# Research on Fault Detection and Automatic Diagnosis Technology of Water Hammer in Centrifugal Pump
###### Abstract  
Centrifugal pumps are important equipment in industrial production, and their safe and reliable operation is of great significance to water supply and industrial safety. During the use of centrifugal pumps, faults such as bearing damage, blade wear, shaft imbalance, shaft misalignment and water hammer often occur. Among them, although water hammer faults occur at a low frequency, they are difficult to monitor and pose significant risks to valve and pipeline interfaces. This article analyzes the causes, mechanisms and phenomena of water hammer faults in centrifugal pumps, designs a monitoring method to effectively monitor the vibration signal of the centrifugal pumps, extracts vibration characteristics to determine and record water hammer events, designs monitoring and diagnostic models for the edge layer and server side, and establishes an experimental verification testing system. The test results show that, under the conditions of simulating water hammer faults, after high-pass filtering of the collected vibration data, the kurtosis index, pulse index and margin index all exceed twice the threshold, and both sensors emit water hammer alarms. The designed data acquisition method can capture water hammer signals in a timely manner, and the analysis model can automatically identify water hammer faults based on existing fault knowledge and rules. This fully demonstrates the scientific and effective nature of the proposed centrifugal pump fault monitoring method and system, which is of great significance for ensuring the safe operation and improving the design of centrifugal pumps. centrifugal pump; water hammer; data acquisition; edge computing; intelligent diagnosis Article  
Research on Fault Detection and  
Technology of Water Hammer in Centrifugal Pump  
**[PERSON]**  
**+**: _Applied_  
**+**: _Sciences_  
**+**: _Applied_  
**+**: _Sciences_  
**+**: _Applied_  
**+**: _Sciences_  
**+**: _Applied_  
during pump stop was analyzed and theoretically calculated, which provided a basis for the optimal design of a pump system in the hydraulic transition process; the corresponding measures to protect the pump system from water hammer in various occasions were also introduced. In the literature [11; 12], the generation and propagation mechanism of the water hammer impact pressure wave were studied, and the influence of the water hammer wave speed on the system's natural frequency and mode shape were analyzed. In the literature [13; 14], the closing law curve was obtained based on Bayesian grid modeling technology, and the water hammer impact was effectively reduced. Although the above literature has made some progress in the analysis of water hammer mechanics and damage assessment, research on water hammer phenomena is still in the developmental stage. At present, there is still no effective means for the automatic capture and recognition of water hammer signals, and there are still some problems:  
1. Methods for the online monitoring and diagnosis of water hammer faults are still limited in practical engineering applications. 2. Monitoring methods have lag. Traditional monitoring methods have lag and are difficult to respond to and address potential equipment damage and safety hazards in a timely manner. 3. Monitoring methods have low data processing efficiency and a strong dependence on manual intervention. Traditional monitoring methods have low efficiency in data collection and transmission, and the diagnostic process relies on manual labor, which affects time and accuracy, while increasing costs and manpower requirements. As new technologies, edge computing and cloud computing bring new opportunities to monitor water hammer failures and diagnose centrifugal pumps. Through real-time data acquisition and processing, edge computing can quickly respond to water hammer failures and the centrifugal pump operation status. Cloud computing enables data storage and trend analysis, enabling intelligent and automated diagnosis.