Sound Sensing: Generative and Discriminant Model-Based Approaches to Bolt Loosening Detection

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 19; p. 6447
• Main Authors: Cheng, Liehai, Zhang, Zhenli, Lacidogna, Giuseppe, Wang, Xiao, Jia, Mutian, Liu, Zhitao
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 05.10.2024; MDPI
• Subjects: Algorithms; bolt loosening; Civil engineering; cumulative energy entropy (CEE); gaussian discriminant analysis (GDA); mel frequency cepstrum coefficients (MFCCs); Methods; Noise; Nondestructive testing; Signal processing; Smart devices; support vector machine (SVM); Time series; mel frequency cepstrum coefficients (MFCCs); bolt loosening; gaussian discriminant analysis (GDA); cumulative energy entropy (CEE); support vector machine (SVM)
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24196447

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.