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<p>To address the lack of quantitative characterization methods for flame propagation during internal non-ignition detonation tests of explosion-proof equipment, this study proposes a damage severity quantification method using an improved LSTM model with thermal imaging feature fusion. A detonation flame thermal imaging dataset is constructed, where flame contours are segmented and features extracted via a pre-trained Faster R-CNN. Temporal sampling generates flame image sequences, from which key spatiotemporal features—area, centroid velocity, aspect ratio, circularity, and area change rate—are derived to describe dynamic flame propagation. Based on this, the improved LSTM captures time-varying behavior: a first-layer bidirectional LSTM fuses forward and backward temporal data to detect propagation direction changes, multi-layer stacking extracts high-level features for adapting to damage scale variation, and Dropout between layers mitigates overfitting. Experimental results show prediction errors remain within 2% across 5–20&#xa0;cm damage levels. The near-zero shift in error distribution confirms the effectiveness of bidirectional fusion, deep feature modeling, and regularization in capturing flame temporal dynamics. This research provides a data-driven approach for accurate damage diagnosis and supports safety certification and maintenance of mining explosion-proof equipment, with implications for coal mine safety.</p>

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Damage quantification of mining explosion-proof equipment using thermal flame dynamics and improved LSTM

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月1日

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  • Xue Xusheng,
  • Zhang Enqiao,
  • Zhang Hongkui,
  • Mao Qinghua,
  • Chen Fandong,
  • Zhang Xuhui,
  • Qiu Junbiao

摘要

To address the lack of quantitative characterization methods for flame propagation during internal non-ignition detonation tests of explosion-proof equipment, this study proposes a damage severity quantification method using an improved LSTM model with thermal imaging feature fusion. A detonation flame thermal imaging dataset is constructed, where flame contours are segmented and features extracted via a pre-trained Faster R-CNN. Temporal sampling generates flame image sequences, from which key spatiotemporal features—area, centroid velocity, aspect ratio, circularity, and area change rate—are derived to describe dynamic flame propagation. Based on this, the improved LSTM captures time-varying behavior: a first-layer bidirectional LSTM fuses forward and backward temporal data to detect propagation direction changes, multi-layer stacking extracts high-level features for adapting to damage scale variation, and Dropout between layers mitigates overfitting. Experimental results show prediction errors remain within 2% across 5–20 cm damage levels. The near-zero shift in error distribution confirms the effectiveness of bidirectional fusion, deep feature modeling, and regularization in capturing flame temporal dynamics. This research provides a data-driven approach for accurate damage diagnosis and supports safety certification and maintenance of mining explosion-proof equipment, with implications for coal mine safety.