Development of Fault Specific Soft Sensors With Application to Gas Turbine Diagnosis

2012 ◽  
Author(s):  
Dorin Scheianu
Keyword(s):  
Gas Turbine ◽  
Dimensional Space ◽  
Analysis Data ◽  
Soft Sensor ◽  
Operating Conditions ◽  
Soft Sensors ◽  
Multivariate Statistical ◽  
Current Observation ◽  
Observation Vector ◽  
Turbine Installation

The paper presents a method of diagnosis for a gas turbine installation monitored for a number of parameters by using tools specific to multivariate statistical analysis. Data is acquired periodically and organized as individual observation vectors. The objective is to detect the occurrence of a fault, to identify and discriminate the type of fault and eventually to find its cause. The author developed a new concept of soft sensor (similar to the sensor fusion in other works, see [1]) based on the intrinsic properties of the data historically acquired when the process was deemed to be in normal operating conditions, and on the current observation vector. This soft sensor was applied to an example of detecting the occurrence of a fault and characterizing it in the p-dimensional space of observation vectors associated with a proper metric.

Development of a Companion Set of Charts—Soft Sensor and Directional Moving Range—for Fault Monitoring, Detection and Diagnosis With Application to Gas Turbine Engines

2008 ◽  
Author(s):  
Dorin Scheianu ◽  
Phillip A. Farrington
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Research Literature ◽  
Soft Sensor ◽  
Fault Detection And Diagnosis ◽  
Critical Parameters ◽  
Multivariate Statistical ◽  
Process Variability ◽  
Monitoring Process ◽  
Detection And Diagnosis

Gas turbines monitoring for fault detection and diagnosis is long desired to be embedded within control systems. Yet the general approach is to have alarms and shut downs when critical parameters exceed certain limits, and fault diagnosis is initiated on the behalf of experienced professionals and testing apparatus during scheduled maintenance time. Statistical methods for monitoring univariate and multivariate processes have been developed and publicized in the research literature. A gas turbine can be treated as a complex multivariate process with parameters depending both on control variables imposed by operator and on independent ambient parameters. The authors propose a set of companion charts that can be implemented on line and allows continuous monitoring both for fault amplitude — represented by a newly introduced soft sensor — and for process variability in the direction of interest. The control limits are introduced using multivariate statistical theory. The set of charts was applied at Wood Group LIT in a test cell, for monitoring process variability and for diagnosis and characterization of engine faults during tests. A second application is used for early detection of faults at the current serviced fleet of turbines.

scholarly journals Condition Assessment of Industrial Gas Turbine Compressor Using a Drift Soft Sensor Based in Autoencoder

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2708
Author(s):  
Martí de Castro-Cros ◽  
Stefano Rosso ◽  
Edgar Bahilo ◽  
Manel Velasco ◽  
Cecilio Angulo
Keyword(s):  
Gas Turbine ◽  
Good Condition ◽  
Condition Assessment ◽  
Soft Sensor ◽  
Soft Sensors ◽  
Gas Turbine Compressor ◽  
Industrial Gas Turbine ◽  
Long Term Performance ◽  
Changes Over Time ◽  
Term Performance

Maintenance is the process of preserving the good condition of a system to ensure its reliability and availability to perform specific operations. The way maintenance is nowadays performed in industry is changing thanks to the increasing availability of data and condition assessment methods. Soft sensors have been widely used over last years to monitor industrial processes and to predict process variables that are difficult to measured. The main objective of this study is to monitor and evaluate the condition of the compressor in a particular industrial gas turbine by developing a soft sensor following an autoencoder architecture. The data used to monitor and analyze its condition were captured by several sensors located along the compressor for around five years. The condition assessment of an industrial gas turbine compressor reveals significant changes over time, as well as a drift in its performance. These results lead to a qualitative indicator of the compressor behavior in long-term performance.

scholarly journals Using Soft Sensors as a Basis of an Innovative Architecture for Operation Planning and Quality Evaluation in Agricultural Sprayers

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1269
Author(s):  
Elmer A. G. Peñaloza ◽  
Vilma A. Oliveira ◽  
Paulo E. Cruvinel
Keyword(s):  
Large Scale ◽  
Principal Component ◽  
Soft Sensor ◽  
Operating Conditions ◽  
K Nearest Neighbor ◽  
Pesticide Application ◽  
Soft Sensors ◽  
Agricultural Pesticides ◽  
Pcr Method

One of the major problems facing humanity in the coming decades is the production of food on a large scale. The production of large quantities of food must be conducted in a sustainable and responsible manner for nature and humans. In this sense, the appropriate application of agricultural pesticides plays a fundamental role since pesticide application in a qualified manner reduces human and environmental risks as well as the costs of food production. Evaluation of the quality of application using sprayers is an important issue, and several quality descriptors related to the average diameter and distribution of droplets are used. This paper describes the construction of a data-driven soft sensor using the parametric principal component regression (PCR) method based on principal component analysis (PCA), which works in two configurations: with the input being the operating conditions of the agricultural boom sprayers and its outputs being the prediction of the quality descriptors of spraying, and vice versa. The soft sensor provides, in one configuration, estimates of the quality of pesticide application at a certain time and, in the other, estimates of the appropriate sprayer-operating conditions, which can be used for control and optimization of the processes in pesticide application. Full cone nozzles are used to illustrate a practical application as well as to validate the usefulness of the soft sensor designed with the PCR method. The selection of historical data, exploration, and filtering of data, and the structure and validation of the soft sensor are presented. For comparison purposes, the results with the well-known nonparametric k-Nearest Neighbor (k−NN) regression method are presented. The results of this research reveal the usefulness of soft sensors in the application of agricultural pesticides and as a knowledge base to assist in agricultural decision-making.

