# Research on Fault Detection and Automatic Diagnosis Technology of Water Hammer in Centrifugal Pump
## 4 Design of Water Hammer Fault Monitoring and Diagnosis System
### Monitoring System Data Collection and Scheduling Method  
The collector can collect the acceleration sensor signal and perform edge computing in the collector to quickly extract the signs of water hammer in the signal. The water  
Figure 4: Interactive topology diagram of edge side and cloud side.  
hammer fault has strong occasional and short duration characteristics after it occurs [25, 26]. According to these characteristics, the data collection and scheduling method was designed. The method is shown in Figure 5. When each group of data arrives, the collector collects data frame by frame, calculates the characteristic parameters of water hammer and determines whether water hammer has occurred. Specifically, if a malfunction occurs, the set of data in the collector is temporarily stored. If there are no faults, the next collection is prepared, and the final frame of additional temporary data is stored before the upload time to detect other types of faults. The idle time interval between each frame of data is within 1 s to ensure that any water hammer event can be monitored. In this way, the extraction interval of symptom extraction is controlled within 1 s, which effectively avoids omission caused by not collecting symptoms when the water hammer event occurs; the collected vibration data are uploaded to the server through the network. And the server learns and corrects the alarm threshold based on the historical data and sends the learned alarm threshold to the collector. If each group of data is uploaded to the server, it takes up a lot of network bandwidth and storage resources, and most of the data features are similar without significant changes. In addition to water hammer, other mechanical failures of centrifugal pumps have a gradual process, so in terms of uploading collected data, a reasonable time interval can be set, such as uploading once every 30s to reduce the upload frequency and network bandwidth usage.  
The data transmission strategy is shown in Figure 6. If the water hammer fault symptom is not detected before the end of the upload window, it indicates that there are no temporary data. Therefore, we calculate other characteristic parameters except for the water hammer fault symptom based on the final frame of the additional temporary data collected within this window. Among them, the fault characteristic parameters mainly include common centrifugal pump faults: balance failure, misalignment, looseness, impeller failure, cavitation, etc. If none of the parameters exceed the predefined threshold, only the fault symptom parameters are uploaded. However, if one or more of these parameters violate the alarm values, the symptom parameters and the entire vibration waveform dataset are uploaded to the server.  
Figure 5: Flow chart of collection and scheduling method.  
If the dataset collected before the upload window closes shows water hammer fault characteristics exceeding the set threshold, it indicates the presence of temporary data. When the upload window opens, the dataset is uploaded together with the calculated fault symptom parameters. If multiple feature datasets collected after detecting water hammer symptoms exceed the standard, we prioritize uploading the first dataset that shows water hammer features exceeding the threshold. In addition to this data transmission strategy, we also upload waveform data at a fixed time every day (such as 13:00).