Real-Time Φ-OTDR Vibration Event Recognition Based on Image Target Detection

An SNR Enhancement Method for Φ-OTDR Vibration Signals Based on the PCA-VSS-NLMS Algorithm

To improve the signal-to-noise ratio (SNR) of vibration signals in a phase-sensitive optical time-domain reflectometer (Φ-OTDR) system, a principal component analysis variable step-size normalized least mean square (PCA-VSS-NLMS) denoising method was proposed in this study. First, the mathematical principle of the PCA-VSS-NLMS algorithm was constructed. This algorithm can adjust the input signal to achieve the best filter effect. Second, the effectiveness of the algorithm was verified via simulation, and the simulation results show that compared with the wavelet denoising (WD), Wiener filtering, variational mode decomposition (VMD), and variable step-size normalized least mean square (VSS-NLMS) algorithms, the PCA-VSS-NLMS algorithm can improve the SNR to 30.68 dB when the initial SNR is -1.23 dB. Finally, the PCA-VSS-NLMS algorithm was embedded into the built Φ-OTDR system, an 11.22 km fiber was measured, and PZT was added at 10.19-10.24 km to impose multiple sets of fixed-frequency disturbances. The experimental results show that the SNR of the vibration signal is 8.77 dB at 100 Hz and 0.07 s, and the SNR is improved to 26.17 dB after PCA-VSS-NLMS filtering; thus, the SNR is improved by 17.40 dB. This method can improve the SNR of the system's position information without the need to change the existing hardware conditions, and it provides a new scheme for the detection and recognition of long-distance vibration signals.

Intelligent water perimeter security event recognition based on NAM-MAE and distributed optic fiber acoustic sensing system

Distributed optical acoustic sensing (DAS) based on phase-sensitive optical time-domain reflectometry can realize the distributed monitoring of multi-point disturbances along an optical fiber, thus making it suitable for water perimeter security applications. However, owing to the complex environment and the production of various noises by the system, continuous and effective recognition of disturbance signals becomes difficult. In this study, we propose a Noise Adaptive Mask-Masked Autoencoders (NAM-MAE) algorithm based on the novel mask mode of a Masked Autoencoders (MAE) and applies it to the intelligent event recognition in DAS. In this method, fewer but more accurate features are fed into the deep learning model for recognition by directly shielding the noise. Taking the fading noise generated by the system as an example, data on water perimeter security events collected in DAS underwater acoustic experiments are used. The NAM-MAE is compared with other models. The results indicate higher training accuracy and higher convergence speed of NAM-MAE than other models. Further, the final test accuracy reaches 96.6134%. It can be demonstrated that the proposed method has feasibility and superiority.

Pattern recognition using self-reference feature extraction for φ-OTDR

This paper proposes a pattern recognition method for φ-OTDR based on self-reference features, where machine learning is applied to classify the vibration monitored. The φ-OTDR collects the light amplitude-time-space sequence, establishes a reference position in the spatial dimension, and combines the two dimensions of the vibration and reference positions to form self-reference features, which are then used as machine learning features. These self-reference features can effectively improve the pattern recognition accuracy. This paper selects a low sampling frequency for data collection, analyzes the influence of sample definition methods of different time lengths on the pattern recognition accuracy, and determines that the optimal sample length is 10 data points. The contribution of different feature parameters to pattern recognition is analyzed, and eight eigenvalues such as average, maximum, and minimum are finally determined to form self-reference features that are used as the input of the machine learning algorithm. The recognition accuracies of five machine learning algorithms including kNN, Decision Tree, Random Forest, LightGBM, and CatBoost are analyzed and compared, and the CatBoost algorithm in the integrated learning algorithm is finally determined as the optimal algorithm. On this basis, this paper proposes a filtering algorithm to deal with abnormal signals, which can effectively compensate for abnormal data and further improve the accuracy of pattern recognition. Finally, this paper conducts the pattern recognition study on four common events of tapping, bending, trampling, and blowing, and obtains the average recognition rate of 98%. In addition, this paper innovatively carried out pattern recognition research on five types of mining equipment, including ball mills, vibrating screens, conveyor belts, filters, and industrial pumps, and obtained the average recognition rate of 93.5%.

Optical Fiber Vibration Signal Identification Method Based on Improved YOLOv4

In the traditional peripheral-security-early-warning system, the endpoint detection and pattern recognition of the signals generated by the distributed optical fiber vibration sensors is completed step-by-step and in an orderly manner. The method by which these two processes may be placed end-to-end in a network model and processed simultaneously to improve work efficiency has increasingly become the focus of research. In this paper, the target detection algorithm combines the endpoint-detection and pattern-recognition processes of the vibration signal, which can not only quickly locate the start and end vibration positions of the signal but also accurately identify a certain type of signal. You Only Look Once v4 (YOLOv4) is one of the most advanced target detection algorithms, achieving the optimal balance of speed and accuracy. To reduce the complexity of the YOLOv4 model and solve the dataset's unbalanced sample classification problem, we use a deep separable convolution (DSC) network and a focal loss function to improve the YOLOv4 model. In this paper, the five kinds of signals collected in real-time are visualized as two different datasets in oscillograph and time-frequency diagrams as detection objects. According to the experimental results, we obtained 98.50% and 93.48% mean Average Precision (mAP) and 84.8 and 69.9 frames per second (FPS), respectively, which are improved compared to YOLOv4. Comparing the improved algorithm with other optical fiber vibration signal recognition algorithms, the mAP and FPS values were improved, and the detection speed was about 20 times faster than that of other algorithms. The improved algorithm in this paper can quickly and accurately identify the vibration signal of external intrusion, reduce the false-alarm rate of the early-warning system, and improve the real-time detection rate of the system while ensuring high recognition accuracy.