基于压电振动能量采集器的无线监测传感节点

环境振动状况监测对设备的安全运营至关重要。通过压电振动能量采集器可实现对环境振动信息的感知，再经过智能信息处理方法无线监测设施的安全运营状态。将无线传感与深度学习相结合，在充分研究压电振动能量采集器输出信号特征的基础上，提出了优化的卷积神经网络模型，用于识别环境异常振动模式，并设计实现了智能感知无线监测传感节点。系统工作时，节点可监测环境振动、温度信息并报警异常事件。测试结果表明， 该传感节点在无线传输距离超过100 m 的情况下，实现了环境振动事件的实时监测，报警时间小于5 s，环境振动模式识别准确率可达95．7%，监测环境温度状况并准确报警异常燃烧事件的时间小于3．7 s。该节点在野外目标监测等场合有广泛的应用前景。

Wireless Monitoring Sensor Node Based on Piezoelectric Vibration Energy Harvester

The monitoring of environmental vibration is very important for the safe operation of the equipment. The perception of environmental vibration information can be realized by using piezoelectric vibration energy harvester, and then the safe operation status of the equipment can be monitored wireless through intelligent information processing methods. Combining the wireless sensing with deep learning, on the basis of fully studying the output signal characteristics of piezoelectric vibration energy harvester, an optimized convolutional neural network model is proposed to identify the abnormal vibration patterns of the environment. The intelligent sensing wireless monitoring sensor node is designed and implemented. When the system works, the sensor node can monitor the environmental vibration and temperature information and alarm the abnormal events. The test results show that the wireless transmission distance exceeds 100 m,the sensor node can realize real-time monitoring of the environmental vibration events, the alarm time is less than 5 s, and the accuracy of environmental vibration pattern recognition can reach 95.7%. The node can monitor the ambient temperature and give an accurate alarm for abnormal combustion events in less than 3.7 s. The node has a wide application prospect in fields target monitoring and other occasions.