Optical Fiber Pre-warning System Research Articles

An Improved High-Intelligence Method of Gas and Oil Pipeline Prewarning System in Real Soil Environment

With the development of technology, the total extent of global pipeline transportation is also increased. However, the traditional long-distance optical fiber prewarning system has poor real-time performance and high false alarm rate when recognizing events threatening pipeline safety. The same vibration signal would vary greatly when collected in different soil environments and this problem would reduce the signal recognition accuracy of the prewarning system. In this paper, we studied this effect theoretically and analyzed soil vibration signals under different soil conditions. Then we studied the signal acquisition problem of long-distance gas and oil pipeline prewarning system in real soil environment. Ultimately, an improved high-intelligence method was proposed for optimization. This method is based on the real application environment, which is more suitable for the recognition of optical fiber vibration signals. Through experiments, the method yielded high recognition accuracy of above 95%. The results indicate that the method can significantly improve signal acquisition and recognition and has good adaptability and real-time performance in the real soil environment.

Research on the adaptability of an improved high-intelligence long-distance optical fiber pre-warning system

With the development of technology, the total extent of global pipeline transportation is also increased each year. Nevertheless, owing to the wide distribution of gas and oil pipelines and the complex laying environment, the traditional long-distance optical fiber pre-warning system (OFPS) has a high false alarm rate when recognizing events threatening the pipeline safety, and it is difficult to define the type of intrusion events. Therefore, an improved high-intelligence long-distance OFPS was proposed, and the generalized adaptability of the system in various environments was studied. Φ-optical time-domain reflectometry technology was used in the distributed sensing part of the system, and a neural network (NN) was used in the signal recognition part to identify and classify intrusion events. Three methods were used in this system including an improved NN, a wavelet packet decomposition-based artificial NN, and a five-layer deep NN. Finally, through experiments in these three methods, the adaptability of the system was explored. The results show that the system possesses an excellent classification effect in the recognition of intrusion events with an average recognition rate reaching over 95%. Thus, it has good adaptability under various real environmental circumstances.

A Novel Fiber Intrusion Signal Recognition Method for OFPS Based on SCN With Dropout

Accurate and fast recognition of fiber intrusion signals has always been a fundamental task in the Optical Fiber Pre-warning System (OFPS). However, currently existing recognition models tend to focus on one aspect and lack a comprehensive approach. In this paper, a dropout-based Stochastic Configuration Network (SCN) optical fiber intrusion signal recognition model is first proposed, which is named DropoutSCN. By combining dropout with randomized algorithm models, it not only enhances the fast learning ability, but also improves the generalization performance of the recognition model. In the experiment, compared with traditional Artificial Neural Network (ANN), Random Vector Functional Link (RVFL), and original SCN models, the DropoutSCN model proposed in this paper has the lowest root-mean-square error (RMSE). In terms of time efficiency, it reduces the time delay by about 2.5 times compared with the traditional ANN. In addition, this paper applies dropout to SCN, which provides a feasible thinking and reference for the study of randomized algorithm models.

AdaBoost-SCN algorithm for optical fiber vibration signal recognition.

Recognition of different types of fiber vibration signals is important for the optical fiber prewarning system (OFPS). Nowadays, the recognition of fiber vibration signals with neural networks is one of the common methods in this field. As a small, well-trained network, stochastic configuration networks (SCNs) can achieve good results when applied to fiber vibration signal recognition. However, in the case of a limited number of vibration signals, the recognition rate of SCNs is also limited. In order to improve the recognition rate of vibration signals, this paper proposes the AdaBoost-SCN algorithm. It integrates different SCNs as base classifiers in AdaBoost. The experiments show that the testing accuracy of the AdaBoost-SCN algorithm is 12.1% higher than that of the original SCN when training with a small vibration signal set. The algorithm proposed in this paper not only increases the recognition rate of fiber vibration signals, but also improves the generalization ability of the original SCN in the case of a limited number of vibration signal samples.

