Prognosis of Blade Material Fatigue Using Elman Neural Networks

2007 ◽  
Vol 10-12 ◽  
pp. 558-562
Author(s):  
Ji Hong Yan ◽  
P.X. Wang
Keyword(s):  
Neural Networks ◽  
Material Testing ◽  
Testing System ◽  
Material Degradation ◽  
Trend Prediction ◽  
Damage Propagation ◽  
Material Fatigue ◽  
Fatigue Data ◽  
Fatigue Severity ◽  
Propagation Prediction

Prognosis of major components such as blades, rotors, valves of steam turbine is crucial to reducing operating and maintenance costs. Prognostic strategies can assist to detect, classify and predict developing faults, guarantee reliable, efficient and continuous operation of electric plants, and may even result in saving lives. In this paper, a recurrent neural network based strategy was developed for blade material degradation assessment and fatigue damage propagation prediction. Two Elman Neural Networks were developed for fatigue severity assessment and trend prediction correspondingly. The performance of the proposed prognostic methodology was evaluated by using blade material fatigue data collected from a material testing system. The prognostic method is found to be a reliable and robust material fatigue predictor.

Blade Material Fatigue Assessment Using Elman Neural Networks

2007 ◽  
Author(s):  
Jihong Yan ◽  
Pengxiang Wang
Keyword(s):  
Neural Networks ◽  
Testing System ◽  
Steam Turbines ◽  
Material Degradation ◽  
Trend Prediction ◽  
Damage Propagation ◽  
Material Fatigue ◽  
Fatigue Severity ◽  
Propagation Prediction ◽  
The Cost

Material degradation evaluation and life prediction of major components such as blades, rotors, valves of steam turbines not only guarantees reliable, efficient and continuous operation of electric plants, but also offers the promise of substantially reducing the cost of repair and replacement of defective parts, and may even result in saving lives. In this paper, a recurrent neural network based strategy was developed for material degradation assessment and fatigue damage propagation prediction. Two Elman Neural Networks were developed for fatigue severity assessment and trend prediction correspondingly. The performance of the proposed prognostic methodology was evaluated by using blade material fatigue data collected from a material testing system. The prognostic method is found to be a reliable and robust material fatigue predictor.

Design of NRL66.4: An Electro-Hydraulic 6-DoF Parallel Robotic Multiaxial Material Testing System

2019 ◽  
Author(s):  
John G. Michopoulos ◽  
Athanasios P. Iliopoulos ◽  
John C. Steuben ◽  
Benjamin D. Graber
Keyword(s):  
Degrees Of Freedom ◽  
Material Testing ◽  
Multiaxial Fatigue ◽  
Current Status ◽  
Testing System ◽  
Damage Propagation ◽  
Propagation Modeling ◽  
Multiaxial Testing ◽  
Reaction Forces ◽  
6 Degrees Of Freedom

Abstract Contemporary material testing applications such as high throughput material testing under realistic conditions, emulation of in-service loading conditions for the qualification of additively manufactured parts, material failure and damage propagation modeling validation and material constitutive characterization, are all underscoring the demand for an automated multiaxial testing capability. In order to address these needs, the present work introduces the initial progress of the design and prototyping of a 6 degrees of freedom (6-DoF) robotic system to be used as such a testing infrastructure. This system is designed to be capable of enforcing 6-DoF kinematic or force controlled boundary conditions on deformable material specimens, while at the same time measuring both the imposed kinematics and the corresponding reaction forces in a fully automated manner. Furthermore, as an extension to our previously prototyped systems, the system proposed here is designed to apply both quasi-static loading but also cyclic loading for enabling multiaxial fatigue studies. In addition to the architecture, the design and current status of its implementation for the most critical sub-systems is presented.

scholarly journals Identification of Mode Shapes of a Composite Cylinder Using Convolutional Neural Networks

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 2801
Author(s):  
Bartosz Miller ◽  
Leonard Ziemiański
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Composite Structures ◽  
Three Dimensional ◽  
Mode Shapes ◽  
Identification Algorithm ◽  
Material Degradation ◽  
Local Material ◽  
Noisy Input ◽  
Shape Vector

The aim of the following paper is to discuss a newly developed approach for the identification of vibration mode shapes of multilayer composite structures. To overcome the limitations of the approaches based on image analysis (two-dimensional structures, high spatial resolution of mode shapes description), convolutional neural networks (CNNs) are applied to create a three-dimensional mode shapes identification algorithm with a significantly reduced number of mode shape vector coordinates. The CNN-based procedure is accurate, effective, and robust to noisy input data. The appearance of local damage is not an obstacle. The change of the material and the occurrence of local material degradation do not affect the accuracy of the method. Moreover, the application of the proposed identification method allows identifying the material degradation occurrence.

scholarly journals Detection of Material Degradation of a Composite Cylinder Using Mode Shapes and Convolutional Neural Networks

Materials ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6686
Author(s):  
Bartosz Miller ◽  
Leonard Ziemiański
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Composite Structures ◽  
Vibration Mode ◽  
Numerical Study ◽  
Mode Shapes ◽  
Zone Size ◽  
Composite Cylinder ◽  
Material Degradation ◽  
Damaged Zone

This paper presents a numerical study of the feasibility of using vibration mode shapes to identify material degradation in composite structures. The considered structure is a multilayer composite cylinder, while the material degradation zone is, for simplicity, considered a square section of the lateral surface of the cylinder. The material degradation zone size and location along the cylinder axis are identified using a deep learning approach (convolutional neural networks, CNNs, are applied) on the basis of previously identified vibration mode shapes. The different numbers and combinations of identified mode shapes used to assess the damaged zone size and location were analyzed in detail. The final selection of mode shapes considered in the identification procedure yielded high accuracy in the identification of the degradation zone.

Biomechanics of the wrist after proximal row carpectomy in cadavers

2009 ◽  
Vol 35 (1) ◽  
pp. 43-45 ◽  
Author(s):  
Y.-L. Zhu ◽  
Y.-Q. Xu ◽  
J. Ding ◽  
J. Li ◽  
B. Chen ◽  
...  
Keyword(s):  
Sharp Decrease ◽  
Material Testing ◽  
Testing System ◽  
Average Pressure ◽  
Proximal Row Carpectomy ◽  
Pressure Sensitive ◽  
Sensitive Film ◽  
Fresh Frozen ◽  
Pressure Sensitive Film ◽  
Lunate Fossa

We investigated the biomechanics of the radiocapitate joint after a proximal row carpectomy in six fresh-frozen cadaver wrists using super-low-pressure-sensitive film on a material testing system. The average pressure within the lunate fossa increased significantly from 23.2 to 136.4 N/cm2 with a sharp decrease in the contact area from 2.08 to 0.30 cm2 after a proximal row carpectomy. The cartilage of the proximal capitate had four sub-facets and therefore was not as smooth as the normal proximal lunate. We found that the wrist was overloaded after a proximal row carpectomy and the main cause was the anatomical mismatch of the radiocapitate articulation.

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