Real-Time Fault Detection Application for Natural Gas Pipelines

2012 ◽  
Author(s):  
Melitsa J. Torres ◽  
Jose D. Posada ◽  
Jaime R. Garcia ◽  
Marco E. Sanjuan
Keyword(s):  
Natural Gas ◽  
Fault Detection ◽  
Real Time ◽  
Principal Component ◽  
Simulation Software ◽  
Third Party ◽  
Normal Operation ◽  
Operation Condition ◽  
Detection Techniques ◽  
Operation Conditions

The implementation of fault detection techniques in industrial systems for process monitoring has proven to be a useful tool to process operators supervising the plant’s operation conditions. As plants become more instrumented, more data is available for fault detection applications, if they are capable of demonstrate anticipation and low false alarm rates. A regional Natural Gas transportation system deals with these types of drawbacks. While the improvements are carried out, some effort should be done in order to improve the safety in operations. In this paper a data-driven technique was used to detect fault conditions along the pipeline, sectioning it in five partitions to increase the detection sensibility. To overcome the lack of quality in data, simulation software intended to gas controllers training and pipeline operation was used to simulate leaks scenarios. Some historic data with high quality is also used to create normal operation condition models by means of Principal Component Analysis. All simulated faults were detected in a reduced time gap and recent events related to third-party actions showed the tool proficiency to detecting faults in real time. In addition, it considers a fault normalized index per section indicating the fault persistence and aggressiveness in a single plot.

scholarly journals ExtSim: A Flexible Data Mapping and Synchronization Middleware for Scientific Visualization in Virtual Worlds

2010 ◽  
Vol 2 (5) ◽  
Author(s):  
Johan Berntsson ◽  
Norman Lin ◽  
Zoltan Dezso
Keyword(s):  
Protein Folding ◽  
Real Time ◽  
Virtual Worlds ◽  
Scientific Computing ◽  
General Purpose ◽  
Current Approach ◽  
Simulation Software ◽  
Third Party ◽  
Data Mapping ◽  
Future Work

In this paper we present a general-purpose middleware, called ExtSim that allows OpenSim to communicate with external simulation software, and to synchronize the in-world representation of the simulator state. We briefly present two projects in ScienceSim where ExtSim has been used; Galaxsee which is an interactive real-time N-body simulation, and a protein folding demonstration, before discussing the merits and problems with the current approach. The main limitation is that we until now only have been limited to a third-party viewer, and a fixed server-client protocol, but we present our work on a new viewer, called 3Di Viewer “Rei”, which opens new possibilities in enhancing both performance and richness of the visualization suitable for scientific computing,. Finally we discuss some ideas we are currently studying for future work.

A Comparative Assessment on Static and Dynamic PCA for Fault Detection in Natural Gas Transmission Systems

2017 ◽  
Author(s):  
Horacio Pinzón ◽  
Cinthia Audivet ◽  
Melitsa Torres ◽  
Javier Alexander ◽  
Marco Sanjuán
Keyword(s):  
Natural Gas ◽  
Fault Detection ◽  
Large Scale ◽  
Detection System ◽  
Principal Component ◽  
Data Driven ◽  
Transmission Systems ◽  
Alarm Management ◽  
Detection Algorithms ◽  
Natural Gas Transmission

Sustainability of natural gas transmission infrastructure is highly related to the system’s ability to decrease emissions due to ruptures or leaks. Although traditionally such detection relies in alarm management system and operator’s expertise, given the system’s nature as large-scale, complex, and with vast amount of information available, such alarm generation is better suited for a fault detection system based on data-driven techniques. This would allow operators and engineers to have a better framework to address the online data being gathered. This paper presents an assessment on multiple fault-case scenarios in critical infrastructure using two different data-driven based fault detection algorithms: Principal component analysis (PCA) and its dynamic variation (DPCA). Both strategies are assessed under fault scenarios related to natural gas transmission systems including pipeline leakage due to structural failure and flow interruption due to emergency valve shut down. Performance evaluation of fault detection algorithms is carried out based on false alarm rate, detection time and misdetection rate. The development of modern alarm management frameworks would have a significant contribution in natural gas transmission systems’ safety, reliability and sustainability.

scholarly journals A Power Dissipation Monitoring Circuit for Intrusion Detection and Botnet Prevention on IoT Devices

Computation ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 19
Author(s):  
Dimitrios Myridakis ◽  
Paul Myridakis ◽  
Athanasios Kakarountas
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Low Cost ◽  
International Standards ◽  
Normal Operation ◽  
Smart Devices ◽  
Smart Device ◽  
Operation Conditions ◽  
Ip Camera ◽  
In Series