Purification of mixed optical fiber intrusion signal by simplex volume analysis

Optical fiber intrusion signal detection is an effective long range technology for perimeter intrusion behavior perception. The optical fiber pre-warning system (OFPS) can detect and recognize many kinds of intrusion signals. However, the current detection-recognition process does not have the purification ability for mixed signals, since the conventional system lacks the unmixing process. The first problem in the unmixing process is how to extract the pure intrusion signal. For this problem, by studying the power spectrum characteristics of intrusion signal, we introduce the statistical volume as the statistical characteristics of intrusion signal in the high dimension space and put forward a novel simplex volume analysis purification method. In the high-dimension space, the mixed signal is located in a simplex composed of the characteristics of the pure signal. By finding the maximum of simplex volume, we can obtain the pure signal of simplex. Furthermore, in order to reduce the interference of outliers as much as possible, we introduce a subspace projection mechanism to remove the outliers. The real data experiments verify the effectiveness of this algorithm as well as good robustness to the noise.

Intrusion signal classification using stochastic configuration network with variable increments of hidden nodes

In the environmental security monitoring application, an optical fiber prewarning system (OFPS) functions not only to locate the intrusion events but also recognize them. As a nonlinear network for recognition, the stochastic configuration network (SCN) is considered a promising method because it does not require setting the network scale beforehand. However, in the specific requirements of the application of OFPS, due to the small feature distance of different intrusion signals to be classified, it is necessary to set a smaller value of error tolerance. However, the side-effect is that meeting the constraint condition faces a challenge. To overcome this, we improve the configuration method of the hidden layer nodes in the SCN network. In the proceeding of the network process, the increment of the hidden layer nodes in each loop is gradually increased, and the space of the corresponding random parameters generated is enlarged. The SCN with variable increments of hidden nodes can adjust the number of hidden nodes added in each loop for continuous construction and obtaining higher classification accuracy. This study has a great significance for the application of SCN in the classification of intrusion signals in OFPS.

Optical fiber intrusion signal recognition method based on TSVD-SCN

Abstract Stochastic Configuration Networks (SCN) introduce the inequality constraint of a supervised mechanism to ensure the universal approximation property of learner model. However, in the processing of building SCN, due to the properties of used activation function and the way of assigning the random input weights and biases of the hidden nodes, the hidden output matrix is often ill-posed, i.e., the matrix can be of rank deficient or demonstrate multicollinearity. Thus, the least squares method for evaluating the output weights may result in poor generalization performance for data modelling problems. This paper aims to overcome this drawback through modifying the computation of the generalized pseudo inverse of the output matrix by a Truncated Singular Value Decomposition (TSVD) method with an adaptively chosen truncation threshold. The improved SCN model is then applied for recognizing intrusion signals in Optical Fiber Pre-warning System. Experimental results show that the proposed improved algorithm can achieve higher recognition rate compared to the original SCN classifier.

The Fast Detection and Identification Algorithm of Optical Fiber Intrusion Signals

With the continuous development of optical fiber sensing technology, the Optical Fiber Pre-Warning System (OFPS) has been widely used in various fields. The OFPS identifies the type of intrusion based on the detected vibration signal to monitor the surrounding environment. Aiming at the real-time requirements of OFPS, this paper presents a fast algorithm to accelerate the detection and recognition processing of optical fiber intrusion signals. The algorithm is implemented in an embedded system that is composed of a digital signal processor (DSP). The processing flow is divided into two parts. First, the dislocation processing method is adopted for the sum processing of original signals, which effectively improves the real-time performance. The filtered signals are divided into two parts and are parallel processed by two DSP boards to save time. Then, the data is input into the identification module for feature extraction and classification. Experiments show that the algorithm can effectively detect and identify the optical fiber intrusion signals. At the same time, it accelerates the processing speed and meets the real-time requirements of OFPS for detection and identification.

