scholarly journals Experimental study on leakage monitoring of pressurized water pipeline based on fiber optic hydrophone

2019 ◽  
Vol 19 (8) ◽  
pp. 2347-2358
Author(s):  
Chengchao Guo ◽  
Kunming Shi ◽  
Xuanxuan Chu
Keyword(s):  
Real Time ◽  
Fiber Optic ◽  
Detection System ◽  
Vibration Signal ◽  
Monitoring Method ◽  
Pressurized Water ◽  
Water Pipelines ◽  
Back Calculation ◽  
Water Pipeline ◽  
Leakage Monitoring

Abstract Leakage of water pipelines will significantly endanger the safety operation and service performance of the pipelines. Based on the vibration of pressurized water pipelines deriving from leakage, the BA-FH3200 fiber optic hydrophone (FOH) leakage detection long-term detection system was adopted in prototype tests. The vibration-based real-time leakage monitoring method of the pressurized water pipeline was studied. During the test, the leakage was simulated by opening a spherical valve in the middle of the pipe, and an FOH was placed right above the pipe wall to detect the vibration signal along the pipe. The FOH analysis software was used to monitor the pipeline operation status in real time and acquire data. Then, the data were processed by a self-developed post-processing program, and the parameters were optimized through back-calculation. The test results reveal that the leakage positioning error lay between ±0.07 m, and real-time monitoring (i.e., early warning alarm and leakage positioning) of the FOH for the pressurized water pipeline was feasible.

scholarly journals Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3635 ◽  
Author(s):  
Guoming Zhang ◽  
Xiaoyu Ji ◽  
Yanjie Li ◽  
Wenyuan Xu
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Real Time ◽  
Condition Monitoring ◽  
Resource Constraints ◽  
Detection System ◽  
Stable Operation ◽  
Detection Accuracy ◽  
Monitoring Method ◽  
Dsp Processors

As a critical component in the smart grid, the Distribution Terminal Unit (DTU) dynamically adjusts the running status of the entire smart grid based on the collected electrical parameters to ensure the safe and stable operation of the smart grid. However, as a real-time embedded device, DTU has not only resource constraints but also specific requirements on real-time performance, thus, the traditional anomaly detection method cannot be deployed. To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption changes accordingly. To validate this idea, we set up a testbed based on DTU and simulated four types of imperceptible attacks that change the code running in ARM and DSP processors, respectively. We generate representative features and select lightweight ML algorithms to detect these attacks. We finally implemented the detection system on the windows and ubuntu platform and validated its effectiveness. The results show that the detection accuracy is up to 99.98% in a non-intrusive and lightweight way.

scholarly journals A Fusion of Multiagent Functionalities for Effective Intrusion Detection System

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Dhanalakshmi Krishnan Sadhasivan ◽  
Kannapiran Balasubramanian
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Intrusion Detection System ◽  
Detection System ◽  
Detection Accuracy ◽  
The Real ◽  
Common Path ◽  
Research Areas ◽  
Water Pipeline ◽  
Active Research

Provision of high security is one of the active research areas in the network applications. The failure in the centralized system based on the attacks provides less protection. Besides, the lack of update of new attacks arrival leads to the minimum accuracy of detection. The major focus of this paper is to improve the detection performance through the adaptive update of attacking information to the database. We propose an Adaptive Rule-Based Multiagent Intrusion Detection System (ARMA-IDS) to detect the anomalies in the real-time datasets such as KDD and SCADA. Besides, the feedback loop provides the necessary update of attacks in the database that leads to the improvement in the detection accuracy. The combination of the rules and responsibilities for multiagents effectively detects the anomaly behavior, misuse of response, or relay reports of gas/water pipeline data in KDD and SCADA, respectively. The comparative analysis of the proposed ARMA-IDS with the various existing path mining methods, namely, random forest, JRip, a combination of AdaBoost/JRip, and common path mining on the SCADA dataset conveys that the effectiveness of the proposed ARMA-IDS in the real-time fault monitoring. Moreover, the proposed ARMA-IDS offers the higher detection rate in the SCADA and KDD cup 1999 datasets.

Kedung Keris Full Well Stream Pipeline Fiber Optic Leak Detection System

2021 ◽  
Author(s):  
Cindy Chairunissa ◽  
Deny Kalfarosi Amanu ◽  
Grizki Astari ◽  
Eska Indrayana
Keyword(s):  
Oil And Gas ◽  
Produced Water ◽  
Fiber Optic ◽  
Detection System ◽  
New Technology ◽  
Cost Savings ◽  
Leak Detection ◽  
Oil Field ◽  
Real Time Analysis ◽  
Water Pipeline

Abstract Kedung Keris (KK) is a sour oil field based in Cepu Block, Indonesia. KK field was originally planned to have a processing facility with separate pipelines to deliver crude & produced water, while the gas was planned to be flared. To reduce cost, this concept was changed to a wellpad with full well stream pipeline with new technology of Fiber Optic Leak Detection Sensing System (LDSS) as a key enabler. The fiber optic LDSS functions by leveraging fiber optic cable attached to the pipeline to detect leak as well as intrusion to the pipeline's Right-of-Way through real-time analysis of physical characteristics of a leak and intrusion, such as changes in temperature, pressure, ground strain and acoustics. The implementation of LDSS, together with other safeguards built into the pipeline design, operations and maintenance, allowed the KK Project to eliminate the separation facility at KK wellpad and an additional water pipeline. It also reduces the flaring by billions of standard cubic feet of gas cumulative until end of PSC life as originally all gas planned to be flared. The change of KK Project concept altogether yielded tens of millions of US dollar gross cost savings (~30% of CAPEX + OPEX reduction) following the KK startup in late 2019. The installed LDSS proven to detect leak for up to few meters location accuracy and has intrusion detection capability. KK Project has pioneered the implementation of fiber optic leak detection system for Indonesia oil and gas companies. This work provided further insight to the utilization of such technology in full well stream pipeline where traditional leak detection system implementation will not be acceptable. Consecutively, full well stream pipeline deployment can lead to future CAPEX + OPEX efficiency in facility project design and operation, as well as flaring reduction opportunity.

Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

2020 ◽  
Vol 16 (5) ◽  
pp. 584-591 ◽  
Author(s):  
Muhammad Hanif Ahmad Nizar ◽  
Chow Khuen Chan ◽  
Azira Khalil ◽  
Ahmad Khairuddin Mohamed Yusof ◽  
Khin Wee Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Heart Disease ◽  
Aortic Valve ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Valvular Heart Disease ◽  
Detection System ◽  
Processing Unit ◽  
Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

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