Tunnel drilling rig is the key equipment used for exploration in underground coal mine. Because it operates for long periods in environments characterized by high humidity, intense vibration, pressure fluctuations, and unstable geological formations, various faults tend to appear frequently. If these faults are not identified in time, they may gradually worsen and ultimately result in severe accidents. Traditional fault detection methods mainly rely on manual inspection, which makes it difficult to obtain reliable information and respond effectively under complex and changing working conditions. To overcome these shortcomings, this study proposes a fault detection approach based on a sparse autoencoder. The raw signals, including pressure, speed, and feed rate, are first preprocessed. After that, a normal operating model of the drilling rig is learned through the sparse autoencoder. Faults are then detected by comparing the real-time reconstruction errors with a preset threshold. Finally, experiments based on actual drilling data are performed, and the results demonstrate the effectiveness of the proposed method.

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