# Identification of Water Hammering for Centrifugal Pump Drive Systems
## 5 Comparison of Previously Proposed Work with the ML-Based Work  
For establish the novelty of the work, some previously proposed conventional methods and some predictive control methods, as well as their works and results, are compared with the ML-based intelligent system. Lots of conventional methods are available, which is very helpful for identifying the anomalies in the system when the ML method was not implemented. These are the velocity adjustment method, wave characteristics method, column separation method, and variations of water hammering using valve opening and closing, etc. The main disadvantages of most of the conventional methods are they are unable to predict faults at the initial level, they take more time for analysis, and some of the methods are only theoretically tested. The methods are complex and mathematically based, and not applicable for all kinds of data. In the application of ML algorithms, Support Vector Machine (SVM), Principal Component Analysis (PCA), and Decision tree are also useful for the feature extraction of large data, and if the data size is small, then regression or K-Nearest Neighbor (KNN) can be applied. Before the ML application model, predictive control method was also used, which could predict anomalies, but this also had some limitations. Variations in valve closing and opening are some of the methods which can detect anomalies in the system by the change of pressure in different valve opening and closing times. The whole method is based on the characteristic's method. Figure 14 shows how indifferent percentages of valve opening negatively varies the water hammering pressure (WHP). The valves are opened and closed in different percentages, i.e., 25%, 50%, 75%, and 100% and at different closure times. Therefore, it is difficult to measure the accuracy of the process if different times and different percentages of aperture are operated [1]. Moreover, the methods of characteristics (MOC) method is a mathematical model-based method and cannot predict anomalies in the system before the system is shut down. It cannot predict the accuracy of the system. However, in the proposed method, the accuracy of linear regression concerning accuracy rate, prediction time, and training speed is 90%. \begin{table}
\begin{tabular}{|c|c|c|c|} \hline
**Set Pressure** & **Pump 1/Valve 1 (VFD)** & **Pump 2/Valve 2 (VFD)** & **Pump 3/Valve 3 (VFD)** \\ \hline
1 bar & \(\bullet\) & \(\bullet\) & \(\bullet\) \\ \hline
1.3 bar & \(\bullet\) & \(\bullet\) & \(\bullet\) \\ \hline
1.5 bar & \(\bullet\) & \(\surd\)The column separation method is one of the oldest conventional methods to detect water hammering in a system. It can be used when a pipeline is full of liquid. However, the proposed predictive control-based system can predict the anomalies even if the pipeline is not fully filled by fluid. The column separation method is a mathematical model-based method; one of the models in this category is the discrete cavity model. Figure 15 shows how, for different head-value changes, velocity changes, but it can be done by complex numerical solutions, as this method was invented before the computational method started. However, the proposed method is a computational method. For this method, the calculations can be done by computer-based programming so that time can be saved, and energy loss can be prevented [3]. MOC is also a traditional method for water hammering detection by using ordinary differential equations. All the calculations have been done based on boundary equations. Figure 16 shows the  
Figure 14: Variation of water hammering pressure along with the percentage of valve opening. Figure 15: Discrete cavity model for column separation. computational mesh grid with the index for MOC. For compute the pressures and flow velocities, it is necessary to know the initial conditions of the previous step. Therefore, it is a very time-consuming method. If the previous step value cannot be computed, the complete analysis will be stopped.