Vibration-based damage detection using a novel hybrid CNN-SVM approach

In classic machine learning-based damage detection algorithms, extracting damage-sensitive features from time series is a challenging issue. Also, this paradigm can delay processing procedures and requires preprocessing. Many efforts have been made to overcome this limitation by expanding deep learning (DL) in structural health monitoring (SHM). However, because most of these systems require considerable measurements during the training step, they are unsuitable for real-time applications. To solve the challenges above, we offer a robust approach using two-dimensional convolutional neural networks (CNNs) and support vector machines (SVMs), merging feature extraction and a rapid classifier at the same time. The method employs a shallow CNN network that receives raw acceleration signals. Both noisy and noise-free datasets are used to verify the hybrid CNN-SVM approach. The results showed an increase in robustness, speed efficiency, and accuracy over traditional machine learning approaches. The results proved efficient, making the algorithm reliable even under high noise conditions.