Optical fiber intrusion signal unmixing by constrained quadratic programming approach

Optical fiber intrusion signal detection is an effective long-distance sensing technology for perimeter intrusion behavior. The existing optical fiber pre-warning system (OFPS) has been able to detect and identify a variety of intrusion behaviors. However, when various intrusion behaviors occur at the same time, identification performance in OFPS will decrease. In this paper, we model the mixed intrusion signal from a geometric perspective at first, and then the unmixing method is studied under the model constraint condition. We set the model error function as an objective function, and transform the unmixing problem into a constrained quadratic programming problem. Through iteration, the pure intrusion signals and their proportion are obtained. This method does not require prior knowledge such as the number of pure components and corresponding characteristics. Compared with the conventional methods, the proposed algorithm can obtain more accurate features under unsupervised conditions.

Study on CA-CFAR Algorithm Based on Normalization Processing of Background Noise for HI of Optical Fiber

Optical fiber pre-warning system (OFPS) is often used to monitor the occurrence of disasters such as the leakage of oil and natural gas pipeline. It analyzes the collected vibration signals to judge whether there is any harmful intrusion (HI) events. At present, the research in this field is mainly focused on the constant false alarm rate (CFAR) methods and derivative algorithms to detect intrusion signals. However, the performance of CFAR is often limited to the actual collected signals distribution. It is found that the background noise usually obeys non-independent and identically distribution (Non-IID) through the statistical analysis of acquisition signals. In view of the actual signal distribution characteristics, this paper presents a CFAR detection method based on the normalization processing for background noise. A high-pass filter is designed for the actual Non-IID background noise data to obtain the characterization characteristic. Then, the background noise is converted to independent and identically distribution (IID) by using the data characteristic. Next, the collected data after normalization is processed with efficient cell average constant false alarm rate (CA-CFAR) method for detection. Finally, the results of experiments both show that the intrusion signals can be effectively detected, and the effectiveness of the algorithm is verified.

RVFL-Based Optical Fiber Intrusion Signal Recognition With Multi-Level Wavelet Decomposition as Feature

The optical fiber pre-warning system (OFPS) has been gradually considered as one of the important means for pipeline safety monitoring. Intrusion signal types are correctly identified which could reduce the cost of troubleshooting and maintenance of the pipeline. Most of the previous feature extraction methods in OFPS are usually quested from the view of time domain. However, in some cases, there is no distinguishing feature in the time domain. In the paper, firstly, the intrusion signal features of the running, digging, and pick mattock are extracted in the frequency domain by multi-level wavelet decomposition, that is, the intrusion signals are decomposed into five bands. Secondly, the average energy ratio of different frequency bands is obtained, which is considered as the feature of each intrusion type. Finally, the feature samples are sent into the random vector functional-link (RVFL) network for training to complete the classification and identification of the signals. Experimental results show that the algorithm can correctly distinguish the different intrusion signals and achieve higher recognition rate.

Mellin Transform-Based Correction Method for Linear Scale Inconsistency of Intrusion Events Identification in OFPS

For the problem that the linear scale of intrusion signals in the optical fiber pre-warning system (OFPS) is inconsistent, this paper presents a method to correct the scale. Firstly, the intrusion signals are intercepted, and an aggregate of the segments with equal length is obtained. Then, the Mellin transform (MT) is applied to convert them into the same scale. The spectral characteristics are obtained by the Fourier transform. Finally, we adopt back-propagation (BP) neural network to identify intrusion types, which takes the spectral characteristics as input. We carried out the field experiments and collected the optical fiber intrusion signals which contain the picking signal, shoveling signal, and running signal. The experimental results show that the proposed algorithm can effectively improve the recognition accuracy of the intrusion signals.

