theoretical basis and engineering solution for the stable operation and predictive maintenance of tunnel drilling rigs.