Defect identification in centrifugal pumps based on vulnerable grams and end-to-end deep learning framework
- 6 December 2025
- journal article
- Published by Elsevier in Engineering Applications of Artificial Intelligence
- Vol. 165
Abstract
No abstract availableFunding Information
- Ministry of Trade, Industry and Energy (20023566)
- Ministry of Trade, Industry and Energy (RS-2022-00142509)
This publication has 36 references indexed in Scilit:
- Fault diagnosis of helicopter accessory gearbox under multiple operating conditions based on feature mode decomposition and multi-scale convolutional neural networksApplied Soft Computing, 2025
- A lightweight YOLOv8-based model with Squeeze-and-Excitation Version 2 for crack detection of pipelinesApplied Soft Computing, 2025
- Analysis of transient hydraulic failure mechanisms induced by gate malfunction during shutdown of large axial flow pump systemsEngineering Failure Analysis, 2025
- Pseudo-label guided dual classifier domain adversarial network for unsupervised cross-domain fault diagnosis with small samplesAdvanced Engineering Informatics, 2024
- Multi-stage damage identification method for PC structures based on machine learning driven by piezoelectric singular featureEngineering Failure Analysis, 2024
- Experimental fault diagnosis for known and unseen operating conditions of centrifugal pumps using MSVM and WPT based analysesMeasurement, 2019
- Investigation of effect of pump rotational speed on performance and detection of cavitation within a centrifugal pump using vibration analysisHeliyon, 2019
- Automation of multi-fault diagnosing of centrifugal pumps using multi-class support vector machine with vibration and motor current signals in frequency domainJournal of the Brazilian Society of Mechanical Sciences and Engineering, 2018
- Bearing problems’ effects on the dynamic performance of pumping stationsAlexandria Engineering Journal, 2013
- A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysisApplied Soft Computing, 2012