An intelligent data analysis system for fault diagnosis of marine monitoring sensors
Shiyao Zhao, Miaomiao Song, Yunlu Liu, Shuting Liu, Jianjia Zheng, Sijia Wang, Peng Tai, Ziliang Jiang · 2022
In order to improve the accuracy of the monitoring data of marine monitoring sensors, diagnose various potential faults of the sensors and repair them in time, an intelligent data analysis system for diagnosing the faults of the marine monitoring sensor is proposed and developed. Three kinds of data analysis methods based on principle of statistics, including the Grubbs Criterion, the PauTa Criterion and the Dixon Criterion, are used to realize the automatic detection of abnormal data and the corresponding algorithm workflow is designed and implemented with Python intelligent computing modules. Taking the wave sensor data as an example, a set of experiments are conducted to verify the effectiveness of the intelligent system. The results indicate that the system ensures the effectiveness and accuracy of monitoring data properly and it can be used to monitor and analyze the abnormal data of wave sensors for fault diagnosis.