# Identification of Water Hammering for Centrifugal Pump Drive Systems
## 6 Discussion  
In this work, the prediction of the fault and the no-fault situation has been identified by the linear regression method. From the waveform, it is verified that at the low-pressure point, water hammering can occur. To elaborate, it happens that when the device pressure head becomes less than the vapor pressure head, i.e., 1.42 m, a high-velocity point occurs where the possibility of pressure drop is high. It is mainly due to sudden changes in hydraulic velocity shock in the pipeline, and water hammering occurs. It is shown that the accuracy of linear regression is highest, the prediction speed is 5 obs/s, and the training time is 16.677 s (Figure 20). Before artificial intelligence (AI) technology was invented, conventional methods such as MOC, wave characteristics method, column separation method, and velocity adjustment method, investigation through pipeline methods were used to identify water hammering. However, these are complex, time-consuming, and numerical analysis-based methods, and a few fails to predict the problem at the initial stage itself so enormous energy is lost. In an AI-based neural network, ML, optimization algorithms-based genetic algorithm and heuristic algorithm-based techniques have introduced a significant change in industrial applications. Also, ML plays a significant role in fault detection. It is highly predictive control-based technology that predicts the anomalies at the initial stage so that the device can be saved from total failure. Huge energy loss can also be prevented. It can handle massive data, and within a few seconds, it can predict a fault. So continuous monitoring is possible by ML-based predictive control method. In Section 5, the conventional methods such as MOC, wave characteristics, column separation, valve closing and opening, model predictive control, etc. are compared with the ML-based predictive control method. From Figure 19, after comparing the accuracy of all the methods with the proposed ML-based method, it is seen that an ML-based method, mainly regression analysis, can provide more accurate results, as it takes less time for prediction, and can handle both large amounts of data and small sizes of data. In the proposed research, the data size is small, so regression analysis has been chosen for the identification of faults. The performance of the intelligent method is also high, whereas the model predictive control method can predict anomalies when the system is open-loop and small. In the case of a robust system, it cannot show an accurate result. Variation of valve-closing and -opening method is a simple method. When using different percentages of value, valves are closed, and this method can be applied. The novelty of the proposed work is to reduce prediction time by implementing ML technology, reducing energy loss, and getting more accurate results of the output of the system. It handles a robust system, and both open- and closed-loop conditions can be implemented. Table 6 is a comparison of various conventional methods and predictive control methods based on ML algorithms. Figure 20: Comparisons of the overall accuracy of different ML algorithms.