Multivariate Statistical Analysis Regarding the Formulation of Oxicam-Based Pharmaceutical Hydrogels

2017 ◽  
Vol 68 (4) ◽  
pp. 726-731
Author(s):  
Lenuta Maria Suta ◽  
Anca Tudor ◽  
Colette Roxana Sandulovici ◽  
Lavinia Stelea ◽  
Daniel Hadaruga ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Pharmaceutical Formulations ◽  
Analysis Data ◽  
Multivariate Statistical ◽  
Predictive Variables ◽  
Rapid Release ◽  
Computational Data ◽  
Experimental Protocols ◽  
Formulation Factors

In this paper, it was analysed the influence of formulation factors over obtaining oxicam hydrogels, using the statistical analysis. Data analysis and predictive modeling by multivariate regression offers a large number of possible explanatory/predictive variables. Therefore, variable selection and dimension reduction is a major task for multivariate statistical analysis, especially for multivariate regressions. The statistical analysis and computational data processing of responses obtained from different pharmaceutical formulations, via different experimental protocols, lead to the optimization of the formulation process. It was found that the most suitable pharmaceutical formulations based on oxicams with the possibility of rapid release contained cyclodextrin, in particular 2-hydroxypropyl-b-cyclodextrin.

scholarly journals A Comparative Study on Fault Detection Methods for Gas Turbine Combustion Systems

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 389
Author(s):  
Jinfu Liu ◽  
Zhenhua Long ◽  
Mingliang Bai ◽  
Linhai Zhu ◽  
Daren Yu
Keyword(s):  
Fault Detection ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Operating Conditions ◽  
Detection Methods ◽  
Distribution Model ◽  
Outlet Temperature ◽  
Combustion System ◽  
Comparison Results ◽  
Gas Turbine Combustion

As one of the core components of gas turbines, the combustion system operates in a high-temperature and high-pressure adverse environment, which makes it extremely prone to faults and catastrophic accidents. Therefore, it is necessary to monitor the combustion system to detect in a timely way whether its performance has deteriorated, to improve the safety and economy of gas turbine operation. However, the combustor outlet temperature is so high that conventional sensors cannot work in such a harsh environment for a long time. In practical application, temperature thermocouples distributed at the turbine outlet are used to monitor the exhaust gas temperature (EGT) to indirectly monitor the performance of the combustion system, but, the EGT is not only affected by faults but also influenced by many interference factors, such as ambient conditions, operating conditions, rotation and mixing of uneven hot gas, performance degradation of compressor, etc., which will reduce the sensitivity and reliability of fault detection. For this reason, many scholars have devoted themselves to the research of combustion system fault detection and proposed many excellent methods. However, few studies have compared these methods. This paper will introduce the main methods of combustion system fault detection and select current mainstream methods for analysis. And a circumferential temperature distribution model of gas turbine is established to simulate the EGT profile when a fault is coupled with interference factors, then use the simulation data to compare the detection results of selected methods. Besides, the comparison results are verified by the actual operation data of a gas turbine. Finally, through comparative research and mechanism analysis, the study points out a more suitable method for gas turbine combustion system fault detection and proposes possible development directions.

Chemical Reactor Network Application to Emissions Prediction for Industial DLE Gas Turbine

2006 ◽  
Author(s):  
I. V. Novosselov ◽  
P. C. Malte ◽  
S. Yuan ◽  
R. Srinivasan ◽  
J. C. Y. Lee
Keyword(s):  
Gas Turbine ◽  
Chemical Reactor ◽  
Kinetic Mechanism ◽  
Chemical Kinetic ◽  
Operating Conditions ◽  
Reacting Flow ◽  
Ongoing Research ◽  
Chemical Reactor Network ◽  
Reaction Zones ◽  
Reactor Network

A chemical reactor network (CRN) is developed and applied to a dry low emissions (DLE) industrial gas turbine combustor with the purpose of predicting exhaust emissions. The development of the CRN model is guided by reacting flow computational fluid dynamics (CFD) using the University of Washington (UW) eight-step global mechanism. The network consists of 31 chemical reactor elements representing the different flow and reaction zones of the combustor. The CRN is exercised for full load operating conditions with variable pilot flows ranging from 35% to 200% of the neutral pilot. The NOpilot. The NOx and the CO emissions are predicted using the full GRI 3.0 chemical kinetic mechanism in the CRN. The CRN results closely match the actual engine test rig emissions output. Additional work is ongoing and the results from this ongoing research will be presented in future publications.