Stress reaction process-based hierarchical recognition algorithm for continuous intrusion events in optical fiber prewarning system

To improve the recognition performance of optical fiber prewarning system (OFPS), this study proposed a hierarchical recognition algorithm (HRA). Compared with traditional methods, which employ only a complex algorithm that includes multiple extracted features and complex classifiers to increase the recognition rate with a considerable decrease in recognition speed, HRA takes advantage of the continuity of intrusion events, thereby creating a staged recognition flow inspired by stress reaction. HRA is expected to achieve high-level recognition accuracy with less time consumption. First, this work analyzed the continuity of intrusion events and then presented the algorithm based on the mechanism of stress reaction. Finally, it verified the time consumption through theoretical analysis and experiments, and the recognition accuracy was obtained through experiments. Experiment results show that the processing speed of HRA is 3.3 times faster than that of a traditional complicated algorithm and has a similar recognition rate of 98%. The study is of great significance to fast intrusion event recognition in OFPS.

Multiband imaging and linear unmixing of optical fiber intrusion signal

Feature extraction of the optical fiber intrusion signal is one of the most basic problems in the optical fiber pre-warning system (OFPS). This paper proposes a spectrum analysis method in the OFPS that adopts the time-frequency analysis technique to realize multiband imaging of the intrusion signal at first. Subsequently, the characteristic spectrum of the intrusion signal can be obtained as the intrusion feature from the imaging results. Since the practical intrusion spectrum is usually a mixture of multiple intrusion types, in order to extract the spectrum corresponding to each intrusion type, the mixed spectrum is characterized by a linear mixing mode. Then, we exploit the nonnegative matrix factorization (NMF) in the mixture analysis. Moreover, we add physical constraints to the NMF and solve it by gradient descent algorithm. Finally, we extract the characteristic spectrums from the mixed spectrum resulting to the spectrum unmixing. The real data experiments verify the effectiveness of this method.

An energy ratio feature extraction method for optical fiber vibration signal

The intrusion events in the optical fiber pre-warning system (OFPS) are divided into two types which are harmful intrusion event and harmless interference event. At present, the signal feature extraction methods of these two types of events are usually designed from the view of the time domain. However, the differences of time-domain characteristics for different harmful intrusion events are not obvious, which cannot reflect the diversity of them in detail. We find that the spectrum distribution of different intrusion signals has obvious differences. For this reason, the intrusion signal is transformed into the frequency domain. In this paper, an energy ratio feature extraction method of harmful intrusion event is drawn on. Firstly, the intrusion signals are pre-processed and the power spectral density (PSD) is calculated. Then, the energy ratio of different frequency bands is calculated, and the corresponding feature vector of each type of intrusion event is further formed. The linear discriminant analysis (LDA) classifier is used to identify the harmful intrusion events in the paper. Experimental results show that the algorithm improves the recognition rate of the intrusion signal, and further verifies the feasibility and validity of the algorithm.

Detection and recognition of mechanical, digging and vehicle signals in the optical fiber pre-warning system

This paper presents detection and recognition method to locate and identify harmful intrusions in the optical fiber pre-warning system (OFPS). Inspired by visual attention architecture (VAA), the process flow is divided into two parts, i.e., data-driven process and task-driven process. At first, data-driven process takes all the measurements collected by the system as input signals, which is handled by detection method to locate the harmful intrusion in both spatial domain and time domain. Then, these detected intrusion signals are taken over by task-driven process. Specifically, we get pitch period (PP) and duty cycle (DC) of the intrusion signals to identify the mechanical and manual digging (MD) intrusions respectively. For the passing vehicle (PV) intrusions, their strong low frequency component can be used as good feature. In generally, since the harmful intrusion signals only account for a small part of whole measurements, the data-driven process reduces the amount of input data for subsequent task-driven process considerably. Furthermore, the task-driven process determines the harmful intrusions orderly according to their severity, which makes a priority mechanism for the system as well as targeted processing for different harmful intrusion. At last, real experiments are performed to validate the effectiveness of this method.