Recently, there has been a sharp increase in the production of smart devices and related networks, and consequently the Internet of Things. One concern for these devices, which is constantly becoming more critical, is their protection against attacks due to their heterogeneity and the absence of international standards to achieve this goal. Thus, these devices are becoming vulnerable, with many of them not even showing any signs of malfunction or suspicious behavior. The aim of the present work is to introduce a circuit that is connected in series with the power supply of a smart device, specifically an IP camera, which allows analysis of its behavior. The detection circuit operates in real time (real-time detection), sampling the supply current of the device, processing the sampled values and finally indicating any detection of abnormal activities, based on a comparison to normal operation conditions. By utilizing techniques borrowed by simple power analysis side channel attack, it was possible to detect deviations from the expected operation of the IP camera, as they occurred due to intentional attacks, quarantining the monitored device from the rest of the network. The circuit is analyzed and a low-cost implementation (under 5US$) is illustrated. It achieved 100% success in the test results, showing excellent performance in intrusion detection.

Fault Detection of a Water Loop Refrigerating System Using Multivariate Statistics

2010 ◽  
Author(s):  
Jose´ Posada ◽  
Jose´ Solano ◽  
Marco Sanjuan
Keyword(s):  
Fault Detection ◽  
Energy Management ◽  
Prediction Error ◽  
Historical Data ◽  
Principal Component ◽  
Multivariate Statistical ◽  
Detection Techniques ◽  
Statistical Measures ◽  
Large Water ◽  
Centrifugal Chiller

This paper describes the use of statistical fault detection techniques to improve energy management in a large water loop refrigerating system (WLRS). The water loop has one centrifugal chiller with specified capacity of 420TR in parallel with another screw chiller with specified capacity of 420 TR, feeding several buildings to control the temperature inside them through condenser units. Multivariate statistical fault detection techniques such as Principal Component Analysis (PCA) were used, in order to analyze the historical data from the system to detect abnormal situations, by means of statistical measures such as Square Prediction Error (SPE) and T2., Finally, using this expertise of plant engineers were used to determine the fault causes, and results will be used to prevent future abnormal conditions.

Dynamic Classification: A Tool for Fault Detection and Vibration Behavior Monitoring of Bearings

2013 ◽  
Author(s):  
Sanaa Kerroumi ◽  
Lanto Rasolofondraibe ◽  
Xavier Chiementin
Keyword(s):  
Fault Detection ◽  
Real Time ◽  
Principal Component ◽  
Processing Technique ◽  
Health Condition ◽  
Classification Method ◽  
Diagnostic Process ◽  
Signal Processing Technique ◽  
Spectral Kurtosis ◽  
Over Time

The emergence of a defect in a mechanical system is always associated with a change in the vibration’s behavior in the spectral and temporal domains. Fault detection by vibration analysis is based on monitoring the behavior of a mechanical component by examining the evolution of fault indicators in real time. However, mere traditional bearing diagnosis is not sufficient to ensure effective and reliable assessment of the component’s health condition. Coupling several fault indicators extracted by different signal processing technique adds more reliability and accuracy to the diagnostic process. Classifications methods are used to analyze the evolution of fault, yet only static methods are solicited which results in overlooking a great source of useful information. In fact, fault indicators issued from turning machine are evolving; they change constantly over time, particularly when the defect is growing. In such situations, static classification methods are a poor choice that deprives the user of the information conveyed in the evolution of the indicators over time. The dynamic classification of fault indicators in dynamic classes can provide useful information about the behavior of the damaged bearings. This information can also be used to predict the end of life of the components. Unlike the static classification, the dynamic classification introduces a new dimension (time), which allows real-time detection of the fault and better visibility of the bearing behavior revealed by the motion and the temporal evolution of classes formed by the indicators. This paper proposes a dynamic classification method that uses several fault detectors to assure the accuracy of the diagnostic and follow up any changing in the behavior of the bearing by analyzing the classes’ time evolution. The proposed multi-features dynamic classification is a new method for fault detection and health condition monitoring for bearings; this technique utilizes multiple features, including traditional features extracted from the raw signal, two special features extracted by wavelet analysis, and the spectral kurtosis, coupled with a nonlinear principal component analysis. This method of classification clusters the multi-features into several classes, the first class represents the healthy state of the bearing, and the other classes represent different damaging state. Monitoring the evolution of the” defective condition” class allows us to draw several useful information, such as the rate of degradation, the relationship between the cluster’ surface and density and the bearing state. The chosen dynamic classification method will be validated by analyzing several degradation bearings from a fatigue bench of thrust ball bearings SNR51207.

