Open AccessFault Diagnosis in Centrifugal Pump using Support Vector Machine and Artificial Neural Network
Author(s) -
Nagendra Singh Ranawat,
Pavan Kumar Kankar,
Ankur Miglani
Publication year - 2021
Publication title -
maǧallaẗ al-abḥāṯ al-handasiyyaẗ
Language(s) - English
Resource type - Journals
eISSN - 2307-1885
pISSN - 2307-1877
DOI - 10.36909/jer.emsme.13881
Subject(s) - support vector machine , artificial neural network , artificial intelligence , centrifugal pump , fault (geology) , computer science , pattern recognition (psychology) , ranking (information retrieval) , feature (linguistics) , feature extraction , machine learning , reliability (semiconductor) , engineering , mechanical engineering , linguistics , philosophy , power (physics) , physics , quantum mechanics , impeller , seismology , geology
Centrifugal pumps are commonly utilized in thermo-fluidic systems in the industry. Being a rotating machinery, they are prone to vibrations and their premature failure may affect the system predictability and reliability. To avoid their premature breakdown during operation, it is necessary to diagnose the faults in a pump at their initial stage. This study presents the methodology to diagnose fault of a cent rifugal pump using two distinct machine learning techniques, namely, Support vector machine (SVM) and Artificial neural network (ANN). Different statistical features are extracted in the time and the frequency domain of the vibration signal for different working conditions of the pump. Furthermore, to decrease the dimensionality of the obtained features different feature ranking (FR) methods, namely, Chi-square, ReliefF and XGBoost are employed. ANN technique is found to be more efficient in classifying faults in a centrifugal pump as compared to the SVM, and Chi-square and XGBoost ranking techniques are better than ReliefF at sorting more relevant features. The results presented in thus study demonstrate that an ANN based machine learning approach with Chi-square and XGBoost feature ranking techniques can be used effectively for the fault diagnosis of a centrifugal pump.