Numerical Simulation of Gas Flow and Heat Transfer in Cross-Wavy Primary Surface Channel for Microtubine Recuperators

2005 ◽  
Author(s):  
H. X. Liang ◽  
Q. W. Wang ◽  
L. Q. Luo ◽  
Z. P. Feng
Keyword(s):  
Heat Transfer ◽  
Numerical Simulation ◽  
Gas Turbine ◽  
Numerical Study ◽  
High Reliability ◽  
Three Dimensional ◽  
Operating Conditions ◽  
Flow And Heat Transfer ◽  
High Heat Transfer ◽  
The Cross

Three-dimensional numerical simulation was conducted to investigate the flow field and heat transfer performance of the Cross-Wavy Primary Surface (CWPS) recuperators for microturbines. Using high-effective compact recuperators to achieve high thermal efficiency is one of the key techniques in the development of microturbine in recent years. Recuperators need to have minimum volume and weight, high reliability and durability. Most important of all, they need to have high thermal-effectiveness and low pressure-losses so that the gas turbine system can achieve high thermal performances. These requirements have attracted some research efforts in designing and implementing low-cost and compact recuperators for gas turbine engines recently. One of the promising techniques to achieve this goal is the so-called primary surface channels with small hydraulic dimensions. In this paper, we conducted a three-dimensional numerical study of flow and heat transfer for the Cross-Wavy Primary Surface (CWPS) channels with two different geometries. In the CWPS configurations the secondary flow is created by means of curved and interrupted surfaces, which may disturb the thermal boundary layers and thus improve the thermal performances of the channels. To facilitate comparison, we chose the identical hydraulic diameters for the above four CWPS channels. Since our experiments on real recuperators showed that the Reynolds number ranges from 150 to 500 under the operating conditions, we implemented all the simulations under laminar flow situations. By analyzing the correlations of Nusselt numbers and friction factors vs. Reynolds numbers of the four CWPS channels, we found that the CWPS channels have superior and comprehensive thermal performance with high compactness, i.e., high heat transfer area to volume ratio, indicating excellent commercialized application in the compact recuperators.

A Self-Correcting and Self-Checking Gas Turbine Meter

1982 ◽  
Vol 104 (2) ◽  
pp. 143-149 ◽  
Author(s):  
W. F. Z. Lee ◽  
D. C. Blakeslee ◽  
R. V. White
Keyword(s):  
Gas Turbine ◽  
Operating Conditions ◽  
Exit Angle ◽  
Test Results ◽  
Main Rotor ◽  
Calibration Accuracy

A new metering concept of a self-correcting and self-checking turbine meter is described in which a sensor rotor downstream from the main rotor senses and responds to changes in the exit angle of the fluid leaving the main rotor. The output from the sensor rotor is then electronically combined with the output from the main rotor to produce an adjusted output which automatically and continuously corrects to original meter calibration accuracy. This takes place despite changes in retarding torques, bearing wear and/or upstream conditions occurring in field operations over those which were experienced during calibration. The ratio of the sensor rotor output to the main rotor output at operating conditions is also automatically and continuously compared with that at calibration conditions. This provides an indication of the amount of accuracy deviation from initial calibration that is being corrected by the sensor rotor. This concept is studied theoretically and experimentally. Both the theory and test results (laboratory and field) confirm the concept’s validity and practicability.

Combustion Tuning for a Gas Turbine Power Plant Using Data-Driven and Machine Learning Approach

2021 ◽  
Vol 143 (3) ◽  
Author(s):  
Suhui Li ◽  
Huaxin Zhu ◽  
Min Zhu ◽  
Gang Zhao ◽  
Xiaofeng Wei
Keyword(s):  
Machine Learning ◽  
Power Plant ◽  
Gas Turbine ◽  
Operating Conditions ◽  
Data Driven ◽  
Ann Model ◽  
Promising Alternative ◽  
Combustion Performance ◽  
Wide Range ◽  
Gas Turbine Power Plant

Abstract Conventional physics-based or experimental-based approaches for gas turbine combustion tuning are time consuming and cost intensive. Recent advances in data analytics provide an alternative method. In this paper, we present a cross-disciplinary study on the combustion tuning of an F-class gas turbine that combines machine learning with physics understanding. An artificial-neural-network-based (ANN) model is developed to predict the combustion performance (outputs), including NOx emissions, combustion dynamics, combustor vibrational acceleration, and turbine exhaust temperature. The inputs of the ANN model are identified by analyzing the key operating variables that impact the combustion performance, such as the pilot and the premixed fuel flow, and the inlet guide vane angle. The ANN model is trained by field data from an F-class gas turbine power plant. The trained model is able to describe the combustion performance at an acceptable accuracy in a wide range of operating conditions. In combination with the genetic algorithm, the model is applied to optimize the combustion performance of the gas turbine. Results demonstrate that the data-driven method offers a promising alternative for combustion tuning at a low cost and fast turn-around.

Sign in / Sign up

Export Citation Format

Share Document