Intrusion signal recognition in OFPS under multi-level wavelet decomposition based on RVFL neural network

The performance of the optical fiber pre-warning system (OFPS) is susceptible to the interference of background noise, especially in the low signal-to-noise ratio (SNR) environment. In this paper, a recognition method based on the multi-level wavelet decomposition is proposed to accurately identify the running and digging intrusion signals in OFPS under the low SNR condition. The method includes the cross-correlation operation, feature extraction, and recognition using a random vector functional-link (RVFL) neural network (NN). Firstly, the collected signals are processed by the cross-correlation operation. Compared with the conventional filtering method, the background noise can be suppressed to the greatest extent by the cross-correlation operation, which keeps the signal details as much as possible. Secondly, the cross-correlation functions obtained from the cross-correlation operation are decomposed into five levels by the multi-level wavelet decomposition. And then the average energy ratios can be obtained along with the decomposed levels, and we select these ratios in the five frequency bands as the feature of intrusion signals. Next, the feature samples are sent into the RVFL NN for training so as to complete the recognition of these intrusion signals. Finally, the effectiveness of the algorithm is verified by the field experiment.

Harmful intrusion detection algorithm of optical fiber pre-warning system based on correlation of orthogonal polarization signals

At present, advanced researches of optical fiber intrusion measurement are based on the constant false alarm rate (CFAR) algorithm. Although these conventional methods overcome the interference of non-stationary random signals, there are still a large number of false alarms in practical applications. This is because there is no specific study on orthogonal polarization signals of false alarm and intrusion. In order to further reduce false alarms, we analyze the correlation of optical fiber signals using birefringence of single-mode fiber. This paper proposes the harmful intrusion detection algorithm based on the correlation of two orthogonal polarization signals. The proposed method uses correlation coefficient to distinguish false alarms and intrusions, which can decrease false alarms. Experiments on real data, which are collected from the practical environment, demonstrate that the difference in correlation is a robust feature. Furthermore, the results show that the proposed algorithm can reduce the false alarms and ensure the detection performance when it is used in optical fiber pre-warning system (OFPS).

A new detection and recognition method for optical fiber pre-warning system

Optical fiber pre-warning system (OFPS) is widely used in several fields. But there are massive vibration data which is varied in complicated surroundings of OFPS. This will be a challenge to locate and identify the vibrations accurately. A new detection and recognition method is established in this article. The method consists of two parts, detection and recognition. First, the presented detection method is based on the theory of constant false alarm rate (CFAR) and dilation and erosion (DE). The former can detect the harmful intrusion signals and the latter can eliminated some isolated interferences. Harmful intrusions can be located and the data quantity for further recognition is reduced by using the detection method. Second, a multi-feature recognition method is established in this article to determine the type of the intrusions. Typical signal features, such as average magnitude difference function (AMDF), pitch period (PP) and duty cycle (DC), are used to identify the vibrations generated by vehicles, machine and artificial intrusions. In order to check out the feasibility and validity of the proposed method, several tests were carried out in Rushan of Shan Dong, China. The results show that the proposed detection and recognition method can locate the harmful intrusions and identify the type of vibrations effectively.

A harmful-intrusion detection method based on background reconstruction and two-dimensional K-S test in an optical fiber pre-warning system

The key technology and main difficulty for optical fiber intrusion pre-warning systems (OFIPS) is the extraction of harmful-intrusion signals. After being processed by a phase-sensitive optical time-domain reflectometer (Φ-OTDR), vibration signals can be preliminarily extracted. Generally, these include noises and intrusions. Here, intrusions can be divided into harmful and harmless intrusions. With respect to the close study of signal characteristics, an effective extraction method of harmful intrusion is proposed in the paper. Firstly, in the part of the background reconstruction, all intrusion signals are first detected by a constant false alarm rate (CFAR). We then reconstruct the backgrounds by extracting two-part information of alarm points, time and amplitude. This ensures that the detection background consists of intrusion signals. Secondly, in the part of the two-dimensional Kolmogorov-Smirnov (K-S) test, in order to extract harmful ones from all extracted intrusions, we design a separation method. It is based on the signal characteristics of harmful intrusion, which are shorter time interval and higher amplitude. In the actual OFIPS, the detection method is used in some typical scenes, which includes a lot of harmless intrusions, for example construction sites and busy roads. Results show that we can effectively extract harmful intrusions.