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dc.contributor.authorChennai Viswanathan, Prasshanth
dc.contributor.authorVenkatesh, Sridharan Naveen
dc.contributor.authorDhanasekaran, Seshathiri
dc.contributor.authorMahanta, Tapan Kumar
dc.contributor.authorSugumaran, Vaithiyanathan
dc.contributor.authorLakshmaiya, Natrayan
dc.contributor.authorParamasivam, Prabhu
dc.contributor.authorNanjagoundenpalayam Ramasamy, Sakthivel
dc.date.accessioned2023-11-21T12:35:58Z
dc.date.available2023-11-21T12:35:58Z
dc.date.issued2023-08-31
dc.description.abstractAbstract The reliable operation of monoblock centrifugal pumps (MCP) is crucial in various industrial applications. Achieving optimal performance and minimizing costly downtime requires effectively detecting and diagnosing faults in critical pump components. This study proposes an innovative approach that leverages deep transfer learning techniques. An accelerometer was adopted to capture vibration signals emitted by the pump. These signals are then converted into spectrogram images which serve as the input for a sophisticated classification system based on deep learning. This enables the accurate identification and diagnosis of pump faults. To evaluate the effectiveness of the proposed methodology, 15 pre-trained networks including ResNet-50, InceptionV3, GoogLeNet, DenseNet-201, ShuffleNet, VGG-19, MobileNet-v2, InceptionResNetV2, VGG-16, NasNetmobile, EfficientNetb0, AlexNet, ResNet-18, Xception, ResNet101 and ResNet-18 were employed. The experimental results demonstrate the efficacy of the proposed approach with AlexNet exhibiting the highest level of accuracy among the pre-trained networks. Additionally, a meticulous evaluation of the execution time of the classification process was performed. AlexNet achieved 100.00% accuracy with an impressive execution (training) time of 17 s. This research provides invaluable insights into applying deep transfer learning for fault detection and diagnosis in MCP. Using pre-trained networks offers an efficient and precise solution for this task. The findings of this study have the potential to significantly enhance the reliability and maintenance practices of MCP in various industrial settings.en_US
dc.identifier.citationChennai Viswanathan, Venkatesh, Dhanasekaran, Mahanta, Sugumaran, Lakshmaiya, Paramasivam, Nanjagoundenpalayam Ramasamy. Deep Learning for Enhanced Fault Diagnosis of Monoblock Centrifugal Pumps: Spectrogram-Based Analysis. Machines. 2023;11(9)en_US
dc.identifier.cristinIDFRIDAID 2184182
dc.identifier.doi10.3390/machines11090874
dc.identifier.issn2075-1702
dc.identifier.urihttps://hdl.handle.net/10037/31841
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.journalMachines
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleDeep Learning for Enhanced Fault Diagnosis of Monoblock Centrifugal Pumps: Spectrogram-Based Analysisen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Med mindre det står noe annet, er denne innførselens lisens beskrevet som Attribution 4.0 International (CC BY 4.0)