# Identification of Water Hammering for Centrifugal Pump Drive Systems
## 7 Conclusions  
This experiment has been done to identify water hammering in VFD-based master-follower parallel pumping systems using the Vib Sensor app. It is an experiment-based work, and an industry-academic joint collaborative work to discover the solutions for an industrial pumping system. The main contribution of this research is the application of ML algorithms, mainly regression algorithm, to identify water hammering in a pumping system that is less complicated, handles a large amount of data, takes less time to classify faults, and predicts anomalies at the initial stage to save the device from total failure. At four different pressures, i.e., 1, 1.3, 1.5, and 2 bar pressure head, the flow rate of the pump has been measured under both normal and water hammering conditions. Here, vibration data has been collected through the Vib Sensor app fixed to the pump motor and by FFT analysis for both normal and water hammering conditions. The current signature has been analyzed using time-domain analysis, and it is demonstrated that when the fault occurs, more noise is created compared to the signal. At different pressures and velocities, there is fluctuation to the flow of the water, as shown by Figures 7 and 9. When the pressure head drops suddenly, and sudden hydraulic shock is created in the pipeline, only water hammering is seen. Figure 9 shows a water hammering point and no water hammering point to classify the faulty and not-faulty points. In Figure 9, red points marked with a square area are the faulty points, and blue points marked with a circle are not-faulty points. After collecting data to predict the problem, various ML algorithms have been applied. Among them, linear regression has more accuracy with respect to prediction speed, training time, and accuracy rate. It is seen that the application of an ML algorithm reduces the time for the prediction of a fault, which is helpful for solving problems very fast and thus preventing damage to the whole pumping system. When ML-based techniques are compared with conventional methods, it is seen that regression analysis is a more accurate and less time-consuming predictive method rather than any other technique. In future, other features of water hammering such as materials of the pipe, valve types, water level reservoir, etc. will be considered to obtain more accurate results regarding anomaly detection. In other industrial applications, ML can also be applied in future. **Author Contributions:** [PERSON], [PERSON] have done the fault analysis in VFD based multistage pumping system, U.S. jointly for analysis of different valve position, the nature of the faults using regression analysis. [PERSON], [PERSON]-[PERSON], [PERSON], [PERSON] shared their expertise in the field of power electronics, control and fault analysis in pumping system using variable speed ac drives. All authors involved equally to articulate the research work for its final depiction as a full research paper. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the Renewable Energy Lab, Department of Communications and Networks, College of Engineering, Prince Sultan University, Riyadh, 11586, Saudi Arabia. **Acknowledgments:** This research work activity carried out on an industry-academic collaborative project. Thanks to all partners, VIT University, Vellore, India, and Danfoss Pvt. Ltd. industry Chennai and Denmark. For carrying out the work the authors got support from Danfoss Advance Drives laboratory, VIT University, India, Renewable Energy lab, Prince Sultan University Saudi Arabia, Center for Bioenergy and Green Engineering (Esbjerg)/Center for Reliable Power Electronics (CORPE-Aalborg), Aalborg University, Denmark. For this research, the authors owe their gratitude to all these institutions. **Conflicts of Interest:** The authors declare no conflict of interest. **Abbreviations**