Research output per year
Research output per year
Abstract
This important book offers a foundation for use of artificial intelligence (AI) in fault diagnosis and classification for centrifugal pumps. It outlines methods for operators to identify and classify faults that are not easily detectable using traditional techniques like time domain and frequency domain methods. It brings together different AI approaches that are integrated with Wavelet Transform (WT) and Genetic Algorithm (GA) into a single reference—making advanced diagnostic methods accessible to engineers who may not have a computer science background.
Covering a wide range of AI applications for mechanical systems, the book:
• Advances readers from description of the problem to application of AI methods for diagnostics
• Outlines ways for operators to identify and classify faults that are not easily detectable using traditional techniques
• Offers new data from a novel experimental rig not previously available
Covering a wide range of AI applications for mechanical systems, the book:
• Advances readers from description of the problem to application of AI methods for diagnostics
• Outlines ways for operators to identify and classify faults that are not easily detectable using traditional techniques
• Offers new data from a novel experimental rig not previously available
| Original language | English |
|---|---|
| Publisher | American Institute of Physics |
| Number of pages | 302 |
| ISBN (Electronic) | 9780735423596 |
| ISBN (Print) | 9780735423565 |
| DOIs | |
| Publication status | Published - Jul 2022 |
Documents and Links
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Dive into the research topics of 'Artificial Intelligence Methods for Fault Diagnosis in Centrifugal Pumps'. Together they form a unique fingerprint.-
Using MLP-GABP and SVM with wavelet packet transform-based feature extraction for fault diagnosis of a centrifugal pump
Al Tobi, M., Bevan, G., Wallace, P., Harrison, D. & Okedu, K. E., Jun 2022, In: Energy Science & Engineering. 10, 6, p. 1826-1839 14 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile8 Citations (Scopus)157 Downloads (Pure) -
Faults diagnosis of a centrifugal pump using multilayer perceptron genetic algorithm back propagation and support vector machine with discrete wavelet transform based feature extraction
Al Tobi, M., Bevan, G., Wallace, P., Harrison, D. & Okedo, K. E., Feb 2021, In: Computational Intelligence. 37, 1, p. 21-46 26 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile18 Citations (Scopus)270 Downloads (Pure) -
Centrifugal pump condition monitoring and diagnosis using frequency domain analysis
Ali Saud Al Tobi, M., Bevan, G., Wallace, P., Harrison, D. & Ramachandran, K. P., Jan 2019, Advances in Condition Monitoring of Machinery in Non-Stationary Operations: Proceedings of the Sixth International Conference on Condition Monitoring of Machinery in Non-Stationary Operations, CMMNO’2018, 20-22 June, 2018, Santander, Spain. Fernandez Del Rincon, A., Viadero Rueda, F., Chaari, F., Zimroz, R. & Haddar, M. (eds.). 1st ed. Springer Nature, p. 122-131 10 p. (Applied Condition Monitoring; vol. 15).Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review
6 Citations (Scopus)