Thermoelastic Steam Turbine Rotor Control Based on Neural Network

2015 ◽  
pp. 2283-2294
Author(s):  
Krzysztof Dominiczak ◽  
Romuald Rzadkowski ◽  
Wojciech Radulski
Keyword(s):  
Neural Network ◽  
Steam Turbine ◽  
Turbine Rotor ◽  
Steam Turbine Rotor ◽  
Rotor Control

scholarly journals Thermoelastic steam turbine rotor control based on neural network

2015 ◽  
Vol 662 ◽  
pp. 012021
Author(s):  
Romuald Rzadkowski ◽  
Krzysztof Dominiczak ◽  
Wojciech Radulski ◽  
R Szczepanik
Keyword(s):  
Neural Network ◽  
Steam Turbine ◽  
Turbine Rotor ◽  
Steam Turbine Rotor ◽  
Rotor Control

Research on Fault Diagnosis of Steam Turbine Rotor Unbalance and Parallel Misalignment Based on Numerical Simulation and Convolutional Neural Network

2021 ◽  
Author(s):  
Chongyu Wang ◽  
Di Zhang ◽  
Yonghui Xie
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Fault Detection ◽  
Steam Turbine ◽  
Turbine Rotor ◽  
Detection Accuracy ◽  
Steam Turbine Rotor ◽  
Parallel Misalignment ◽  
Coupling Fault ◽  
The Impact

Abstract The steam turbine rotor is still the main power generation equipment. Affected by the impact of new energy on the power grid, the steam turbine needs to participate in peak load regulation, which will make turbine rotor components more prone to failure. The rotor is an important equipment of a steam turbine. Unbalance and misalignment are the normal state of rotor failure. In recent years, more and more attention has been paid to the fault detection method based on deep learning, which takes rotating machinery as the object. However, there is a lack of research on actual steam turbine rotors. In this paper, a method of rotor unbalance and parallel misalignment fault detection based on residual network is proposed, which realizes the end-to-end fault detection of rotor. Meanwhile, the method is evaluated with numerical simulation data, and the multi task detection of rotor unbalance, parallel misalignment, unbalanced parallel misalignment coupling faults (coupling fault called in this paper) is realized. The influence of signal-to-noise ratio and the number of training samples on the detection performance of neural network is discussed. The detection accuracy of unbalanced position is 93.5%, that of parallel misalignment is 99.1%. The detection accuracy for unbalance and parallel misalignment is 89.1% and 99.1%, respectively. The method can realize the direct mapping between the unbalanced, parallel misalignment, coupling fault vibration signals and the fault detection results. The method has the ability to automatically extract fault features. It overcomes the shortcoming of traditional methods that rely on signal processing experience, and has the characteristics of high precision and strong robustness.

scholarly journals C206 A METHOD EVALUATING SERVICE LIFE OF STEAM TURBINE ROTOR BASED ON ARTIFICIAL NEURAL NETWORK

2003 ◽  
Vol 2003.2 (0) ◽  
pp. _2-201_-_2-204_
Author(s):  
Chun YE ◽  
Hanping CHEN ◽  
Anzhong JIANG ◽  
Jianhua XIN ◽  
Xing JIN ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Service Life ◽  
Steam Turbine ◽  
Turbine Rotor ◽  
Steam Turbine Rotor ◽  
Artificial Neural

scholarly journals A linear cascade tunnel for flow investigations of steam turbine rotor tip blades in subsonic nucleating flows

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Vital Kumar Yadav Pillala ◽  
B. V. S. S. S. Prasad ◽  
N. Sitaram ◽  
M. Mahendran ◽  
Debasish Biswas ◽  
...  
Keyword(s):  
Steam Turbine ◽  
Turbine Rotor ◽  
Turbine Cascade ◽  
Flow Measurements ◽  
Linear Cascade ◽  
Dry Air ◽  
Steam Turbine Rotor ◽  
Pressure Distributions ◽  
Working Medium ◽  
Exit Mach Number

AbstractThe paper presents details of a unique experimental facility along with necessary accessories and instrumentation for testing steam turbine cascade blades in wet and nucleating steam. A steam turbine rotor tip cascade is chosen for flow investigations. Cascade inlet flow measurements show uniform conditions with dry air and steam and dry air mixture of different ratios. Exit flow surveys indicate that excellent flow periodicity is obtained. Blade surface static pressure and exit total pressure distributions are also presented with dry air and with steam and dry air mixture of different ratios as the working medium at an exit Mach number of 0.52.

scholarly journals On the creep fatigue and creep rupture behaviours of 9–12% Cr steam turbine rotor

2019 ◽  
Vol 76 ◽  
pp. 263-278 ◽  
Author(s):  
Xuanchen Zhu ◽  
Haofeng Chen ◽  
Fuzhen Xuan ◽  
Xiaohui Chen
Keyword(s):  
Steam Turbine ◽  
Turbine Rotor ◽  
Creep Rupture ◽  
Steam Turbine Rotor ◽  
Creep Fatigue

Effects of Laser Cladding Layers Width on Total Indicated Runout Characteristics of Steam Turbine Rotor Surface

2017 ◽  
Vol 46 (3) ◽  
pp. 612-616
Author(s):  
Guo Shirui ◽  
Shang Huichao ◽  
Cui Lujun ◽  
Guo Xiaofeng ◽  
Yao Jianhua
Keyword(s):  
Steam Turbine ◽  
Laser Cladding ◽  
Turbine Rotor ◽  
Steam Turbine Rotor ◽  
Rotor Surface ◽  
Cladding Layers
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