An Extended Implementation of Fault Detection in Multi-State Systems Based on Warp Analysis: A Case Study on Natural Gas Transmission Systems in Tropical Regions

2017 ◽  
Author(s):  
Horacio Pinzón ◽  
Cinthia Audivet ◽  
Ivan Portnoy ◽  
Marlon Consuegra ◽  
Javier Alexander ◽  
...  
Keyword(s):  
Natural Gas ◽  
Fault Detection ◽  
Large Scale ◽  
Detection Algorithm ◽  
Operating Conditions ◽  
Underlying Structure ◽  
Transmission Systems ◽  
Original Algorithm ◽  
Tropical Conditions ◽  
Natural Gas Transmission

Natural gas transmission infrastructure is a large-scale complex system often exhibiting a considerable operating states not only due to natural, slow and normal process changes related to aging but also to a dynamic interaction with multiple agents overall having different functional parameters, an irregular demand trend adjusted by the hour, and sometimes affected by external conditions as severe climate periods. As traditional fault detection relies in alarm management system and operator’s expertise, it is paramount to deploy a strategy being able to update its underlying structure and effectively adapting to such process shifts. This feature would allow operators and engineers to have a better framework to address the online data being gathered in dynamic on transient conditions. This paper presents an extended analysis on WARP technique to address the abnormal condition management activities of multiple-state processes deployed in critical natural gas transmission infrastructure. Special emphasis is made on the updating activity to incorporate effectively the operating shifts exhibited by a new operating condition implemented on a fault detection strategy. This analysis broadens the authors’ original algorithm scope to include multi-state systems in addition to process drifting behavior. The strategy is assessed under two different scenarios rendering a major shift in process’ operating conditions related to natural gas transmission systems: A transition between low and high natural gas demand to support hydroelectric generation matrix on severe tropical conditions. Performance evaluation of fault detection algorithm is carried out based on false alarm rate, detection time and misdetection rate estimated around the model update.

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.

Experimental Results of a PEM System Operated on Hydrogen and Reformate

2008 ◽  
Vol 5 (1) ◽  
Author(s):  
M. Minutillo ◽  
E. Jannelli ◽  
F. Tunzio
Keyword(s):  
Fuel Cell ◽  
Natural Gas ◽  
Large Scale ◽  
Pem Fuel Cell ◽  
Proton Exchange ◽  
Operating Conditions ◽  
Cell Performance ◽  
Pure Hydrogen ◽  
Test Bed ◽  
Fuel Cell Performance

The main objective of this study is to evaluate the performance of a proton exchange membrane (PEM) fuel cell generator operating for residential applications. The fuel cell performance has been evaluated using the test bed of the University of Cassino. The experimental activity has been focused to evaluate the performance in different operating conditions: stack temperature, feeding mode, and fuel composition. In order to use PEM fuel cell technology on a large scale, for an electric power distributed generation, it could be necessary to feed fuel cells with conventional fuel, such as natural gas, to generate hydrogen in situ because currently the infrastructure for the distribution of hydrogen is almost nonexistent. Therefore, the fuel cell performance has been evaluated both using pure hydrogen and reformate gas produced by a natural gas reforming system.

scholarly journals Fault Detection for the Scraper Chain Based on Vibration Analysis Using the Adaptive Optimal Kernel Time-Frequency Representation

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Xing Zhang ◽  
Wei Li ◽  
Zhencai Zhu ◽  
Shanguo Yang ◽  
Fan Jiang
Keyword(s):  
Fault Detection ◽  
Vibration Analysis ◽  
Large Scale ◽  
Experimental Testing ◽  
Operating Conditions ◽  
Mining Equipment ◽  
Time Frequency ◽  
Failure Patterns ◽  
Frequency Representation ◽  
Optimal Kernel

A scraper conveyor is a key component of large-scale mechanized coal mining equipment, and its failure patterns are mainly caused by chain jam and chain fracture. Due to the difficulties with direct measurement for multiple performance parameters of the scraper chain, this paper deals with a novel strategy for fault detection of the scraper chain based on vibration analysis of the chute. First, a chute vibration model (CVM) is applied for modal analysis, and the hammer impact test (HIT) is conducted to validate the accuracy of the CVM; second, the measuring points for vibration analysis of the chute are determined based on the modal assurance criterion (MAC); and third, to simulate the actual vibration properties of the chute, a dynamic transmission system model (DTSM) is constructed based on finite element modeling. The fixed-point experimental testing (FPET) is then conducted to indicate the correctness of simulation results. Subsequently, the DTSM-based vibration responses of the chute under different operating conditions are obtained. In this paper, the proposed strategy is employed to determine the occurrence of chain faults by amplitude comparisons, while failure patterns are distinguished by the adaptive optimal kernel time-frequency representation (AOKR).

scholarly journals Natural Gas Liquids Extraction Rights in Alberta: An Evolving Landscape

2010 ◽  
pp. 347-386
Author(s):  
Dave Guichon ◽  
Bernette Ho ◽  
Robert Froehlich
Keyword(s):  
Natural Gas ◽  
Background Information ◽  
Pipeline System ◽  
Transmission Systems ◽  
The Future ◽  
Natural Gas Transmission ◽  
The Common ◽  
The 1960S ◽  
Over Time

