Sound Sensing: Generative and Discriminant Model-Based Approaches to Bolt Loosening Detection
The detection of bolt looseness is crucial to ensure the integrity and safety of bolted connection structures. Percussion-based bolt looseness detection provides a simple and cost-effective approach. However, this method has some inherent shortcomings that limit its application. For example, it high...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/19/6447 |
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| author | Liehai Cheng Zhenli Zhang Giuseppe Lacidogna Xiao Wang Mutian Jia Zhitao Liu |
| author_facet | Liehai Cheng Zhenli Zhang Giuseppe Lacidogna Xiao Wang Mutian Jia Zhitao Liu |
| author_sort | Liehai Cheng |
| collection | DOAJ |
| description | The detection of bolt looseness is crucial to ensure the integrity and safety of bolted connection structures. Percussion-based bolt looseness detection provides a simple and cost-effective approach. However, this method has some inherent shortcomings that limit its application. For example, it highly depends on the inspector’s hearing and experience and is more easily affected by ambient noise. In this article, a whole set of signal processing procedures are proposed and a new kind of damage index vector is constructed to strengthen the reliability and robustness of this method. Firstly, a series of audio signal preprocessing algorithms including denoising, segmenting, and smooth filtering are performed in the raw audio signal. Then, the cumulative energy entropy (CEE) and mel frequency cepstrum coefficients (MFCCs) are utilized to extract damage index vectors, which are used as input vectors for generative and discriminative classifier models (Gaussian discriminant analysis and support vector machine), respectively. Finally, multiple repeated experiments are conducted to verify the effectiveness of the proposed method and its ability to detect the bolt looseness in terms of audio signal. The testing accuracy of the trained model approaches 90% and 96.7% under different combinations of torque levels, respectively. |
| format | Article |
| id | doaj-art-0b52dfd4d30442de8a5dc241f8a655f3 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-0b52dfd4d30442de8a5dc241f8a655f32025-08-20T01:47:37ZengMDPI AGSensors1424-82202024-10-012419644710.3390/s24196447Sound Sensing: Generative and Discriminant Model-Based Approaches to Bolt Loosening DetectionLiehai Cheng0Zhenli Zhang1Giuseppe Lacidogna2Xiao Wang3Mutian Jia4Zhitao Liu5Shandong Electric Power Engineering Consulting Institute Corp., Ltd., Jinan 250013, ChinaShandong Electric Power Engineering Consulting Institute Corp., Ltd., Jinan 250013, ChinaDepartment of Structural, Geotechnical and Building Engineering, Politecnico di Torino, 10129 Torino, ItalySchool of Civil Engineering, Tianjin University, Tianjin 300350, ChinaSchool of Civil Engineering, Tianjin University, Tianjin 300350, ChinaSchool of Civil Engineering, Tianjin University, Tianjin 300350, ChinaThe detection of bolt looseness is crucial to ensure the integrity and safety of bolted connection structures. Percussion-based bolt looseness detection provides a simple and cost-effective approach. However, this method has some inherent shortcomings that limit its application. For example, it highly depends on the inspector’s hearing and experience and is more easily affected by ambient noise. In this article, a whole set of signal processing procedures are proposed and a new kind of damage index vector is constructed to strengthen the reliability and robustness of this method. Firstly, a series of audio signal preprocessing algorithms including denoising, segmenting, and smooth filtering are performed in the raw audio signal. Then, the cumulative energy entropy (CEE) and mel frequency cepstrum coefficients (MFCCs) are utilized to extract damage index vectors, which are used as input vectors for generative and discriminative classifier models (Gaussian discriminant analysis and support vector machine), respectively. Finally, multiple repeated experiments are conducted to verify the effectiveness of the proposed method and its ability to detect the bolt looseness in terms of audio signal. The testing accuracy of the trained model approaches 90% and 96.7% under different combinations of torque levels, respectively.https://www.mdpi.com/1424-8220/24/19/6447bolt looseningmel frequency cepstrum coefficients (MFCCs)cumulative energy entropy (CEE)gaussian discriminant analysis (GDA)support vector machine (SVM) |
| spellingShingle | Liehai Cheng Zhenli Zhang Giuseppe Lacidogna Xiao Wang Mutian Jia Zhitao Liu Sound Sensing: Generative and Discriminant Model-Based Approaches to Bolt Loosening Detection Sensors bolt loosening mel frequency cepstrum coefficients (MFCCs) cumulative energy entropy (CEE) gaussian discriminant analysis (GDA) support vector machine (SVM) |
| title | Sound Sensing: Generative and Discriminant Model-Based Approaches to Bolt Loosening Detection |
| title_full | Sound Sensing: Generative and Discriminant Model-Based Approaches to Bolt Loosening Detection |
| title_fullStr | Sound Sensing: Generative and Discriminant Model-Based Approaches to Bolt Loosening Detection |
| title_full_unstemmed | Sound Sensing: Generative and Discriminant Model-Based Approaches to Bolt Loosening Detection |
| title_short | Sound Sensing: Generative and Discriminant Model-Based Approaches to Bolt Loosening Detection |
| title_sort | sound sensing generative and discriminant model based approaches to bolt loosening detection |
| topic | bolt loosening mel frequency cepstrum coefficients (MFCCs) cumulative energy entropy (CEE) gaussian discriminant analysis (GDA) support vector machine (SVM) |
| url | https://www.mdpi.com/1424-8220/24/19/6447 |
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