scholarly journals Time-Frequency analysis using Bayesian Regularized Neural Network Model

10.5772/46963 ◽  
2010 ◽  
Author(s):  
Imran Shafi ◽  
Jamil Ahmad ◽  
Syed Ismail ◽  
Ataul Aziz
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Frequency Analysis ◽  
Neural Network Model ◽  
Time Frequency Analysis ◽  
Time Frequency

Multi-steps degradation process prediction for bearing based on improved back propagation neural network

2012 ◽  
Vol 227 (7) ◽  
pp. 1544-1553 ◽  
Author(s):  
Lin Mi ◽  
Wei Tan ◽  
Ran Chen
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
Principal Component ◽  
Degradation Process ◽  
Extraction Methods ◽  
Back Propagation Neural Network ◽  
Time Frequency ◽  
Process Prediction

Bearing degradation process prediction is extremely important in industry. This article proposed a new method to achieve multi-steps bearing degradation prediction based on an improved back propagation neural network model. Firstly, time domain and time–frequency domain features extraction methods are employed to extract the original features from the mass vibration signals. However, the extracted original features still with high dimensional and include superfluous information, the multi-features fusion technique principal component analysis is used to merge the original features and reduce the dimension, the typical sensitive features can be extracted. Then, based on the extracted features, the improved three-layer back propagation neural network model is constructed and trained for multi-steps bearing degradation process prediction. The phase space construction method is used to determine the embedding dimension of the back propagation neural network model. An accelerated bearing run-to-failure experiment was carried out, the results proved the effectiveness of the methodology.

Application of Time-Frequency Analysis and Back-Propagation Neural Network in the Lung Sound Signal Recognition

2012 ◽  
Vol 190-191 ◽  
pp. 927-930
Author(s):  
Mei Yung Chen ◽  
Chien Chou Huang
Keyword(s):  
Neural Network ◽  
Frequency Analysis ◽  
Recognition Rate ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Visual Signal ◽  
Lung Sound ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Breath Sounds

In the diagnosis of the respiratory diseases, auscultation is a non-invasive and convenient diagnostic method. In the digital auscultation analysis, what method we use to analyze the lung signals which microphone recorded will affect the results of the experiment greatly. The purpose of this study is to use frequency analysis and time-frequency analysis to analyze the six lung sound signals, which are vesicular breath sounds, bronchial breath sounds, crackle, and wheeze. Finally, the study transformed the analysis results into the characteristic images, and put them to the back propagation neural network for training. After that, the study compares the results of the two methods. We also analyze the realistic lung sound signals and simulated lung sound signals, and compare the results finally. First, we use the piezoelectric microphone and data acquisition card NI-PXI 4472B to acquire LS signals, and signals preprocessing. Then we use Visual Signal to analyze the lung sound signals by time-frequency analysis. We also analyze the lung sound signals which are from the auscultation teaching website. Finally we compare the result of two kinds of signals, and assess their similarity and accuracy by the test of back-propagation neural network. According to the result of this study, we found that time-frequency analysis provide much information about the lung signals, and are more suitable as a basis of diagnosis, and increase the recognition rate of the back-propagation neural network.

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