Fiber Optic Vibration Signal Recognition Based on IMFCC and CDET

In this article, a novel technique that aims at improving the signal recognition effectiveness and real-time performance of distributed optical fiber sensing vibration systems is reported. To this end, a procedure is developed based on the improved Mel-frequency cepstral coefficient (IMFCC), compensation distance estimation technology (CDET), and multiclassification support vector machine (SVM). The IMFCC is first used in the feature extraction stage. The algorithm optimizes the Mel filter according to the energy ratio to extract the characteristics of the optical fiber vibration signal. Then, the proposed method seeks to formulate a feature optimization strategy by calculating the ratio of the feature inter-class distance to the intraclass distance. The CDET removes redundant data in feature extraction and reduces feature dimension. The performance of the proposed method is tested on an experimental example of the optical fiber sensing vibration system. Moreover, the proposed method is compared against a MFCC-based method. The results demonstrate the superiority of the proposed method in long-term condition monitoring. It improves the classification effectiveness of optical fiber vibration signals and improves the real-time performance through the feature dimension reduction method, which can be widely used in optical fiber vibration signal recognition systems.