Review of machine learning based fault detection for centrifugal pump induction motors
AffiliationUniversity of Bedfordshire
motor current signature analysis
MetadataShow full item record
AbstractCentrifugal pumps are an integral part of many industrial processes and are used extensively in water supply, sewage, heating and cooling systems. While there are several review papers on machine learning-based fault diagnosis on induction motors, its application to centrifugal pumps has received relatively little attention. This work attempts to summarize and review recent research and development in machine learning-based pump condition monitoring and fault diagnosis. The paper starts with a brief explanation of pump operation including common pump faults and the main principles of the motor current signature analysis (MCSA) method. This is followed by a detailed explanation of various machine learning-based methods including the types of detected faults, experimental details and reported accuracies. The performances of different approaches are then presented systematically in a unified table. Finally, the authors discuss practical aspects and challenges related to data collection, storage and real-world implementation.
CitationSunal CE, Dyo V, Velisavljevic V (2022) 'Review of Machine Learning Based Fault Detection for Centrifugal Pump Induction Motors', IEEE Access, 10 (), pp.71344-71355.
SponsorsInnovate UK KTP grant
The following license files are associated with this item:
- Creative Commons
Except where otherwise noted, this item's license is described as Green - can archive pre-print and post-print or publisher's version/PDF