Enhancing Acoustic Leak Detection with Data Augmentation: Overcoming Background Noise Challenges
A leak detection method is developed for leaks typically encountered in industrial production. Leaks of 1 mm diameter and less are considered at operating pressures up to 10 bar. The system uses two separate datasets—one for the leak noises and the other for the background noises—both are linked usi...
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MDPI AG
2025-06-01
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| Series: | AI |
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| Online Access: | https://www.mdpi.com/2673-2688/6/7/136 |
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| author | Deniz Quick Jens Denecke Jürgen Schmidt |
| author_facet | Deniz Quick Jens Denecke Jürgen Schmidt |
| author_sort | Deniz Quick |
| collection | DOAJ |
| description | A leak detection method is developed for leaks typically encountered in industrial production. Leaks of 1 mm diameter and less are considered at operating pressures up to 10 bar. The system uses two separate datasets—one for the leak noises and the other for the background noises—both are linked using a developed mixup technique and thus simulate leaks trained in background noises. A specific frequency window between 11 and 20 kHz is utilized to generate a quadratic input for image recognition. With this method, detection accuracies of over 95% with a false alarm rate under 2% can be achieved on a test dataset under the background noises of hydraulic machines in laboratory conditions. |
| format | Article |
| id | doaj-art-469ae9d4d8074fa5bde4ec1c4bf5bd5a |
| institution | Kabale University |
| issn | 2673-2688 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj-art-469ae9d4d8074fa5bde4ec1c4bf5bd5a2025-08-20T03:35:36ZengMDPI AGAI2673-26882025-06-016713610.3390/ai6070136Enhancing Acoustic Leak Detection with Data Augmentation: Overcoming Background Noise ChallengesDeniz Quick0Jens Denecke1Jürgen Schmidt2CSE Center of Safety Excellence (CSE-Institut), 76327 Pfinztal, GermanyCSE Center of Safety Excellence (CSE-Institut), 76327 Pfinztal, GermanyCSE Center of Safety Excellence (CSE-Institut), 76327 Pfinztal, GermanyA leak detection method is developed for leaks typically encountered in industrial production. Leaks of 1 mm diameter and less are considered at operating pressures up to 10 bar. The system uses two separate datasets—one for the leak noises and the other for the background noises—both are linked using a developed mixup technique and thus simulate leaks trained in background noises. A specific frequency window between 11 and 20 kHz is utilized to generate a quadratic input for image recognition. With this method, detection accuracies of over 95% with a false alarm rate under 2% can be achieved on a test dataset under the background noises of hydraulic machines in laboratory conditions.https://www.mdpi.com/2673-2688/6/7/136convolutional neural networksacoustic leak detectionacoustic mixupbackground noiseshort-time Fourier transformationchemical plants |
| spellingShingle | Deniz Quick Jens Denecke Jürgen Schmidt Enhancing Acoustic Leak Detection with Data Augmentation: Overcoming Background Noise Challenges AI convolutional neural networks acoustic leak detection acoustic mixup background noise short-time Fourier transformation chemical plants |
| title | Enhancing Acoustic Leak Detection with Data Augmentation: Overcoming Background Noise Challenges |
| title_full | Enhancing Acoustic Leak Detection with Data Augmentation: Overcoming Background Noise Challenges |
| title_fullStr | Enhancing Acoustic Leak Detection with Data Augmentation: Overcoming Background Noise Challenges |
| title_full_unstemmed | Enhancing Acoustic Leak Detection with Data Augmentation: Overcoming Background Noise Challenges |
| title_short | Enhancing Acoustic Leak Detection with Data Augmentation: Overcoming Background Noise Challenges |
| title_sort | enhancing acoustic leak detection with data augmentation overcoming background noise challenges |
| topic | convolutional neural networks acoustic leak detection acoustic mixup background noise short-time Fourier transformation chemical plants |
| url | https://www.mdpi.com/2673-2688/6/7/136 |
| work_keys_str_mv | AT denizquick enhancingacousticleakdetectionwithdataaugmentationovercomingbackgroundnoisechallenges AT jensdenecke enhancingacousticleakdetectionwithdataaugmentationovercomingbackgroundnoisechallenges AT jurgenschmidt enhancingacousticleakdetectionwithdataaugmentationovercomingbackgroundnoisechallenges |