Quantum mechanics

Frequency domain

Support vector machine

Optical time-domain reflectometer

Optical fiber

Telecommunications

Classifier (UML)

Vibration

Computer science

Fiber optic sensor

Acoustics

Fiber optic splitter

Physics

Pattern recognition (psychology)

Mathematics

Spectrogram

Mathematical analysis

Artificial intelligence

Time domain

Artificial neural network

Feature extraction

Convolutional neural network

Fourier transform

Speech recognition

Computer vision

Optical Engineering

Based on vibration signals detected by a phase-sensitive optical time-domain reflectometer distributed optical fiber sensing system, this paper presents an implement of time-frequency analysis and convolutional neural network (CNN), used to classify different types of vibrational events. First, spectral subtraction and the short-time Fourier transform are used to enhance time-frequency features of vibration signals and transform different types of vibration signals into spectrograms, which are input to the CNN for automatic feature extraction and classification. Finally, by replacing the soft-max layer in the CNN with a multiclass support vector machine, the performance of the classifier is enhanced. Experiments show that after using this method to process 4000 vibration signal samples generated by four different vibration events, namely, digging, walking, vehicles passing, and damaging, the recognition rates of vibration events are over 90%. The experimental results prove that this method can automatically make an effective feature selection and greatly improve the classification accuracy of vibrational events in distributed optical fiber sensing systems.

Pattern recognition based on time-frequency analysis and convolutional neural networks for vibrational events in φ-OTDR