Diagnosis Method of Vortex Flowmeter Based on IoT

摘要

Vortex flowmeter is widely used in various applications because it can measure a wide range of media. When the flowmeter fails, the traditional on-site trouble shooting method has the disadvantages of time-consuming and difficult to ascertain the cause due to the complexity of the field conditions. This paper presents a convenient diagnostic method based on the Internet of Things transmission technology, which consists of the hardware, server and website. The hardware is a vortex flowmeter itself, which is to respond to server-side requests and data feedback, the server-side is used for the information interaction between device-side and web-side and data processing, and the web-side is used for the interaction between human-computer. The server can automatically complete the abnormal checking of the flowmeter's settings. More importantly, it can perform the FFT transformation and feature analysis on the original signal of the vortex sensor. After trouble-shooting, a firmware package containing the corresponding solution can be selected to upgrade the firmware of the flow meter on site remotely. Before the flowmeter is delivered from the factory, all parameters will be saved in the cloud as backup. During the process of diagnosis, the original setting parameters are firstly found according to the serial number of the flowmeter, and then the server will one-to-one match the currently settings and the back up data. After matching, the discrepant parameters will be marked and the parameter modification commands will be sent automatically. After the parameter modification, the server will read the parameters again to ensure the correctness of the amendment. The server will pushes the info of successful amendment and the detailed information of the amendment to the web after the amendment is completed, and the web will shows the specific reasons for the failure. If there is no problem with the parameter setting, the original signal of the vortex sensor is to be obtained, and the server will perform FFT transformation on the acquired time domain signal to obtain the amplitude spectrum, and completes the statistical analysis of the frequency variance and the high frequency component. The server will cross check the abnormal data template with the signal characteristics and the corresponding firmware package is automatically upgraded according to the matching result. After the diagnosis is completed, the field data is saved to improve and enrich the template data. The method presented in this paper offers a simple and intuitive human-computer interaction, and diagnose a vortex flow meter accurately and swiftly to reduce the maintenance cost of the flowmeter.