Phase-sensitive optical time-domain reflectometry (ϕ-OTDR) is a powerful sensing device that enables fully distributed optical fiber vibration detection with fast response and high sensitivity. Based on the introduction of its principles, Rayleigh backscatter, sensing mechanism, the characteristics of the ϕ-OTDR system are analyzed in detail. Some gaps in the ϕ-OTDR technology are outlined, and its development trends are highlighted. The application of its current technology is illustrated in different fields such as the perimeter security monitoring system, submarine cable surveillance, and so on. In the experiments analysis, it was pointed out that the device is very sensitive, which is subject to a high rate of false alarms; it also has unresolved types of disturbances. For these purposes, this paper first summarizes the development status of the ϕ-OTDR-based fully distributed optical fiber sensing device. Then, it analyzes and proposes the use of a Printed Circuit Board (PCB) winding flyback converter with shielding, to solve the problem of sensitivity. The shielding method has been proven to significantly reduce high frequency noise up to 30 MHz. This will enable relevant industries and future researchers to know the current state of the ϕ-OTDR device.

Submarine cables are the main sources of power supply for oil rigs; in this regard, they must be assessed and effectively secured. Monitoring of the environment of submarine cables is rare, and cable faults generally require interventions. This article develops the detection and recognition of shock events on submarine cables using the vibration signals of its optical fiber sensor. The vibration signals from the optical fiber are detected by means of phase-sensitive optical time-domain reflectometry (V-OTDR). An effective fiber vibration pattern recognition scheme based on the time-frequency-domain characteristics (TFDC) and Support vector machine (SVM) is proposed. The scheme uses the TFDC for the recognition of the fiber vibrations, and the SVM to determine the magnitude of the event. Three different groups of data are collected and processed in real-time. Experimental results from multiple tests carried out on data samples show that the proposed method is valid and reliable for recognizing and determining the magnitude of events on the submarine cable.

A phase-sensitive optical time-domain reflectometry with a detection distance of 10 km is used in this paper to recognize and to detect in real time events along the perimeter security monitoring system. To optimize the signal processing in the software system and achieve the design of distributed optical fiber vibration signal of the perimeter security monitoring system, a two-level vibration pattern recognition scheme based on the power spectral estimation is proposed. The monitoring system software of the proposed algorithm is designed and implemented, as well as the functional modules involved in the design of the software system. The experimental tests of the whole system show that the proposed algorithm can effectively identify in real time any intrusion event along the sensing cable, with an intrusion detection rate greater than 95%, including false alarms. Also, the corresponding monitoring system software can display the data collected in real time, with a processing time of around 1.6 s, which proves its reliability, thereby ensuring the system’s validity.

To detect and recognize any type of events over the perimeter security system, this article proposes a fiber-optic vibration pattern recognition method based on the combination of time-domain features and time-frequency domain features. The performance parameters (event recognition, event location, and event classification) are very important and describe the validity of this article. The pattern recognition method is precisely based on the empirical mode decomposition of time-frequency entropy and center-of-gravity frequency. It implements the function of identifying and classifying the event (intrusions or non-intrusion) over the perimeter to secure. To achieve this method, the first-level prejudgment is performed according to the time-domain features of the vibration signal, and the second-level prediction is carried out through time-frequency analysis. The time-frequency distribution of the signal is obtained by empirical mode decomposition and Hilbert transform and then the time-frequency entropy and center-of-gravity frequency are used to form the time-frequency domain features, that is, combined with the time-domain features to form feature vectors. Multiple types of probabilistic neural networks are identified to determine whether there are intrusions and the intrusion types. The experimental results demonstrate that the proposed method is effective and reliable in identifying and classifying the type of event.

One of the important successes of optical fiber sensor established for the security system is the detection and the recognition of any type of events. The performance parameters (event recognition, event detection position, and time of detection) are unavoidable and describe the validity of any perimeter detection system. An event recognition is any signal detected within the protected area, and it is related to a non-intrusion event and an intrusion event. To achieve the detection and the recognition events at the real time, an effective two-level vibration recognition method and a technique are proposed and presented in this article. The signal characteristics (short-term energy and short-time over-threshold) have been used and compared to the dynamic threshold to judge the type of event. Then the extraction of the power distribution features on the frequency domain through power spectral estimation on the suspected intrusion signal samples is carried out and finally combined with the time-domain characteristics as feature vector through Support Vector Machine to determine the efficiency and effectiveness of the proposed vibration recognition method. The experimental simulation results show that the proposed method is effective and reliable. With collected data, it can detect and recognize the type of event in real time.

The vibration signal is essential for accurate event recognition along submarine cables. However, rapid and concise identification still poses challenges for prompt recognition and classification of events in harsh environments. For such reasons, this article proposes a developed hybrid model combining the modern power spectrum estimation (MPSE) and improved gradient neural networks (IGNNs), to identify and organize events instantaneously. Three different categories of data are selected as data samples, collected using the phase-sensitive optical time-domain reflectometry ( $\Phi $ -OTDR) device. The signal characteristics of the power distribution are extracted in the frequency and time domains. These characteristics together form the eigenvector, used in a multiclass IGNN classifier, to initiate the extent and position of collision events on the cable. The results demonstrate that the proposed hybrid model has a better identification rate, compared to the existing schemes used for the same purposes. It is verified that the accuracy rate of identification has expanded by almost 1%; confirming the validity and reliability of the proposed hybrid model.