scholarly journals A data driven health assessment model for high pressure output pumps in LNG terminals

2020 ◽  
Vol 446 ◽  
pp. 042064
Author(s):  
Yuyun Zeng ◽  
Xiaoshang Wang ◽  
Guangyao Xie ◽  
Jingquan Liu
Keyword(s):  
High Pressure ◽  
Health Assessment ◽  
Assessment Model ◽  
Data Driven ◽  
Pressure Output

Hybrid Model-Based and Data-Driven Diagnostic Algorithm for Gas Turbine Engines

2020 ◽  
Author(s):  
Amare Fentaye ◽  
Valentina Zaccaria ◽  
Moksadur Rahman ◽  
Mikael Stenfelt ◽  
Konstantinos Kyprianidis
Keyword(s):  
High Pressure ◽  
Measurement Uncertainty ◽  
Gas Turbine ◽  
Diagnostic Algorithm ◽  
Data Driven ◽  
Intermediate Pressure ◽  
Path Component ◽  
Fault Diagnostic ◽  
Component Faults ◽  
Pressure Compressor

Abstract Data-driven algorithms require large and comprehensive training samples in order to provide reliable diagnostic solutions. However, in many gas turbine applications, it is hard to find fault data due to proprietary and liability issues. Operational data samples obtained from end-users through collaboration projects do not represent fault conditions sufficiently and are not labeled either. Conversely, model-based methods have some accuracy deficiencies due to measurement uncertainty and model smearing effects when the number of gas path components to be assessed is large. The present paper integrates physics-based and data-driven approaches aiming to overcome this limitation. In the proposed method, an adaptive gas path analysis (AGPA) is used to correct measurement data against the ambient condition variations and normalize. Fault signatures drawn from the AGPA are used to assess the health status of the case engine through a Bayesian network (BN) based fault diagnostic algorithm. The performance of the proposed technique is evaluated based on five different gas path component faults of a three-shaft turbofan engine, namely intermediate-pressure compressor fouling (IPCF), high-pressure compressor fouling (HPCF), high-pressure turbine erosion (HPTE), intermediate-pressure turbine erosion (IPTE), and low-pressure turbine erosion (LPTE). Robustness of the method under measurement uncertainty has also been tested using noise-contaminated data. Moreover, the fault diagnostic effectiveness of the BN algorithm on different number and type of measurements is also examined based on three different sensor groups. The test results verify the effectiveness of the proposed method to diagnose single gas path component faults correctly even under a significant noise level and different instrumentation suites. This enables to accommodate measurement suite inconsistencies between engines of the same type. The proposed method can further be used to support the gas turbine maintenance decision-making process when coupled with overall Engine Health Management (EHM) systems.

Modeling the Principles of Community-Based Participatory Research in a Community Health Assessment Conducted by a Health Foundation

2009 ◽  
Vol 10 (1) ◽  
pp. 67-75 ◽  
Author(s):  
Karen Jaynes Williams ◽  
Patricia Gail Bray ◽  
Carrie K. Shapiro-Mendoza ◽  
Ilana Reisz ◽  
Jane Peranteau
Keyword(s):  
Community Health ◽  
Participatory Research ◽  
Health Assessment ◽  
Community Based Participatory Research ◽  
Local Area ◽  
Lessons Learned ◽  
Assessment Model ◽  
Community Based ◽  
Community Health Assessment ◽  
Practical Applications

The authors discuss strategies used and lessons learned by a health foundation during development of a community health assessment model incorporating community-based participatory research (CBPR) approaches. The assessment model comprises three models incorporating increasing amounts of CPBR principles. Model A combines local-area analysis of quantitative data, qualitative information (key informants, focus groups), and asset mapping. Model B, a community-based participatory model, emphasizes participatory rural appraisal approaches and quantitative assessment using rapid epidemiological assessment. Model C, a modified version of Model B, is financially more sustainable for our needs than Model B. The authors (a) describe origins of these models and illustrate practical applications and (b) explore the lessons learned in their transition from a traditional, nonparticipatory, quantitative approach to participatory approaches to community-health assessment. It is hoped that this article will contribute to the growing body of knowledge of practical aspects of incorporating CBPR approaches into community health assessments.

