# Identification of Water Hammering for Centrifugal Pump Drive Systems
## 1 Introduction  
Centrifugal pumps are commonly used for a variety of industries and used for fluid delivery. They are driven by an electrical motor, usually an induction motor. Regular maintenance of the pumphelps to avoid sudden failure in the system because of sudden pressure changes that cause efficiency reduction that result in hydraulic failure. Water hammering is a problem in pumping systems caused by the change in flow, velocity resulting from a change in valve-closing and valve-opening times. It happens as pressure surge, and transient flow and vibration created in the system. Water hammering can analyze by the percentage of valves closing at different time-periods [1]. Mechanical failure can happen when any component of the pump fails to function or because of continuous use. Problems may arise within these machines are a reduction of the fluid flow within the pipelines, and interruption of the transportation of fluid to its destination in the process. Bearing failure, impeller breakage, seal breakage, and lubrication problems are the causes of mechanical failure. The failure creates a blockage in the system, interrupts the fluid flow, and causes substantial economic loss [2]. Continuous monitoring of devices and proper maintenance of the system can reduce maintenance costs and at the same time, increase the lifetime of the machine. Various predictive and condition-based monitoring techniques are available for diagnosis and identification of incipient faults in centrifugal pumping systems. Unnatural variation of any parameter causes significant faults, which should be prevented in due time [3]. In some cases, it is challenging to monitor every parameter manually, but machine learning (ML)-based algorithm techniques are used as an effective method for fault detection, due to their advantages over conventional methods such as signal-based fault detection, model-based fault detection, vibration analysis-based anomaly detection, and current spectrum analysis, etc. These traditional methods are time-consuming, costly, and less efficient. It is possible to predict the best-suited output using ML-based predictive control methods instead of the traditional method [4]. Transient flow analysis is considered to be one of the reasons for water hammering. Flow analysis of the feeder network of the Baneta Water Distribution system shows that transient flow depends on pump inertia and valve closure time. Due to the more significant velocity of water, the effect of the wave front of velocity can be varied [5; 6]. In long-distance pipeline-based simulations, a multi-valve protection system is used. To analyze the velocity and flow rate of the fluid in a pipeline, decreasing flow rate and sudden pressure-head changes are used. For keep the pressure inside the pipe within an allowable range, excess pressure-escape valves are installed at dry openings. For satisfy all the additional attachments, a multifunctional pump control valve may be installed [7]. Analysis of wave velocity is typical for piping systems and has been made. Flow-rate values are compared with velocity values in the transient condition. The change of wave in the fluid causes water hammering when the least volume of materials is used in the wall, bottom, and roof of the surge tank. The pressure variation at different points with different fast shutting timings is analyzed for pipes by genetic and fuzzy algorithms. Fulfilment of the minimum pressure constraint and critical submergence shows the effectiveness of the fuzzy-genetic method [8]. Conventional methods are time-consuming, complex, and less accurate. The unsupervised self-learning method is helpful for fault analysis, and prediction of the fault is also possible. The deep learning (DL) method requires new feature extraction, and a non-stationary SoftMax regression model is suitable for automatic identification of failure nodes. Predictive control pattern recognition technology helps improve efficiency.