| Author Name | Affiliation | | Janani Shruti Rapur | Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, India | | Rajiv Tiwari | Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, India | | Aakash Dewangan | Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, India | | D. J. Bordoloi | Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, India |
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| Abstract: |
| Condition based maintenance (CBM) is one of the solutions to machinery maintenance requirements. Latest approaches to CBM aim at reducing human engagement in the real-time fault detection and decision making. Machine learning techniques like fuzzy-logic-based systems, neural networks, and support vector machines help to reduce human involvement. Most of these techniques provide fault information with 100% confidence. It is undeniably apparent that this area has a vast application scope. To facilitate future exploration, this review is presented describing the centrifugal pump faults, the signals they generate, their CBM based diagnostic schemes, and case studies for blockage and cavitation fault detection in centrifugal pump (CP) by performing the experiment on test rig. The classification accuracy is above 98% for fault detection. This review gives a head-start to new researchers in this field and identifies the un-touched areas pertaining to CP fault diagnosis. |
| Key words: centrifugal pumps condition-based maintenance fault diagnosis machine learning techniques review |
| DOI：10.11916/j.issn.1005-9113.2021147 |
| Clc Number:TP306; TP183 |
| Fund: |
