Coating Life Prediction for Combustion Turbine Blades

1999 ◽  
Vol 121 (3) ◽  
pp. 484-488 ◽  
Author(s):  
K. S. Chan ◽  
N. S. Cheruvu ◽  
G. R. Leverant
Keyword(s):  
Experimental Data ◽  
Turbine Blade ◽  
Cycle Time ◽  
Life Prediction ◽  
Aluminide Coating ◽  
Turbine Blades ◽  
Prediction Method ◽  
Remaining Life ◽  
Degradation Mechanisms ◽  
Useful Life

A life prediction method for combustion turbine blade coatings has been developed by modeling coating degradation mechanisms including oxidation, spallation, and aluminum loss due to inward diffusion. Using this model, the influence of cycle time on coating life is predicted for GTD-111 coated with an MCrAlY, PtAl, or aluminide coating. The results are used to construct a coating life diagram that depicts failure and safe regions for the coating in a log-log Plot of number of startup cycles versus cycle time. The regime where failure by oxidation, spallation, and inward diffusion dominates is identified and delineated from that dominated by oxidation and inward diffusion only. A procedure for predicting the remaining life of a coating is developed. The utility of the coating life diagram for predicting the failure and useful life of MCrAlY, aluminide, or PtAl coatings on the GTD-111 substrate is illustrated and compared against experimental data.

scholarly journals Coating Life Prediction for Combustion Turbine Blades

1998 ◽  
Author(s):  
Kwai S. Chan ◽  
N. Sastry Cheruvu ◽  
Gerald R. Leverant
Keyword(s):  
Experimental Data ◽  
Turbine Blade ◽  
Cycle Time ◽  
Life Prediction ◽  
Aluminide Coating ◽  
Turbine Blades ◽  
Prediction Method ◽  
Remaining Life ◽  
Degradation Mechanisms ◽  
Useful Life

A life prediction method for combustion turbine blade coatings has been developed by modeling coating degradation mechanisms including oxidation, spallation, and aluminum loss due to inward diffusion. Using this model, the influence of cycle time on coating life is predicted for GTD-111 coated with an MCrAlY, PtAl, or aluminide coating. The results are used to construct a coating life diagram that depicts failure and safe regions for the coating in a log-log plot of number of startup cycles versus cycle time. The regime where failure by oxidation, spallation, and inward diffusion dominates is identified and delineated from that dominated by oxidation and inward diffusion only. A procedure for predicting the remaining life of a coating is developed. The utility of the coating life diagram for predicting the failure and useful life of MCrAlY, aluminide, or PtAl coatings on the GTD-111 substrate is illustrated and compared against experimental data.

Hybrid remaining useful life prediction method. A case study on railway D-cables

2021 ◽  
pp. 107746
Author(s):  
Yu Zang ◽  
Wei Shangguan ◽  
Baigen Cai ◽  
Huasheng Wang ◽  
Michael. G. Pecht
Keyword(s):  
Life Prediction ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Useful Life

Remaining useful life prediction method for machine tools based on meta-action theory

2021 ◽  
pp. 1748006X2110025
Author(s):  
Zongyi Mu ◽  
Yan Ran ◽  
Genbao Zhang ◽  
Hongwei Wang ◽  
Xin Yang
Keyword(s):  
Machine Tools ◽  
Life Prediction ◽  
Action Theory ◽  
Prediction Method ◽  
Operational Reliability ◽  
Remaining Useful Life ◽  
Practical Application ◽  
Dynamic Prediction ◽  
Degradation Model ◽  
Useful Life

Remaining useful life (RUL) is a crucial indictor to measure the performance degradation of machine tools. It directly affects the accuracy of maintenance decision-making, thus affecting operational reliability of machine tools. Currently, most RUL prediction methods are for the parts. However, due to the interaction among the parts, even RUL of all the parts cannot reflect the real RUL of the whole machine. Therefore, an RUL prediction method for the whole machine is needed. To predict RUL of the whole machine, this paper proposes an RUL prediction method with dynamic prediction objects based on meta-action theory. Firstly, machine tools are decomposed into the meta-action unit chains (MUCs) to obtain suitable prediction objects. Secondly, the machining precision unqualified rate (MPUR) control chart is used to conduct an out of control early warning for machine tools’ performance. At last, the Markov model is introduced to determine the prediction objects in next prediction and the Wiener degradation model is established to predict RUL of machine tools. According to the practical application, feasibility and effectiveness of the method is proved.

Water Droplet Erosion Life Prediction Method for Steam Turbine Blade Materials Based on Image Recognition and Machine Learning

2020 ◽  
Author(s):  
Zheyuan Zhang ◽  
Tianyuan Liu ◽  
Di Zhang ◽  
Yonghui Xie
Keyword(s):  
Machine Learning ◽  
Turbine Blade ◽  
Image Recognition ◽  
Water Droplet ◽  
Turbine Blades ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Feature Maps ◽  
Water Droplet Erosion ◽  
Data Processing Method

Abstract In this paper, a method for predicting remaining useful life (RUL) of turbine blade under water droplet erosion (WDE) based on image recognition and machine learning is presented. Using the experimental rig for testing the WDE characteristics of materials, the morphology pictures of specimen surface at different times in the process of WDE are collected. According to the data processing method of ASTM-G73 and the cumulative erosion-time curves, the WDE stages of materials is quantitatively divided and the WDE life coefficient (ζ) is defined. The life coefficient (ζ) could be used to calculate the RUL of turbine blades. One convolutional neural network model and three machine learning models are adopted to train and predict the image dataset. Then the training process and feature maps of the Resnet model are studied in detail. It is found that the highest prediction accuracy of the method proposed in this paper can be 0.949, which is considered acceptable to provide reference for turbine overhaul period and blade replacement time.

Life Prediction Method of CC and DS Ni Base Superalloys Under High Temperature Biaxial Fatigue Loading

2010 ◽  
Vol 132 (11) ◽  
Author(s):  
Takashi Ogata
Keyword(s):  
Fatigue Life ◽  
Gas Turbine ◽  
Turbine Blade ◽  
Life Prediction ◽  
Compressive Strain ◽  
Prediction Method ◽  
Strain Range ◽  
Fatigue Loading ◽  
Normal Strain ◽  
Biaxial Fatigue

Polycrystalline conventional casting (CC) and directionally solidified (DS) Ni base superalloys are widely used as gas turbine blade materials. It was reported that the surface of a gas turbine blade is subjected to a biaxial tensile-compressive fatigue loading during a start-stop operation, based on finite element stress analysis results. It is necessary to establish the life prediction method of these superalloys under biaxial fatigue loading for reliable operations. In this study, the in-plane biaxial fatigue tests with different phases of x and y directional strain cycles were conducted on both CC and DS Ni base superalloys (IN738LC and GTD111DS) at high temperatures. The strain ratio ϕ was defined as the ratio between the x and y directional strains at 1/4 cycle and was varied from 1 to −1. In ϕ=1 and −1. The main cracks propagated in both the x and y directions in the CC superalloy. On the other hand, the main cracks of the DS superalloy propagated only in the x direction, indicating that the failure resistance in the solidified direction is weaker than that in the direction normal to the solidified direction. Although the biaxial fatigue life of the CC superalloy was correlated with the conventional Mises equivalent strain range, that of the DS superalloy depended on ϕ. The new biaxial fatigue life criterion, equivalent normal strain range for the DS superalloy was derived from the iso-fatigue life curve on a principal strain plane defined in this study. Fatigue life of the DS superalloy was correlated with the equivalent normal strain range. Fatigue life of the DS superalloy under equibiaxial fatigue loading was significantly reduced by introducing compressive strain hold dwell. Life prediction under equibiaxial fatigue loading with the compressive strain hold was successfully made by the nonlinear damage accumulation model. This suggests that the proposed method can be applied to life prediction of the gas turbine DS blades, which are subjected to biaxial fatigue loading during operation.

scholarly journals A Remaining Useful Life Prediction Method Considering the Dimension Optimization and the Iterative Speed

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 180383-180394 ◽  
Author(s):  
Yiming Li ◽  
Xiangmin Meng ◽  
Zhongchao Zhang ◽  
Guiqiu Song
Keyword(s):  
Life Prediction ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Useful Life
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