SVM Algorithm for Vibration Fault Diagnosis in Centrifugal Pump

Vibration failure in the pumping system is a significant issue for industries that rely on the pump as a critical device which requires regular maintenance. To save energy and money, a new automated system must be developed that can detect anomalies at an early stage. This paper presents a case study of a machine learning (ML)-based computational technique for automatic fault detection in a cascade pumping system based on variable frequency drive (VFD). Since the intensity of the vibrational effect depends on which axis has the most significant effect, a three-axis accelerometer is used to measure it in the pumping system. The emphasis is on determining the vibration effect on different axes. For experiment, various ML algorithms are investigated on collected vibratory data through Matlab software in x, y, z axes and performances of the algorithms are compared based on accuracy rate, prediction speed and training time. Based on the proposed research results, the multiclass support vector machine (MSVM) is found to be the best suitable algorithm compared to other algorithms. It has been demonstrated that ML algorithms can detect faults automatically rather than conventional methods. MSVM is used for the proposed work because it is less complex and pro-duces better results with a limited data set.