Application of Data Transmission Capability on Natural Gas Transmission Pipelines

2002 ◽  
Author(s):  
Karen A. Moore ◽  
Robert Carrington ◽  
John Richardson ◽  
Ray A. Zatorski
Keyword(s):  
Natural Gas ◽  
Real Time ◽  
Porous Metal ◽  
Third Party ◽  
Natural Gas Pipeline ◽  
Natural Gas Transmission ◽  
Unique Manner ◽  
Transmission Capability ◽  
Pipeline Failures ◽  
Thermally Sprayed

Third party damage is as a significant factor in natural gas pipeline failures. 75% of pipeline failures induced by a third party occur immediately following impact. Current inspection techniques are both labor intensive and expensive to implement and they represent only a point in time status. The objective is for a near real time detection and communication system that utilizes the pipe itself, i.e., a “smart pipe”. The industry is calling for an inexpensive rugged process that transmits the state of the pipe. The INEEL is developing a near real time damage detection and location system that utilizes resistive traces applied to the wall of the pipe, which detects damage by measuring the strain state of the pipeline. This data will also allow for efficient repairs and emergency response. The technique employed is a network of thermally sprayed resistive traces deposited on either the interior or exterior wall of the pipeline. The ability of a thermally sprayed resistive trace to detect damage is due to the unique manner in which a porous metal changes resistivity when placed under strain.

scholarly journals Online Fault Detection of Fixed-Wing UAV Based on DKPCA Algorithm with Multiple Operation Conditions Considered

2020 ◽  
Vol 38 (3) ◽  
pp. 619-626
Author(s):  
Shaojun Liang ◽  
Shirong Zhang ◽  
Xing Zheng ◽  
Dongsheng Lin
Keyword(s):  
Fault Detection ◽  
Measurement Errors ◽  
Wavelet Denoising ◽  
Operation Condition ◽  
Data Set ◽  
Control Loops ◽  
Operation Conditions ◽  
Fault Monitoring ◽  
Online Matching ◽  
Multiple Operation

The mission execution process of a fixed-wing UAV has multiple phases and multiple operation conditions. Its parameters are nonlinear and dynamic. These characteristics make its online fault detection rather complicated. To carry out the fault detection, this paper selects nine key parameters of the transverse, longitudinal and velocity control loops of the UAV to characterize its real-time conditions. The core parameters are dynamically preprocessed to construct an augmented matrix so as to describe the dynamic characteristics of the UAV. Then, the improved k-mediods* algorithm is used to cluster the operation conditions of the UAVs by means of augmented dimensions. Neural networks are used to achieve the online matching of operation conditions. To overcome the nonlinearity of the UAV, the fault detection is performed by using the DKPCA algorithm; the fault monitoring is conducted through constructing the compound indexes of SPE and T2, notated as FAI. Furthermore, the fault separation algorithm is proposed to specify the variables of fault from the augmented high-dimensional data set. In order to deal with the erroneous reporting of faults due to measurement errors, the paper conducts the wavelet denoising of FAI, the compound indexes of the DKPCA algorithm. Finally, the data set collected from a real UAV flight is used to verify the effectiveness of the DKPCA algorithm for operation condition clustering and matching, fault detection and wavelet denoising.

Fault detection of damper in railway vehicle suspension based on the cross-correlation analysis of bogie accelerations

2019 ◽  
Vol 20 (1) ◽  
pp. 102 ◽  
Author(s):  
Mădălina Dumitriu
Keyword(s):  
Fault Detection ◽  
Numerical Simulations ◽  
Cross Correlation ◽  
Fault Detection And Isolation ◽  
Condition Based Maintenance ◽  
Normal Operation ◽  
Railway Vehicle ◽  
Operation Condition ◽  
Cross Correlation Analysis ◽  
The Cross

Nowadays, the condition-based maintenance is associated more and more with railway transport to improve the safety, availability, reliability and capacity of this transport system, and to reduce life cycle costs for the railway vehicles. The condition-based maintenance requires that vehicle components are replaced based on their real condition, which implies the fault detection and isolation during the train's operation. The paper proposes a method to detect the failure of the damper in the primary suspension of the rail vehicle, based on the analysis of cross-correlation of the vertical accelerations measured on the bogie frame against the two axles. The numerical simulations and experimental results show a very good correlation between the bogie accelerations when the dampers are in a normal operation condition. This thing is shown based on the values of the cross-correlation coefficient (CCC) of the bogie accelerations. The failure in a damper can be detected by the decrease of the CCC of the bogie accelerations, a confirmed fact in the results derived from numerical simulations. The proposed method has more advantages, namely, it is a signal-based method and hence does not require a complex mathematical modelling of the vehicle-track system and knowledge of its parameters or of the external conditions; the method makes relative comparisons between measurements and hence reduces the effect of the factors that influence outputs; the method can be also extended for the secondary suspension; the method can be easily implemented on any type of bogie.

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