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<p>In industrial environments, the operating status of safety valves is easily affected by complex electromagnetic interference and changes in operating conditions. Traditional monitoring methods lack real-time, intelligent analysis capabilities, making it difficult to issue timely warnings of abnormal conditions. This paper proposes a wireless transmission adaptive safety valve monitoring system based on machine learning, and realizes multimodal data acquisition by building embedded sensor nodes and uses anti-interference radio frequency modules to ensure communication stability in complex electromagnetic environments; this paper introduces a gated attention mechanism and a Long Short-Term Memory (LSTM) model optimized with a channel pruning strategy at the edge, realizes efficient reasoning by enhancing local feature extraction capabilities and model compression, and completes online identification and trend prediction of the safety valve status; at the same time, this paper introduces an adaptive control mechanism to dynamically optimize the sampling frequency and transmission strategy to improve the efficiency of system resource utilization. The experimental results show that the system achieves an average state recognition accuracy of 97.6% during continuous monitoring, the communication packet loss rate is controlled at 1.2% or less, and the average response delay is 47.64ms, which meets the needs of rapid response to abnormal states of safety valves in industrial scenarios. The research effectively improves the intelligence, adaptability, and reliability of the safety valve monitoring system.</p>

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Wireless transmission adaptive safety valve monitoring system based on machine learning

  • 期刊论文

  • 发表日期：

    2026年2月23日

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  • Min Han,
  • Xiaoyu Liu

摘要

In industrial environments, the operating status of safety valves is easily affected by complex electromagnetic interference and changes in operating conditions. Traditional monitoring methods lack real-time, intelligent analysis capabilities, making it difficult to issue timely warnings of abnormal conditions. This paper proposes a wireless transmission adaptive safety valve monitoring system based on machine learning, and realizes multimodal data acquisition by building embedded sensor nodes and uses anti-interference radio frequency modules to ensure communication stability in complex electromagnetic environments; this paper introduces a gated attention mechanism and a Long Short-Term Memory (LSTM) model optimized with a channel pruning strategy at the edge, realizes efficient reasoning by enhancing local feature extraction capabilities and model compression, and completes online identification and trend prediction of the safety valve status; at the same time, this paper introduces an adaptive control mechanism to dynamically optimize the sampling frequency and transmission strategy to improve the efficiency of system resource utilization. The experimental results show that the system achieves an average state recognition accuracy of 97.6% during continuous monitoring, the communication packet loss rate is controlled at 1.2% or less, and the average response delay is 47.64ms, which meets the needs of rapid response to abnormal states of safety valves in industrial scenarios. The research effectively improves the intelligence, adaptability, and reliability of the safety valve monitoring system.