A Hybrid Particle Swarm Optimization-Based Wavelet Threshold Denoising Algorithm for Acoustic Emission Signals
Acoustic emission (AE) as a non-destructive monitoring method is used to identify small damage in various materials effectively. However, AE signals acquired during the monitoring of oil and gas steel pipelines are always contaminated with noise. A noisy signal can be a threat to the reliability and...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/14/6/1253 |
| _version_ | 1797481910696411136 |
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| author | Farrukh Hassan Lukman Ab. Rahim Ahmad Kamil Mahmood Saad Adnan Abed |
| author_facet | Farrukh Hassan Lukman Ab. Rahim Ahmad Kamil Mahmood Saad Adnan Abed |
| author_sort | Farrukh Hassan |
| collection | DOAJ |
| description | Acoustic emission (AE) as a non-destructive monitoring method is used to identify small damage in various materials effectively. However, AE signals acquired during the monitoring of oil and gas steel pipelines are always contaminated with noise. A noisy signal can be a threat to the reliability and accuracy of the findings. To address these shortcomings, this study offers a technique based on discrete wavelet transform to eliminate noise in these signals. The denoising performance is affected by several factors, including wavelet basis function, decomposition level, thresholding method, and the threshold selection criteria. Traditional threshold selection rules rely on statistical and empirical variables, which influence their performance in noise reduction under various conditions. To obtain the global best solution, a threshold selection approach is proposed by integrating particle swarm optimization and the late acceptance hill-climbing heuristic algorithms. By comparing five common approaches, the superiority of the suggested technique was validated by simulation results. The enhanced thresholding solution based on particle swarm optimization algorithm outperformed others in terms of signal-to-noise ratio and root-mean-square error of denoised AE signals, implying that it is more effective for the detection of AE sources in oil and gas steel pipelines. |
| first_indexed | 2024-03-09T22:21:19Z |
| format | Article |
| id | doaj.art-88d8e6f20a6549b8a93c4512aa56907c |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-09T22:21:19Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-88d8e6f20a6549b8a93c4512aa56907c2023-11-23T19:13:22ZengMDPI AGSymmetry2073-89942022-06-01146125310.3390/sym14061253A Hybrid Particle Swarm Optimization-Based Wavelet Threshold Denoising Algorithm for Acoustic Emission SignalsFarrukh Hassan0Lukman Ab. Rahim1Ahmad Kamil Mahmood2Saad Adnan Abed3Department of Computer and Information Sciences, High Performance Cloud Computing Centre (HPC3), Universiti Teknologi PETRONAS, Seri Iskandar 32610, MalaysiaDepartment of Computer and Information Sciences, High Performance Cloud Computing Centre (HPC3), Universiti Teknologi PETRONAS, Seri Iskandar 32610, MalaysiaInterventure Tech, 4 Laluan Tronoh 9, Desa Tronoh, Tronoh 31750, MalaysiaCollege of Medicine, University of Fallujah, Fallujah 31002, IraqAcoustic emission (AE) as a non-destructive monitoring method is used to identify small damage in various materials effectively. However, AE signals acquired during the monitoring of oil and gas steel pipelines are always contaminated with noise. A noisy signal can be a threat to the reliability and accuracy of the findings. To address these shortcomings, this study offers a technique based on discrete wavelet transform to eliminate noise in these signals. The denoising performance is affected by several factors, including wavelet basis function, decomposition level, thresholding method, and the threshold selection criteria. Traditional threshold selection rules rely on statistical and empirical variables, which influence their performance in noise reduction under various conditions. To obtain the global best solution, a threshold selection approach is proposed by integrating particle swarm optimization and the late acceptance hill-climbing heuristic algorithms. By comparing five common approaches, the superiority of the suggested technique was validated by simulation results. The enhanced thresholding solution based on particle swarm optimization algorithm outperformed others in terms of signal-to-noise ratio and root-mean-square error of denoised AE signals, implying that it is more effective for the detection of AE sources in oil and gas steel pipelines.https://www.mdpi.com/2073-8994/14/6/1253discrete wavelet transformacoustic emissionparticle swarm optimizationlocal searchgenetic algorithmlate acceptance hill climbing |
| spellingShingle | Farrukh Hassan Lukman Ab. Rahim Ahmad Kamil Mahmood Saad Adnan Abed A Hybrid Particle Swarm Optimization-Based Wavelet Threshold Denoising Algorithm for Acoustic Emission Signals Symmetry discrete wavelet transform acoustic emission particle swarm optimization local search genetic algorithm late acceptance hill climbing |
| title | A Hybrid Particle Swarm Optimization-Based Wavelet Threshold Denoising Algorithm for Acoustic Emission Signals |
| title_full | A Hybrid Particle Swarm Optimization-Based Wavelet Threshold Denoising Algorithm for Acoustic Emission Signals |
| title_fullStr | A Hybrid Particle Swarm Optimization-Based Wavelet Threshold Denoising Algorithm for Acoustic Emission Signals |
| title_full_unstemmed | A Hybrid Particle Swarm Optimization-Based Wavelet Threshold Denoising Algorithm for Acoustic Emission Signals |
| title_short | A Hybrid Particle Swarm Optimization-Based Wavelet Threshold Denoising Algorithm for Acoustic Emission Signals |
| title_sort | hybrid particle swarm optimization based wavelet threshold denoising algorithm for acoustic emission signals |
| topic | discrete wavelet transform acoustic emission particle swarm optimization local search genetic algorithm late acceptance hill climbing |
| url | https://www.mdpi.com/2073-8994/14/6/1253 |
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