Journal of Northeastern University(Natural Science) ›› 2025, Vol. 46 ›› Issue (7): 37-48.DOI: 10.12068/j.issn.1005-3026.2025.20240164

• Industrial Intelligent Theory and Methods • Previous Articles     Next Articles

Intelligent Identification Method of Industrial Mixed Gases Based on ConvGRU Fusion Attention Mechanism

Fan-li MENG(), Shu-chang LI, Hao WANG, Zhen-yu YUAN   

  1. School of Information Science & Engineering,Northeastern University,Shenyang 110819,China.
  • Received:2024-09-06 Online:2025-07-15 Published:2025-09-24
  • Contact: Fan-li MENG

Abstract:

To address the issue of high data dependency and insufficient accuracy in mixed gas identification for traditional semiconductor gas sensors, a ConvGRUAttention network model that integrates gated recurrent units (GRU), convolutional layers, and attention mechanism is proposed. Empirical wavelet transform (EWT) is employed to convert raw signals into the time-frequency domain and perform multi-scale decomposition, which suppresses noise, reduces data dependency, and enhances the model’s robustness. The model extracts local dynamic features through convolutional layers, captures long-term dependencies using GRU, and optimizes feature weights across multi-scale signals via the attention mechanism, thereby improving feature extraction and generalization capabilities. Experimental results demonstrate 100% accuracy in qualitative identification and a root mean square error (RMSE) of 3.3×10⁻⁶ in quantitative detection. Compared with the traditional methods, the detection accuracy for mixed gases is significantly improved.

Key words: semiconductor gas sensor, empirical wavelet transform (EWT), ConvGRUAttention model, qualitative identification, quantitative detection

CLC Number: