In this article, a novel vibration-based damage detection approach is proposed based on selecting effective cepstral coefficients, consisting of three main stages: (1) signal processing and feature extraction, (2) damage detection by combining effective cepstral coefficients through feature selection methods, and (3) performance evaluation. First, two feature extraction techniques are used in damage identification systems, including linear prediction cepstral coefficients and mel frequency cepstral coefficients. Second, to improve the performance of damage detection, the combination of the effective cepstral coefficients is proposed as a damage index. By applying several feature selection methods, the most effective coefficients are found and then combined to create a subset that carries the most significant information about the structural damage. Finally, the support vector machine classifier is performed to evaluate the proposed approach in detecting the structural damage. The proposed technique is verified using a suite of numerical and full-scale studies. Results confirm that the proposed method achieves a significant performance with great accuracy and reduces false alarms.

本文在选择有效倒谱系数的基础上，提出了一种基于振动的损伤检测新方法，包括三个主要阶段：（1）信号处理和特征提取；（2）通过特征选择方法结合有效倒谱系数进行损伤检测。 ，以及（3）绩效评估。首先，在损伤识别系统中使用了两种特征提取技术，包括线性预测倒谱系数和梅尔频率倒谱系数。其次，为了提高损伤检测的性能，提出了有效倒谱系数的组合作为损伤指标。通过应用几种特征选择方法，可以找到最有效的系数，然后将其组合以创建一个子集，该子集携带有关结构损伤的最重要信息。最后，执行支持向量机分类器以评估所提出的检测结构损伤的方法。所提出的技术已使用一组数值和全面研究进行了验证。结果证实了所提出的方法具有很高的准确性，并且减少了误报。