Risk Management at TransCanada Pipelines

1998 ◽  
Author(s):  
Kevin Cicansky ◽  
Glenn Yuen
Keyword(s):  
Risk Assessment ◽  
Risk Management ◽  
High Pressure ◽  
Natural Gas ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Gas Pipelines ◽  
Natural Gas Pipelines

This Paper presents the method TransCanada PipeLines uses to assess the integrity risks with respect to operating its high pressure natural gas pipelines. TransCanada PipeLines’ experiences, results and successes gained through the implementation of its risk program, TRPRAM (TransCanada Pipelines Risk Assessment Model) are highlighted.

scholarly journals Psychological Health Assessment Model of Enterprise Employees Based on DNN Technology

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Xin Li
Keyword(s):  
Network Model ◽  
Psychological Health ◽  
Health Assessment ◽  
Health Indicators ◽  
Assessment Model ◽  
Economic Market ◽  
Factors Affecting ◽  
Test Items ◽  
Leading Position ◽  
Complex Test

All enterprises gradually recognise the importance of employees’ healthy psychology to business activities in order to improve their own economic level and occupy a certain leading position in the economic market. The main factors affecting employees’ psychological health are used as input samples in this paper, and a network model of enterprise employees’ psychological health prediction based on DNN is developed. To form a specific set, the psychological health indicators are separated from the complex test items. The key influencing factors in psychological health assessment are chosen as input vectors, and the DNN algorithm’s output results are obtained, analysed, and compared. Following sample training, the artificial NN’s error between predicted and measured values is only 3.55 percent, achieving the desired effect. The DNN principle is used in this paper to create a mathematical prediction network model based on an analysis of psychological factors affecting employees in businesses. The calculation of the final result of the prediction system is simple and flexible when the parameters of the NN are changed, and the network model’s prediction efficiency and accuracy are greatly improved.

scholarly journals A Review of Data-Driven Oil and Gas Pipeline Pitting Corrosion Growth Models Applicable for Prognostic and Health Management

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Roohollah Heidary ◽  
Steven A. Gabriel ◽  
Mohammad Modarres ◽  
Katrina M. Groth ◽  
Nader Vahdati
Keyword(s):  
Stochastic Process ◽  
Pitting Corrosion ◽  
Oil And Gas ◽  
Health Management ◽  
Assessment Model ◽  
Data Driven ◽  
Gas Pipelines ◽  
Oil And Gas Pipelines ◽  
Process Based Models ◽  
Prognostic And Health Management

Pitting corrosion is a primary and most severe failure mechanism of oil and gas pipelines. To implement a prognostic and health management (PHM) for oil and gas pipelines corroded by internal pitting, an appropriate degradation model is required. An appropriate and highly reliable pitting corrosion degradation assessment model should consider, in addition to epistemic uncertainty, the temporal aspects, the spatial heterogeneity, and inspection errors. It should also take into account the two well-known characteristics of pitting corrosion growing behavior: depth and time dependency of pit growth rate. Analysis of these different levels of uncertainties in the amount of corrosion damage over time should be performed for continuous and failure-free operation of the pipelines. This paper reviews some of the leading probabilistic data-driven prediction models for PHM analysis for oil and gas pipelines corroded by internal pitting. These models categorized as random variable-based and stochastic process-based models are reviewed and the appropriateness of each category is discussed. Since stochastic process-based models are more versatile to predict the behavior of internal pitting corrosion in oil and gas pipelines, the capabilities of the two popular stochastic process-based models, Markov process-based and gamma process-based, are discussed in more detail.

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