A Fiber Vibration Signal Recognition Method Based on CNN-CBAM-LSTM

Abstract

By trying to solve the issue of identifying multiple types of intrusion vibration signals collected by distributed vibrating fiber optic sensors, this study investigates the signal identification and feature extraction of intrusion signals, and proposes an optical fiber vibration signal (OFVS) identification method based on deep learning. The external vibration signal is collected by the Sagnac fiber optic interferometer, and then denoised by spectral subtraction. Endpoint detection is carried out by combining the short-time logarithmic energy method and the spectral entropy method. Finally, the equal-length signal containing valid information is intercepted and the corresponding preprocessing is carried out. The method for feature processing incorporates the strong feature learning capability of the long-short-term memory (LSTM) and the great short-term feature extraction capability of the convolutional neural network (CNN). At the same time, to further enhance the signal feature identification, a convolutional block attention module (CBAM) is introduced to perform adaptive feature refinement on the signal. In summary, a network model combining CNN, LSTM, and CBAM is proposed to process the signal features, and finally, the multi-layer perceptron (MLP) is used to complete the task of classification and recognition of multi-type intrusion signals. The experimental findings indicate that the OFVS method of CNN-CBAM-LSTM can effectively identify four kinds of OFVS, and the overall average recognition accuracy reaches 97.9%. Walking and knocking signals among them are recognized with over 99% accuracy.

Recognition of Fiber Optic Vibration Signals Based on Laplace Wavelet Transform and Deep Learning

“…Method 3: Ref. [13] combines a 1D CNN, a long short‐term memory (LSTM) network, and a convolutional block attention module (CBAM) to recognize and classify the original signal directly. Method 4: Ref.…”

“…Huang et al . [13] introduced a method for recognizing optical fiber vibration signals based on CNN‐CBAM‐LSTM, achieving superior recognition results by leveraging Long Short‐Term Memory (LSTM) and Convolutional Attention Module (CBAM). This method reflects the trend of cross‐pollination among various algorithms used to recognize fiber optic vibration signals.…”

LSTM-Autoencoder for Vibration Anomaly Detection in Vertical Carousel Storage and Retrieval System (VCSRS)

“…The equation for the LSTM model can be defined using the mathematical expression below: where describes the input of the LSTM architecture cell, , , and represent the hidden states and cell states of the architecture which are documented in several related theories [ 54 , 55 , 56 , 57 ].…”

Feature fusion-based fiber-optic distributed acoustic sensing signal identification method

“…To further verify the CDIL-CBAM-BiLSTM network model proposed, we referred to four typical performance indexes of accuracy, precision, recall, and F1-score and compared the proposed model with 1D CNN [20,27], CNN-CBAM-LSTM [28], AF-CNN [21], and 1D CNN + DenseNet [29], which processing 1D signals in the field of fiber distributed sensing signal recognition methods. Tables 4 and 5 show, in detail, the architectural details of the 1D CNN and CDIL-CBAM-BiLSTM, including the size of the input and output, size of the convolutional kernel, step size of the different layers, and padding size.…”