Fault diagnosis of a centrifugal pump using MLP-GABP and SVM with CWT
- Submitting institution
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Glasgow Caledonian University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 33035469
- Type
- D - Journal article
- DOI
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10.1016/j.jestch.2019.01.005
- Title of journal
- Engineering Science and Technology, an International Journal
- Article number
- -
- First page
- 854
- Volume
- 22
- Issue
- 3
- ISSN
- 2215-0986
- Open access status
- Not compliant
- Month of publication
- January
- Year of publication
- 2019
- URL
-
-
- Supplementary information
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-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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4
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- One of a series of papers comparing different wavelet-based methods for classifying faults in centrifugal pumps, using a purpose-built experimental rig to generate mechanical and hydraulic fault data. The research is the result of long-standing and on-going collaboration between GCU and the National University of Science and Technology in Oman and was motivated by problems that were identified in rotating machines at Petroleum Development Oman (PDO), to find the best techniques to optimise maintenance and reduce inefficiencies. The Research Council in Oman is now funding further investigation into AI machine-fault finding by the author whose doctoral research this was.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -