Probability Analysis of Static Frequency and Dynamic Frequency of Steam Turbine Blade Based on RBF Neural Network and Monte Carlo Simulation

2007 ◽  
Author(s):  
Wei Duan ◽  
Zhang-Qi Wang
Keyword(s):  
Neural Network ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Steam Turbine ◽  
Turbine Blade ◽  
Rbf Neural Network ◽  
Probability Analysis ◽  
Steam Turbine Blade ◽  
Dynamic Frequency

Reliability Analysis of Steam Turbine Blade Using Monte Carlo Simulation

2004 ◽  
Vol 261-263 ◽  
pp. 549-554
Author(s):  
Chul Su Kim ◽  
Jung Kyu Kim
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Fatigue Strength ◽  
Reliability Analysis ◽  
Steam Turbine ◽  
Turbine Blade ◽  
Applied Stress ◽  
Element Analysis ◽  
Bending Load ◽  
Steam Turbine Blade

In this study, the reliability analysis of the low pressure steam turbine blade was performed using the Monte Carlo simulation considering variations of applied stress and strength. Applied stress under the service condition of steady state was obtained by finite element analysis. The fatigue strength under rotating bending load was evaluated by the staircase method. The most appropriate probabilistic distribution of the fatigue strength is 3-parameter Weibull distribution, which is determined by the comparative analysis. The failure probability under various loading conditions was derived from the strength-stress interference model.

Kalman Filter Based Neural Network Methodology for Predictive Maintenance: A Case Study on Steam Turbine Blade Performance Prognostics

2006 ◽  
Author(s):  
Jihong Yan ◽  
Min Lv ◽  
Pengxiang Wang ◽  
Meiying Wang
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Steam Turbine ◽  
Turbine Blade ◽  
Residual Life ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Neural Network Approach ◽  
Steam Turbine Blade ◽  
Component Failures

Predictive maintenance involves condition monitoring, fault detection and prediction of remaining useful life or forthcoming failures. Predictive maintenance systems for steam turbine engines offer detection, classification, and prediction (or prognosis) of potential critical component failures, and ensures substantially reducing the cost of repair and replacement of defective parts, and may even result in saving lives. This paper describes a Kalman filter based neural network approach to provide performance evaluation and residual life prediction with the objectives of improving availability and implementing maintenance before failure occurs by estimating degradation severity and the proper timing for replacement. The approach has been applied to a steam turbine blade fatigue experiment testbed to illustrate the prognostic functionalities of the methodology.

scholarly journals Failure Analysis in Lacing Wire Of Last Stage Low Pressure Steam Turbine Blade

2021 ◽  
Vol 1096 (1) ◽  
pp. 012097
Author(s):  
A M Kongkong ◽  
H Setiawan ◽  
J Miftahul ◽  
A R Laksana ◽  
I Djunaedi ◽  
...  
Keyword(s):  
Failure Analysis ◽  
Steam Turbine ◽  
Turbine Blade ◽  
Low Pressure ◽  
Pressure Steam ◽  
Steam Turbine Blade ◽  
Last Stage

Studies on Determination of Natural Frequencies of Industrial Turbine Blades

1995 ◽  
Author(s):  
Mahesh M. Bhat ◽  
V. Ramamurti ◽  
C. Sujatha
Keyword(s):  
Steam Turbine ◽  
Dynamic Behavior ◽  
Turbine Blade ◽  
Natural Frequencies ◽  
Complex Structure ◽  
Turbine Blades ◽  
Blade Root ◽  
Steam Turbine Blade ◽  
Manufacturing Tolerances

Abstract Steam turbine blade is a very complex structure. It has geometric complexities like variation of twist, taper, width and thickness along its length. Most of the time these variations are not uniform. Apart from these geometric complexities, the blades are coupled by means of lacing wire, lacing rod or shroud. Blades are attached to a flexible disc which contributes to the dynamic behavior of the blade. Root fixity also plays an important role in this behavior. There is a considerable variation in the frequencies of blades of newly assembled turbine and frequencies after some hours of running. Again because of manufacturing tolerances there can be some variation in the blade to blade frequencies. Determination of natural frequencies of the blade is therefore a very critical job. Problems associated with typical industrial turbine bladed discs of a 235 MW steam turbine are highlighted in this paper.

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