A Novel Low-Velocity Impact Region Identification Method for Cantilever Beams Using a Support Vector Machine

• Published in: Mathematical problems in engineering 2022-10, Vol.2022, p.1-10
• Main Authors: Wang, Fengde, Kang, Yongtian, Xiao, Wensheng, Li, Changjiang, Liu, Qi
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial neural networks; Bragg gratings; Cantilever beams; Classification; Comparative studies; Engineering; Feature extraction; Flexibility; Identification methods; Identification systems; Impact damage; Impact response; Lagrange multiplier; Localization; Machine learning; Neural networks; Noise; Optics; Optimization; Parameter estimation; Pattern recognition; Peak frequency; Power spectral density; Reliability engineering; Sensors; Signal processing; Software; Spectral density function; Spectrum allocation; Structural reliability; Structural stability; Support vector machines; Velocity; Wavelet transforms; Weighting methods
• ISSN: 1024-123X; 1563-5147
• DOI: 10.1155/2022/2906535

Damage induced by a low-velocity impact can reduce the stability and reliability of structures. In this study, a novel low-velocity impact region identification method based on the spectral peak frequency (SPF) and support vector machine (SVM) is proposed to identify the low-velocity impact regions on a steel cantilever beam. A low-velocity impact region identification system of the cantilever beam is established by applying fiber Bragg grating (FBG) sensors, and only 2 sensors are used in this system. The power spectral density functions of the impact response signal are smoothed using the linear weighting method to remove pseudospectral peak frequencies, and then, SPFs are extracted as the features. For 25 low-velocity impact regions with dimensions of 30 mm × 10 mm, the results show that the recognition rate obtained by the proposed method is 100% and the feature vector consisting of the first two SPFs with the largest amplitude has the highest recognition rate. Through the comparative study, it is found that the recognition rate of SVM is higher than that of the probabilistic neural network (PNN) and extreme learning machine (ELM) for low-velocity impact area recognition of cantilever beams. As a result, the low-velocity impact region identification method of this paper can be applied to the real-time health monitoring of cantilever beam structures.