A novel blade fault diagnosis using a deep learning model based on image and statistical analysis
Artificial intelligence technology has a high potential for machinery fault detection and diagnosis. Blade component failure is the main type of failure that usually occur in gas turbine and this component tends to fail unexpectedly. Detection and diagnosis of blade components are different with gea...
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| Format: | Conference or Workshop Item |
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2022
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| author | Saufi, Mohd. Syahril Ramadhan Isham, M. Firdaus Abu Hassan, M. Danial |
| author_facet | Saufi, Mohd. Syahril Ramadhan Isham, M. Firdaus Abu Hassan, M. Danial |
| author_sort | Saufi, Mohd. Syahril Ramadhan |
| collection | ePrints |
| description | Artificial intelligence technology has a high potential for machinery fault detection and diagnosis. Blade component failure is the main type of failure that usually occur in gas turbine and this component tends to fail unexpectedly. Detection and diagnosis of blade components are different with gear and bearing as both components have a standard vibration analysis and the fault can be examined using frequency domain analysis. Due to the complex structure of the blade system, the informative feature from the vibration signal on the blade fault often obscure with the noise signal. Therefore, this paper proposed a system using a combination of time–frequency image analysis and a stacked sparse autoencoder (SSAE) model to tackle the challenge of blade fault detection and diagnosis. The experiment is carried out using a multi-stage blade system and the result showed that proposed system is able to provide more than 90% diagnosis performance. |
| first_indexed | 2024-03-05T21:16:43Z |
| format | Conference or Workshop Item |
| id | utm.eprints-99353 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T21:16:43Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | utm.eprints-993532023-02-22T08:39:15Z http://eprints.utm.my/99353/ A novel blade fault diagnosis using a deep learning model based on image and statistical analysis Saufi, Mohd. Syahril Ramadhan Isham, M. Firdaus Abu Hassan, M. Danial TJ Mechanical engineering and machinery Artificial intelligence technology has a high potential for machinery fault detection and diagnosis. Blade component failure is the main type of failure that usually occur in gas turbine and this component tends to fail unexpectedly. Detection and diagnosis of blade components are different with gear and bearing as both components have a standard vibration analysis and the fault can be examined using frequency domain analysis. Due to the complex structure of the blade system, the informative feature from the vibration signal on the blade fault often obscure with the noise signal. Therefore, this paper proposed a system using a combination of time–frequency image analysis and a stacked sparse autoencoder (SSAE) model to tackle the challenge of blade fault detection and diagnosis. The experiment is carried out using a multi-stage blade system and the result showed that proposed system is able to provide more than 90% diagnosis performance. 2022 Conference or Workshop Item PeerReviewed Saufi, Mohd. Syahril Ramadhan and Isham, M. Firdaus and Abu Hassan, M. Danial (2022) A novel blade fault diagnosis using a deep learning model based on image and statistical analysis. In: 6th International Conference on Electrical, Control and Computer Engineering, InECCE 2021, 23 August 2021, Kuantan, Pahang. http://dx.doi.org/10.1007/978-981-16-8690-0_100 |
| spellingShingle | TJ Mechanical engineering and machinery Saufi, Mohd. Syahril Ramadhan Isham, M. Firdaus Abu Hassan, M. Danial A novel blade fault diagnosis using a deep learning model based on image and statistical analysis |
| title | A novel blade fault diagnosis using a deep learning model based on image and statistical analysis |
| title_full | A novel blade fault diagnosis using a deep learning model based on image and statistical analysis |
| title_fullStr | A novel blade fault diagnosis using a deep learning model based on image and statistical analysis |
| title_full_unstemmed | A novel blade fault diagnosis using a deep learning model based on image and statistical analysis |
| title_short | A novel blade fault diagnosis using a deep learning model based on image and statistical analysis |
| title_sort | novel blade fault diagnosis using a deep learning model based on image and statistical analysis |
| topic | TJ Mechanical engineering and machinery |
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