Health Assessment of Aircraft Based on Wavelet Neural Network

2013 ◽  
Vol 756-759 ◽  
pp. 4581-4585
Author(s):  
Chen Guang Zhao ◽  
Xu Ping Qi ◽  
Chen Ming Mi ◽  
Teng Fei Miao
Keyword(s):  
Neural Network ◽  
Health Assessment ◽  
Prediction Method ◽  
Wavelet Neural Network ◽  
Fault Prediction ◽  
Health State ◽  
Learning Speed ◽  
Concept Of Health ◽  
Heavy Duty Gas Turbine ◽  
The Mathematical Model

Health assessment is one of the key technology in the aircraft operating system. Aiming at the characteristic of aircraft structure, the aircraft fault prediction method based on data mining is presented in this paper. The concept of health assessment is introduced first, the wavelet neural network provide the mathematical model reflecting aircraft health state. The experiment results show that the health prediction applying wavelet neural network works well with high fidelity and real time. Focusing at a typical heavy-duty gas turbine, the critical information collected by the sensor is applied as the network input, then the wavelet neural network is constructed, the quick training and learning speed is proved. The results indicate proposed approach is promising for reliable diagnostics of aircraft.

Fault Prediction Method of the Marine Gas Turbine Based on Neural Network-Markov

2014 ◽  
Vol 538 ◽  
pp. 171-174 ◽  
Author(s):  
Jian Guo Cui ◽  
Long Zhang ◽  
Gui Hua Wang ◽  
Bo Cui ◽  
Li Ying Jiang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Gas Turbine ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Fault Prediction ◽  
Health State ◽  
Object A ◽  
The Neural Network ◽  
Specific Object

Since the fault of marine gas turbine is difficult to predict accurately, making the rolling bearing as the specific object, a fault prediction model of the marine gas turbine based on Neural Network and Markov method is built through the data analysis, preprocessing and feature extraction for the rolling bearing history test data. First, it uses the neural network method to realize the health state recognition of the marine gas turbine. Then, the fault of the marine gas turbine is predicted by taking advantage of the fault prediction which is based on the Markov model. The results show that the efficiency of fault prediction for the marine gas turbine can be realized better through the fault prediction model constructed in view of the Neural Network and Markov. And it also has a significant practical value in project item.

scholarly journals Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1094 ◽  
Author(s):  
Lanjun Wan ◽  
Hongyang Li ◽  
Yiwei Chen ◽  
Changyun Li
Keyword(s):  
Neural Network ◽  
Working Conditions ◽  
Bp Neural Network ◽  
Evidence Theory ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Fault Prediction ◽  
Fault Classification ◽  
Bearing Fault ◽  
Hidden Layer

To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster–Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster–Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.

scholarly journals Research on bus elastic departure interval based on Wavelet Neural Network

2021 ◽  
Vol 261 ◽  
pp. 03052
Author(s):  
Zhe Lv ◽  
Jiayu Zou ◽  
Zhongyu Zhao
Keyword(s):  
Neural Network ◽  
Prediction Method ◽  
Wavelet Neural Network ◽  
Economic Costs ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Intelligent Optimization Algorithm ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Important Basis

In recent years, more and more people choose to travel by bus to save time and economic costs, but the problem of inaccurate bus arrival has become increasingly prominent. The reason is the lack of scientific planning of departure time. This paper takes the passenger flow as an important basis for departure interval, proposes a passenger flow prediction method based on wavelet neural network, and uses intelligent optimization algorithm to study the bus elastic departure interval. In this paper, the wavelet neural network prediction model and the elastic departure interval optimization model are established, and then the model is solved by substituting the data, and finally the theoretical optimal departure interval is obtained.

Fault Prediction of Pitch Actuator for Wind Turbines

2014 ◽  
Vol 721 ◽  
pp. 397-401
Author(s):  
Hong Shan Zhao ◽  
Sha Sha Lian ◽  
Ling Shao
Keyword(s):  
Wind Turbines ◽  
Prediction Method ◽  
Fault Isolation ◽  
Fault Prediction ◽  
False Alarms ◽  
Controlled System ◽  
Pitch Control ◽  
Prediction Scheme ◽  
The Impact ◽  
The Mathematical Model

Hydraulic pitch-controlled system is one of the components of wind turbines which are frequently prone to faults. Early fault prediction of the pitch control system can improve the operation reliability effectively and reduce the unnecessary loss. Wind turbines suffer much environmental interference; moreover, data-based fault prediction is vulnerable to occur false alarms by the impact of these factors. And it is difficult to implement the fault isolation. So this paper presents a fault prediction method for the pitch-controlled system, which is based on the mathematical model of wind turbines physical properties. The residual root mean square (RMS) is used as residual evaluation function. In the end of the paper, by the simulation using the hydraulic pitch actuator fault as the example, the effectiveness of the proposed fault prediction scheme is verified.

Wavelet Neural Network Based on Chaos Genetic Algorithm

2013 ◽  
Vol 339 ◽  
pp. 307-312 ◽  
Author(s):  
Hu Cheng Zhao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Learning Algorithm ◽  
Wavelet Neural Network ◽  
Optimal Parameters ◽  
Chaos Optimization ◽  
Chaos Optimization Algorithm ◽  
Learning Speed ◽  
Chaos Genetic Algorithm ◽  
Remarkable Progress

To improve the performance of Wavelet Neural Network (WNN), a hybrid WNN learning algorithm, which is combination of Genetic Algorithm (GA) and Chaos Optimization Algorithm (COA) in a mutual complementarity manner, is proposed. In the algorithm, GA is first used to roughly search the optimal parameters of WNN as a whole, and then COA is adopted to perform the refined search on the basis of the result obtained by GA, which can make remarkable progress in modeling accuracy, learning speed, and overcoming local convergence or precocity. Simulation show its effectiveness.

A New Prediction Method for Chaotic Time Series

2013 ◽  
Vol 380-384 ◽  
pp. 1673-1676
Author(s):  
Juan Du
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Cumulative Effect ◽  
Training Data ◽  
Training Algorithm ◽  
Learning Speed ◽  
Sunspot Data

In order to show the time cumulative effect in the process for the time series prediction, the process neural network is taken. The training algorithm of modified particle swarm is used to the model for the learning speed. The training data is sunspot data from 1700 to 2007. Simulation result shows that the prediction model and algorithm has faster training speed and prediction accuracy than the artificial neural network.

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