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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.