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

2021 ◽  
Author(s):  
Zheyuan Zhang ◽  
Tianyuan Liu ◽  
Di Zhang ◽  
Yonghui Xie
Keyword(s):  
Machine Learning ◽  
Steam Turbine ◽  
Turbine Blade ◽  
Image Recognition ◽  
Water Droplet ◽  
Life Prediction ◽  
Prediction Method ◽  
Water Droplet Erosion ◽  
Steam Turbine Blade

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.

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

2021 ◽  
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 (?) is defined. The life coefficient (?) 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.

scholarly journals Experimental Research on Water Droplet Erosion Resistance Characteristics of Turbine Blade Substrate and Strengthened Layers Materials

Materials ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 4286
Author(s):  
Juan Di ◽  
Shunsen Wang ◽  
Xiaojiang Yan ◽  
Xihang Jiang ◽  
Jinyi Lian ◽  
...  
Keyword(s):  
Solid Solution ◽  
Steam Turbine ◽  
Turbine Blade ◽  
Laser Cladding ◽  
Water Droplet ◽  
Positive Contribution ◽  
Water Droplet Erosion ◽  
Stellite Alloy ◽  
New Findings ◽  
Starting Point

In this paper, the water droplet erosion (WDE) performance of typical martensitic precipitation substrate 0Cr17Ni4Cu4Nb in steam turbine final stage, laser solid solution strengthened sample, laser cladding sample and brazed stellite alloy samples have been studied based on a high-speed rotating waterjet test system. The WDE resistance of several materials from strong to weak is in sequence: Brazed stellite alloy > laser cladding sample > laser solid solution sample > martensitic substrate. Furthermore, the WDE resistance mechanism and the failure mode of brazed stellite alloy have been revealed. It is found that the hard carbide in the stellite alloy is the starting point of crack formation and propagation. Under the continuous droplet impact, cracks grow and connect into networks, resulting in the removal of carbide precipitates and WDE damage. It is proved that the properties of the Co-based material itself is the reason for its excellent WDE resistance. And the carbides have almost no positive contribution to its anti-erodibility. These new findings are of great significance to process methods and parameter selection of steam turbine blade materials and surface strengthened layers.

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
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