Alberta’s natural gas liquids (NGLs) industry commenced development in the 1960s and, with the support of the Alberta government, expanded rapidly in the subsequent decades. Over time each of the major natural gas transmission systems in Alberta developed its own protocol in respect of NGL extraction entitlement and procedures. In the case of the NOVA Gas Transmission Ltd. (NGTL) pipeline system, such a protocol was developed by way of convention, and has never been formalized in the NGTL tariff. On several occasions the Alberta Energy and Utilities Board (EUB), and its predecessors, examined the issue of NGL ownership and associated extraction, but significant issues remained. In 2007, the EUB undertook an inquiry regarding matters relating to NGL ownership and extraction from the common stream of natural gas that flows through EUB regulated transmission systems and facilities. The EUB’s decision in this respect was released in February 2009. This article provides background information on the NGL extraction industry, outlines the regulatory history relating to NGL ownership and extraction, reviews the decision released by the EUB following the inquiry, considers related jurisdictional questions raised while the inquiry was ongoing, and considers the future of NGL ownership and extraction rights within the province of Alberta.

Abstract P152: Health Informatics: A New Hope for Familial Hypercholesterolemia

Circulation ◽  
2018 ◽  
Vol 137 (suppl_1) ◽  
Author(s):  
Amy P Joseph ◽  
Charles J Mullett ◽  
Matthew Armistead ◽  
Jeff Cox ◽  
Michael Denney ◽  
...  
Keyword(s):  
Familial Hypercholesterolemia ◽  
Language Processing ◽  
Large Scale ◽  
Discrete Data ◽  
Detection Algorithm ◽  
Screening Process ◽  
Record Keeping ◽  
Original Algorithm ◽  
Data Elements ◽  
The U.S

Introduction: Electronic Health Records (EHRs) benefit record keeping, information collation, error prevention, and charge capture. They provide a large database of clinical information that can be used for research. Sorting vast amounts of data manually is inefficient, hence, an effectual, validated method is required to uncover information from large sets of data and generate knowledge. The U.S., and especially West Virginia, has a tremendous burden of cardiovascular disease (CVD). Undiagnosed Familial Hypercholesterolemia (FH) is an important factor for CVD in the U.S. FH results in elevated levels of LDL from childhood and early atherosclerotic disease. We are interested in better screening processes for FH. One method is to detect adults with coronary artery disease (CAD) and determine if their lipid levels are indicative of FH. Relatives and children can then be screened for FH and treated. Efficient identification of a CAD phenotype from EHRs is an important initial step in this screening process. Hypothesis: We hypothesized that a CAD phenotype detection algorithm that uses discrete data elements from EHRs can be validated as a precursor to detection of FH. Methods: We developed an algorithm to detect a CAD phenotype, which searched through discrete data elements, such as diagnoses lists (ICD-10) and procedure (CPT) codes. Direct inspection of EHR discrete data avoided the need for artificial intelligence, such as natural language processing. The algorithm was applied to a cohort of 1,000 patients with varying characteristics. We then determined which patients had CAD by systematically going through EHRs. Following this, we revised the algorithm by refining the constraints under which it operated. We ran the algorithm again on the same 1,000 patients, and determined the accuracy of the modified algorithm. Results: Manual validation of the 1,000 patients resulted in 413 with CAD and 587 without. The original algorithm distinguished 488 CAD positive patients and 512 CAD negative patients. This was 89% accurate, 96% sensitive, and 85% specific. After revising the algorithm and applying it to the same cohort, it determined that there were 474 CAD patients and 526 without CAD. This was 93% accurate, 99% sensitive, and 89% specific. Conclusion: EHR usage has created a large pool of minable clinical data. However, without an efficient method to obtain inferences from it, the information cannot be effectually utilized. We have created an algorithm that detects CAD on a large scale with high accuracy. It has proven to be useful among a varied patient population. Since the constraints that are used, such as ICD codes and CPT codes, are universal, it can be utilized across many hospital systems; although, local validation is prudent. Using this algorithm can select a population with a propensity for FH, thereby allowing us to screen and manage patients with undiagnosed FH or other familial dyslipidemias.

Automotive Wire Control Steering Sensor Fault Detection Algorithm Research

2015 ◽  
Vol 727-728 ◽  
pp. 708-711
Author(s):  
Zhi Ping Liu
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Large Scale ◽  
Structural Changes ◽  
Detection Algorithm ◽  
Steering System ◽  
Steering Wheel ◽  
Detection Accuracy ◽  
Sensor Fault ◽  
Sensor Fault Detection

This article to cancel after the mechanical connections between steering wheel and steering, wire control steering system security and reliability problems, put forward on the basis of the analytical redundancy software sensor method of wire control steering system. In order to solve the compared with the traditional steering system in terms of reliability and safety of the problems of structural changes, the wire control steering system of the main sensor fault diagnosis methods are studied. In wire control steering system associated with the vehicle dynamics model is established under the premise of hypothesis testing to double adaptive fading Kalman filtering technology as a platform, combined with according to the working state of each sensors to determine fault feature vector, to build the main sensor wire control steering automobile fault diagnosis method of residual error threshold. For fault diagnosis of automobile EPS sensor, the BP neural network is put forward to EPS sensor for auto are introduced in the fault diagnosis. For large-scale wireless sensor networks (WSN), reduce the fault detection accuracy, and larger load of communication problems, according to the spatial and temporal correlation characteristics of sensor nodes, proposes a distributed sensor fault detection algorithm based on cluster. These algorithms for sensor fault detection is of great significance.

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