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.

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.

scholarly journals Sensuator: A Hybrid Sensor–Actuator Approach to Soft Robotic Proprioception Using Recurrent Neural Networks

Actuators ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 30
Author(s):  
Pornthep Preechayasomboon ◽  
Eric Rombokas
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Linear Models ◽  
Open Loop ◽  
Proof Of Concept ◽  
State Estimator ◽  
Loop Control ◽  
Practical Applications ◽  
Soft Actuator ◽  
The Cost

Soft robotic actuators are now being used in practical applications; however, they are often limited to open-loop control that relies on the inherent compliance of the actuator. Achieving human-like manipulation and grasping with soft robotic actuators requires at least some form of sensing, which often comes at the cost of complex fabrication and purposefully built sensor structures. In this paper, we utilize the actuating fluid itself as a sensing medium to achieve high-fidelity proprioception in a soft actuator. As our sensors are somewhat unstructured, their readings are difficult to interpret using linear models. We therefore present a proof of concept of a method for deriving the pose of the soft actuator using recurrent neural networks. We present the experimental setup and our learned state estimator to show that our method is viable for achieving proprioception and is also robust to common sensor failures.

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.

Constructing Expert System to Automatic Translation for Software development

2018 ◽  
Vol 2 (2) ◽  
pp. 231-247
Author(s):  
Safwan Hasoon ◽  
Fatima Younis
Keyword(s):  
Neural Networks ◽  
Expert System ◽  
Radial Basis Function Network ◽  
Back Propagation ◽  
Transition Process ◽  
Back Propagation Neural Network ◽  
Knowledge Bases ◽  
Translation Process ◽  
Automatic Translation ◽  
The Cost

the development in computer fields, especially in the software engineering, emerged the need to construct intelligence tool for automatic translation from design phase to coding phase, for producing the source code from the algorithm model represented in pseudo code, and execute it depending on the constructing expert system which reduces the cost, time and errors that may occur during the translation process, which has been built the knowledge base, inference engine, and the user interface. The knowledge bases consist of the facts and the rules for the automatic transition. The results are compared with a set of neural networks, which are Back propagation neural network, Cascade-Forward network, and Radial Basis Function network. The results showed the superiority of the expert system in automatic transition process speed, as well as easy to add, delete or modify process for rules or data of the pseudo code compared with previously mentioned neural networks.

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