基于云平台的蒸渗仪远程故障诊断方法研究

Cloud Platform based Remote Fault Diagnosis Method for Lysimeters

摘要:

针对蒸渗仪部署分散，设备产生故障后不能及时发现的问题，设计了一种基于云平台的远程故障诊断系统。首先，利用无线通讯技术，将蒸渗仪采集的数据发送至远程云平台；然后，采用卡尔曼滤波算法与阈值检测机制对采集数据进行异常检测；在此基础上，采用基于贝叶斯网络的故障诊断方法对异常数据进行分析，从而推断设备故障原因；最后，通过实时更新历史故障库来动态优化贝叶斯网络诊断模型的结构和参数，以提高系统的正确诊断率。实际应用结果表明，该系统能有效地检测出蒸渗仪的异常信息并给出故障原因，对确保监测数据的正确性具有重要意义。

Abstract:

The lysimeters are usually dispersedly deployed and the fault is difficult to find in time. Therefore, a remote fault diagnosis system based on cloud platform is designed in this paper. Firstly, the wireless communication technology is used to transmit the data collected by the lysimeters to the remote cloud platform. Then, the Kalman filter algorithm and the threshold detection mechanism are employed to detect the abnormality of the collected data. On this basis, the fault diagnosis method based on Bayesian network is adopted to analysis abnormal data for inferring the reasons of equipment failure. Finally, the real-time updating of the historical fault library optimizes the structure and parameters of Bayesian network diagnostic model dynamically, such that the correct diagnostic rate of the system is improved. The practical application results indicate that the system can detect the abnormal information of lysimeters effectively and provide fault cause, which is of great significance to ensure the validity of the monitoring data.