Performance monitoring of heavy duty gas turbines based on Bayesian and Dempster-Shafer theory

2017 ◽  
Author(s):  
Siyamak Afshar Khamseh ◽  
Alireza Fatehi
Keyword(s):  
Gas Turbines ◽  
Performance Monitoring ◽  
Heavy Duty ◽  
Dempster Shafer Theory ◽  
Shafer Theory

Hybrid Modeling of Heavy Duty Gas Turbines for On-Line Performance Monitoring

2014 ◽  
Author(s):  
Thomas Palmé ◽  
Francois Liard ◽  
Dan Cameron
Keyword(s):  
Gas Turbine ◽  
Surrogate Model ◽  
Gas Turbines ◽  
Performance Monitoring ◽  
Performance Model ◽  
Heavy Duty ◽  
Operational Parameters ◽  
Actual Performance ◽  
Real Gas ◽  
The Real

Due to their complex physics, accurate modeling of modern heavy duty gas turbines can be both challenging and time consuming. For online performance monitoring, the purpose of modeling is to predict operational parameters to assess the current performance and identify any possible deviation between the model’s expected performance parameters and the actual performance. In this paper, a method is presented to tune a physical model to a specific gas turbine by applying a data-driven approach to correct for the differences between the real gas turbine operation and the performance model prediction of the same. The first step in this process is to generate a surrogate model of the 1st principle performance model through the use of a neural network. A second “correction model” is then developed from selected operational data to correct the differences between the surrogate model and the real gas turbine. This corrects for the inaccuracies between the performance model and the real operation. The methodology is described and the results from its application to a heavy duty gas turbine are presented in this paper.

Decision Support System for Advanced Traffic Management Through Data Fusion

2002 ◽  
Vol 1804 (1) ◽  
pp. 173-178 ◽  
Author(s):  
Lawrence A. Klein ◽  
Ping Yi ◽  
Hualiang Teng
Keyword(s):  
Data Fusion ◽  
Traffic Management ◽  
Information Sources ◽  
Partial Information ◽  
Weather Conditions ◽  
Field Testing ◽  
Traffic Operations ◽  
Dempster Shafer Theory ◽  
Classification Technique ◽  
Shafer Theory

The Dempster–Shafer theory for data fusion and mining in support of advanced traffic management is introduced and tested. Dempste–Shafer inference is a statistically based classification technique that can be applied to detect traffic events that affect normal traffic operations. It is useful when data or information sources contribute partial information about a scenario, and no single source provides a high probability of identifying the event responsible for the received information. The technique captures and combines whatever information is available from the data sources. Dempster’s rule is applied to determine the most probable event—as that with the largest probability based on the information obtained from all contributing sources. The Dempster–Shafer theory is explained and its implementation described through numerical examples. Field testing of the data fusion technique demonstrated its effectiveness when the probability masses, which quantify the likelihood of the postulated events for the scenario, reflect current traffic and weather conditions.

Remote Thermal Performance Monitoring and Diagnostics: Turning Data Into Knowledge

2013 ◽  
Author(s):  
Xiaomo Jiang ◽  
Craig Foster
Keyword(s):  
Anomaly Detection ◽  
Gas Turbines ◽  
Performance Monitoring ◽  
Combined Cycle ◽  
Integrated System ◽  
Site Specific ◽  
Performance Improvements ◽  
Maintenance Schedule ◽  
Effectiveness Monitoring ◽  
And Performance

Gas turbine simple or combined cycle plants are built and operated with higher availability, reliability, and performance in order to provide the customer with sufficient operating revenues and reduced fuel costs meanwhile enhancing customer dispatch competitiveness. A tremendous amount of operational data is usually collected from the everyday operation of a power plant. It has become an increasingly important but challenging issue about how to turn this data into knowledge and further solutions via developing advanced state-of-the-art analytics. This paper presents an integrated system and methodology to pursue this purpose by automating multi-level, multi-paradigm, multi-facet performance monitoring and anomaly detection for heavy duty gas turbines. The system provides an intelligent platform to drive site-specific performance improvements, mitigate outage risk, rationalize operational pattern, and enhance maintenance schedule and service offerings via taking appropriate proactive actions. In addition, the paper also presents the components in the system, including data sensing, hardware, and operational anomaly detection, expertise proactive act of company, site specific degradation assessment, and water wash effectiveness monitoring and analytics. As demonstrated in two examples, this remote performance monitoring aims to improve equipment efficiency by converting data into knowledge and solutions in order to drive value for customers including lowering operating fuel cost and increasing customer power sales and life cycle value.

scholarly journals Automatic Updates of Transition Potential Matrices in Dempster-Shafer Networks Based on Evidence Inputs

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3727
Author(s):  
Joel Dunham ◽  
Eric Johnson ◽  
Eric Feron ◽  
Brian German
Keyword(s):  
Sensor Fusion ◽  
A Priori ◽  
Evidential Reasoning ◽  
Unmanned Aerial Systems ◽  
Sufficient Information ◽  
User Input ◽  
Transition Potential ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Aerial Systems

Sensor fusion is a topic central to aerospace engineering and is particularly applicable to unmanned aerial systems (UAS). Evidential Reasoning, also known as Dempster-Shafer theory, is used heavily in sensor fusion for detection classification. High computing requirements typically limit use on small UAS platforms. Valuation networks, the general name given to evidential reasoning networks by Shenoy, provides a means to reduce computing requirements through knowledge structure. However, these networks use conditional probabilities or transition potential matrices to describe the relationships between nodes, which typically require expert information to define and update. This paper proposes and tests a novel method to learn these transition potential matrices based on evidence injected at nodes. Novel refinements to the method are also introduced, demonstrating improvements in capturing the relationships between the node belief distributions. Finally, novel rules are introduced and tested for evidence weighting at nodes during simultaneous evidence injections, correctly balancing the injected evidenced used to learn the transition potential matrices. Together, these methods enable updating a Dempster-Shafer network with significantly less user input, thereby making these networks more useful for scenarios in which sufficient information concerning relationships between nodes is not known a priori.

scholarly journals Investigation of Failure in Gas Turbines: Part 2 — Engineering and Metallographic Aspects of Failure Investigation

1993 ◽  
Author(s):  
Robert E. Dundas
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Heavy Duty ◽  
Metallographic Examination ◽  
Fracture Surfaces ◽  
Failure Investigation ◽  
Materials Used ◽  
Component Failures

This paper opens with a discussion of the various mechanisms of cracking and fracture encountered in gas turbine failures, and discusses the use of metallographic examination of crack and fracture surfaces. The various types of materials used in the major components of heavy-duty industrial and aeroderivative gas turbines are tabulated. A collection of macroscopic and microscopic fractographs of the various mechanisms of failure in gas turbine components is then presented for reference in failure investigation. A discussion of compressor damage due to surge, as well as some overall observations on component failures, follows. Finally, a listing of the most likely types of failure of the various major components is given.

An application of Dempster-Shafer theory to the assessment of biogas technology

Energy ◽  
1992 ◽  
Vol 17 (3) ◽  
pp. 205-214
Author(s):  
Aurora A. Kawahara ◽  
Peter M. Williams
Keyword(s):  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Biogas Technology

Development and Validation of a Three-Dimensional Multiphase Flow CFD Analysis for Journal Bearings in Steam and Heavy Duty Gas Turbines

2012 ◽  
Author(s):  
Stephan Uhkoetter ◽  
Stefan aus der Wiesche ◽  
Michael Kursch ◽  
Christian Beck
Keyword(s):  
Experimental Data ◽  
Multiphase Flow ◽  
Gas Turbines ◽  
High Speed ◽  
Three Dimensional ◽  
Air Entrainment ◽  
Journal Bearings ◽  
Heavy Duty ◽  
Present Contribution ◽  
Two Phase

The traditional method for hydrodynamic journal bearing analysis usually applies the lubrication theory based on the Reynolds equation and suitable empirical modifications to cover turbulence, heat transfer, and cavitation. In cases of complex bearing geometries for steam and heavy-duty gas turbines this approach has its obvious restrictions in regard to detail flow recirculation, mixing, mass balance, and filling level phenomena. These limitations could be circumvented by applying a computational fluid dynamics (CFD) approach resting closer to the fundamental physical laws. The present contribution reports about the state of the art of such a fully three-dimensional multiphase-flow CFD approach including cavitation and air entrainment for high-speed turbo-machinery journal bearings. It has been developed and validated using experimental data. Due to the high ambient shear rates in bearings, the multiphase-flow model for journal bearings requires substantial modifications in comparison to common two-phase flow simulations. Based on experimental data, it is found, that particular cavitation phenomena are essential for the understanding of steam and heavy-duty type gas turbine journal bearings.

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