Blade fault localization with the use of vibration signals through artificial neural network: a data-driven approach
Turbines are significant for extracting energy for petrochemical plants, power generation, and aerospace industries. However, it has been reported that turbine-blade failures are the most common causes of machinery breakdown. Therefore, numerous analyses have been performed to formulate techniques f...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
UPM Press
2023
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/38233/ http://umpir.ump.edu.my/id/eprint/38233/1/Blade%20fault%20localization%20with%20the%20use%20of%20vibration%20signals%20through%20artificial%20neural%20network_.pdf |
| _version_ | 1848825456533438464 |
|---|---|
| author | Keng, Ngui Wai Mohd Salman, Leong Mohd Ibrahim, Shapiai Hee, Lim Meng |
| author_facet | Keng, Ngui Wai Mohd Salman, Leong Mohd Ibrahim, Shapiai Hee, Lim Meng |
| author_sort | Keng, Ngui Wai |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Turbines are significant for extracting energy for petrochemical plants, power generation, and aerospace industries. However, it has been reported that turbine-blade failures are the most common causes of machinery breakdown. Therefore, numerous analyses have been performed to formulate techniques for detecting and classifying the fault of the turbine blade. Nevertheless, the blade fault localization method, performed to locate the faulty parts, is equally important for plant operation and maintenance. Therefore, this study will propose a blade fault localization method centered on time-frequency feature extraction and a machine learning approach. The purpose is to locate the faulty parts of the turbine blade. In addition, experimental research is carried out to simulate various blade faults. It includes blade rubbing, blade parts loss, and twisted blade. An artificial neural network model was developed to localize blade fault through the extracted features with newly proposed and selected features. The classification results indicated that the proposed feature set and feature selection method could be used for blade fault localization. It can be seen from the classification rate for blade faultiness localization. |
| first_indexed | 2025-11-15T03:29:13Z |
| format | Article |
| id | ump-38233 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:29:13Z |
| publishDate | 2023 |
| publisher | UPM Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-382332023-09-05T06:55:29Z http://umpir.ump.edu.my/id/eprint/38233/ Blade fault localization with the use of vibration signals through artificial neural network: a data-driven approach Keng, Ngui Wai Mohd Salman, Leong Mohd Ibrahim, Shapiai Hee, Lim Meng T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics Turbines are significant for extracting energy for petrochemical plants, power generation, and aerospace industries. However, it has been reported that turbine-blade failures are the most common causes of machinery breakdown. Therefore, numerous analyses have been performed to formulate techniques for detecting and classifying the fault of the turbine blade. Nevertheless, the blade fault localization method, performed to locate the faulty parts, is equally important for plant operation and maintenance. Therefore, this study will propose a blade fault localization method centered on time-frequency feature extraction and a machine learning approach. The purpose is to locate the faulty parts of the turbine blade. In addition, experimental research is carried out to simulate various blade faults. It includes blade rubbing, blade parts loss, and twisted blade. An artificial neural network model was developed to localize blade fault through the extracted features with newly proposed and selected features. The classification results indicated that the proposed feature set and feature selection method could be used for blade fault localization. It can be seen from the classification rate for blade faultiness localization. UPM Press 2023 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/38233/1/Blade%20fault%20localization%20with%20the%20use%20of%20vibration%20signals%20through%20artificial%20neural%20network_.pdf Keng, Ngui Wai and Mohd Salman, Leong and Mohd Ibrahim, Shapiai and Hee, Lim Meng (2023) Blade fault localization with the use of vibration signals through artificial neural network: a data-driven approach. Pertanika Journal of Science and Technology, 31 (1). pp. 51-68. ISSN 0128-7680. (Published) https://doi.org/10.47836/pjst.31.1.04 https://doi.org/10.47836/pjst.31.1.04 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics Keng, Ngui Wai Mohd Salman, Leong Mohd Ibrahim, Shapiai Hee, Lim Meng Blade fault localization with the use of vibration signals through artificial neural network: a data-driven approach |
| title | Blade fault localization with the use of vibration signals through artificial neural network: a data-driven approach |
| title_full | Blade fault localization with the use of vibration signals through artificial neural network: a data-driven approach |
| title_fullStr | Blade fault localization with the use of vibration signals through artificial neural network: a data-driven approach |
| title_full_unstemmed | Blade fault localization with the use of vibration signals through artificial neural network: a data-driven approach |
| title_short | Blade fault localization with the use of vibration signals through artificial neural network: a data-driven approach |
| title_sort | blade fault localization with the use of vibration signals through artificial neural network: a data-driven approach |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics |
| url | http://umpir.ump.edu.my/id/eprint/38233/ http://umpir.ump.edu.my/id/eprint/38233/ http://umpir.ump.edu.my/id/eprint/38233/ http://umpir.ump.edu.my/id/eprint/38233/1/Blade%20fault%20localization%20with%20the%20use%20of%20vibration%20signals%20through%20artificial%20neural%20network_.pdf |