# Identification of Water Hammering for Centrifugal Pump Drive Systems
## 5 Comparison of Previously Proposed Work with the ML-Based Work
However, in the case of the proposed method, the value will be automatically transferred to the next value as a training set, so the process will never be hampered, and anomalies can be detected easily [25]. The wave characteristics method is a straightforward method to identify water hammering. Rapid changes in the velocity of fluid during valve closing generate high pressure, which is transmitted through the system at the speed of sound. The method is based on the mathematical model, and the whole process is time-consuming. The processes can be done one by one, i.e., after the detection of one pipeline's anomalies, the next pipeline's anomalies can be detected. Figure 17 shows the change of velocity when valves are closed. However, in the proposed ML-based method, at a time n, no pipelines' anomalies can be detected [18]. Instead of conventional methods, the predictive control based intelligent method also can be implemented for the identification of the fault. SVM is one of the intelligent methods that can be implemented mainly for identification of faults and binary classification. Sludge, electrical fault and cavitation problem in the pumping system can be identified and classified by SVM easily [29; 30]. The velocity adjustment method can be done by controlling the velocity and keeping the velocity at a constant value. The method can be implemented for a multiple-valve system, but then velocity will be variable and valve closure time will be more like 44 s, so the method is more time-consuming. It can be implemented in the mine drainage system. Limited space and the change of costly equipment are the main characteristics of mine drainage systems. For the problem of valve-closing water hammering, protection of a mine drainage system requires a protection method based on velocity adjustment of the hydraulic valve system [17]. Figure 18 shows the flow chart of the water hammering detection method. The Lax-Wendroff difference method with nonlinear filter can solve water hammering in a pumping system. It is better than the MOC method, as it does not depend on time changes. However, in instantaneous valve closure and pressure discontinuities, the method fails to predict the fault [31]. Figure 16: Computational mesh grid with an index for MOC. Previously, the model predictive control method was used to identify faults in a system. For fault detection of a six-phase induction machine and fault detection of a boiler, predictive control methods are applied. The model predictive control is also mathematical model-based technology. In induction motor applications, it can adapt the flux value to reduce copper loss. However, tests can be done when there is an open-loop system. In a closed-loop system, robust system model predictive control technology is challenging to implement [32; 33]. The Kalman filter-based soft sensor can predict faults for an electro-pump system. The method is applicable to nonlinear systems. The main disadvantage is it is applicable only for sensory soft-fault detection and can only be theoretically applied. Practically, the method has not yet been tested [34]. PCA is one of the ML methods by which the spring fault and off-shoe fault of a pumping system can be detected. However, this method can also only be theoretically applied, and so far, has not been applied practically [35]. Cavitation problems in the pump can be easily identified by the load torque signature analysis method. This is an experimental procedure and can be successfully implemented in a frequency inverter drive-based pumping system [36]. For an axial piston pump, a spare SVM algorithm can be applied, which is one of the simple ML algorithms for fault detection. The SVM model can accurately detect bearing faults in the axial lubricating pump, which is 96.67%. However, one problem of this experimental procedure is if the dataset is large then SVM will work better, but if the dataset is small, then the feature extraction will not be accurate using this algorithm [37]. For small data sizes, SVM accuracy is 70%, and for PCA it is 55%.