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ISBOA-KELM multi-sensor data fusion model for early warning method in laboratory safety
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Liang GE1, 2, Nüqing ZHOU1, Honglei CHE3, Guoqing XIAO4, Xi LAI1, Wen ZENG5
China Safety Science Journal | 2026, 36(1) : 63 - 71
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China Safety Science Journal | 2026, 36(1): 63-71
• Safety Technology and Engineering •
ISBOA-KELM multi-sensor data fusion model for early warning method in laboratory safety
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Liang GE1, 2, Nüqing ZHOU1, Honglei CHE3, Guoqing XIAO4, Xi LAI1, Wen ZENG5
Affiliations
  • 1School of Mechanical and Electrical Engineering, Southwest Petroleum University, Chengdu Sichuan 610500, China
  • 2National Key Laboratory of Reservoir Geology and Development Engineering, Southwest Petroleum University, Chengdu Sichuan 6105003, China
  • 3China Academy of Safety Science and Technology, Beijing 100012, China
  • 4School of Chemistry and Chemical Engineering, Southwest Petroleum University, Chengdu Sichuan 610599, China
  • 5College of Materials Science and Engineering, Chongqing University, Chongqing 400045, China
Published: 2026-01-28 doi: 10.16265/j.cnki.issn1003-3033.2026.01.1133
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To address the challenges of complex data environments, low accuracy of single-sensor detection, and limited precision in traditional laboratory safety systems, this study presented a multi-sensor fusion early warning model based on an ISBOA and algorithm KELM. First, the KELM framework was employed to integrate heterogeneous sensor data and construct the warning model, where a regularization term was introduced to alleviate overfitting. Then, the improved ISBOA adaptively optimized the regularization coefficient C and kernel parameterσ of the KELM, thereby enhancing parameter robustness and diagnostic accuracy. Finally, simulation and experimental analyses were conducted using both synthetic and real laboratory datasets, and the proposed ISBOA-KELM model was compared with the unimproved Secretary Bird Optimization Algorithm (SBOA), Particle Swarm Optimization (PSO), and Gray Wolf Optimization (GWO) algorithms. The experimental results show that the ISBOA-KELM model improved accuracy by 4%, 3%, and 2%, respectively, compared with the other three models. In four representative laboratory safety scenarios, including fire and gas leakage, the detection accuracy exceeds 96% with the false negative rate below 6%, which significantly improves the reliability and robustness of safety accident early warning.

laboratory safety  /  improved secretary bird optimization algorithm (ISBOA)  /  kernel extreme learning machine (KELM)  /  multi-sensor data fusion  /  intelligent early warning
Liang GE, Nüqing ZHOU, Honglei CHE, Guoqing XIAO, Xi LAI, Wen ZENG. ISBOA-KELM multi-sensor data fusion model for early warning method in laboratory safety[J]. China Safety Science Journal, 2026 , 36 (1) : 63 -71 . DOI: 10.16265/j.cnki.issn1003-3033.2026.01.1133
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Year 2026 volume 36 Issue 1
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2026.01.1133
  • Receive Date：2025-09-14
  • Online Date：2026-07-08
  • Published：2026-01-28
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History
  • Received：2025-09-14
  • Revised：2025-11-22
Funding
Affiliations
    1School of Mechanical and Electrical Engineering, Southwest Petroleum University, Chengdu Sichuan 610500, China
    2National Key Laboratory of Reservoir Geology and Development Engineering, Southwest Petroleum University, Chengdu Sichuan 6105003, China
    3China Academy of Safety Science and Technology, Beijing 100012, China
    4School of Chemistry and Chemical Engineering, Southwest Petroleum University, Chengdu Sichuan 610599, China
    5College of Materials Science and Engineering, Chongqing University, Chongqing 400045, China
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