Flame Burning Condition Recognition Based on Improved ART Neural Network

2011 ◽  
Vol 3 (4) ◽  
pp. 32-40 ◽  
Author(s):  
Jinxue Sui ◽  
Li Yang ◽  
Zhilin Zhu
Keyword(s):  
Neural Network ◽  
Condition Recognition

scholarly journals Multi-Factor Operating Condition Recognition Using 1D Convolutional Long Short-Term Network

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5488 ◽  
Author(s):  
Zhinong Jiang ◽  
Yuehua Lai ◽  
Jinjie Zhang ◽  
Haipeng Zhao ◽  
Zhiwei Mao
Keyword(s):  
Neural Network ◽  
Diesel Engine ◽  
Mean Value ◽  
Fault Detection And Diagnosis ◽  
Operating Conditions ◽  
Short Term ◽  
Vibration Signals ◽  
Operating Condition ◽  
Model Training ◽  
Condition Recognition

For a diesel engine, operating conditions have extreme importance in fault detection and diagnosis. Limited to various special circumstances, the multi-factor operating conditions of a diesel engine are difficult to measure, and the demand of automatic condition recognition based on vibration signals is urgent. In this paper, multi-factor operating condition recognition using a one-dimensional (1D) convolutional long short-term network (1D-CLSTM) is proposed. Firstly, a deep neural network framework is proposed based on a 1D convolutional neural network (CNN) and long short-Term network (LSTM). According to the characteristics of vibration signals of a diesel engine, batch normalization is introduced to regulate the input of each convolutional layer by fixing the mean value and variance. Subsequently, adaptive dropout is proposed to improve the model sparsity and prevent overfitting in model training. Moreover, the vibration signals measured under 12 operating conditions were used to verify the performance of the trained 1D-CLSTM classifier. Lastly, the vibration signals measured from another kind of diesel engine were applied to verify the generalizability of the proposed approach. Experimental results show that the proposed method is an effective approach for multi-factor operating condition recognition. In addition, the adaptive dropout can achieve better training performance than the constant dropout ratio. Compared with some state-of-the-art methods, the trained 1D-CLSTM classifier can predict new data with higher generalization accuracy.

Research of Tool Wear Condition Recognition Diagnosis System Based on the Machined Workpiece Surface Texture Image

2011 ◽  
Vol 130-134 ◽  
pp. 2508-2512
Author(s):  
Wei Dong Xu ◽  
Xiao Hong Ren ◽  
Li Jie Li ◽  
Ying Gao Yue
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Surface Texture ◽  
Texture Feature ◽  
Texture Characteristic ◽  
Workpiece Surface ◽  
Diagnosis System ◽  
Diagnosis Rate ◽  
Wear Condition ◽  
Condition Recognition

Aiming at the machined workpiece surface texture images,some technology about image pre-processing and the texture feature extraction based on gray level co-occurrence matrix are researched. Then it is time for the analysis of the texture characteristic parameters based on BP neural network and the identification and diagnosis of tool wear state, Finally the recognition diagnosis system interface is designed by Matlab-GUI.System simulation shows that the interface fusion of image processing and neural network is a good way to ensure the realization of tool wear condition recognition,what’more, the identification diagnosis rate is profect.

scholarly journals Multifeatures Fusion and Nonlinear Dimension Reduction for Intelligent Bearing Condition Monitoring

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Liang Guo ◽  
Hongli Gao ◽  
Haifeng Huang ◽  
Xiang He ◽  
ShiChao Li
Keyword(s):  
Neural Network ◽  
Dimension Reduction ◽  
Frequency Domain ◽  
Deep Neural Network ◽  
Features Selection ◽  
Time Frequency ◽  
Nonlinear Dimension Reduction ◽  
Bearing Condition Monitoring ◽  
Nonlinear Dimension ◽  
Condition Recognition

Condition-based maintenance is critical to reduce the costs of maintenance and improve the production efficiency. Data-driven method based on neural network (NN) is one of the most used models for mechanical components condition recognition. In this paper, we introduce a new bearing condition recognition method based on multifeatures extraction and deep neural network (DNN). First, the method calculates time domain, frequency domain, and time-frequency domain features to represent characteristic of vibration signals. Then the nonlinear dimension reduction algorithm based on deep learning is proposed to reduce the redundancy information. Finally, the top-layer classifier of deep neural network outputs the bearing condition. The proposed method is validated using experiment test-bed bearing vibration data. Meanwhile some comparative studies are performed; the results show the advantage of the proposed method in adaptive features selection and superior accuracy in bearing condition recognition.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural
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