Nondestructive detection of chilled mutton freshness based on multi-label information fusion and adaptive BP neural network

2018 ◽  
Vol 155 ◽  
pp. 371-377 ◽  
Author(s):  
Jiang Xinhua ◽  
Xue Heru ◽  
Zhang Lina ◽  
Gao Xiaojing ◽  
Wu Guodong ◽  
...  
Keyword(s):  
Neural Network ◽  
Information Fusion ◽  
Bp Neural Network ◽  
Nondestructive Detection ◽  
Label Information

Multisensor Information Fusion Based on D-S Evidence Theory and BP Neural Network

2013 ◽  
Vol 567 ◽  
pp. 113-117 ◽  
Author(s):  
Can Zhao ◽  
C.R. Tang ◽  
S. Wan
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Cutting Force ◽  
Information Fusion ◽  
Bp Neural Network ◽  
Cutting Tool ◽  
Evidence Theory ◽  
Vibration Signal ◽  
Inspection System ◽  
Tool Monitoring

This paper applies the information fusion technology to tool monitoring. As one of the most important processing factor, the cutting tool and the tool wear directly influence size precision. Signals of cutting force and vibration are measured with multi-sensor. By using multi-sensor the drawbacks can be overcome, the multi-sensor information fusion mentioned in manufacture stands for extracting kinds of information from different sensors (especially for cutting force and vibration signal in this paper), making best use of all resources，according to certain criterion to judge the spatial and time redundancy , to make the system more comprehensive. Two data fusion methods, which are BP Neural Network and Wavelet Neural Network for predicting tool wear, and are debated. By the hybrid of BP and wavelet based neural network the cutting tool status inspection system is built so that the forecast of tool wear can be achieved. The results show experimentally two of these presented methods effectively implement tool wear monitoring and predicting.

Research on Intelligent Classification System of Ceramic Tiles Based on Machine Vision

2011 ◽  
Vol 101-102 ◽  
pp. 648-651
Author(s):  
Shi Long Li ◽  
Yao Chen
Keyword(s):  
Neural Network ◽  
Information Fusion ◽  
Bp Neural Network ◽  
Classification System ◽  
Shape Features ◽  
Ceramic Tiles ◽  
Color Features ◽  
The Neural Network ◽  
Sensor Information

An intelligent classification system of ceramic tiles is introduced in the light of the theory about multi-sensor information fusion. The system includes image acquisition, image processing and intelligent classification of ceramic tiles. The color features and shape features of tile image are synthetically processed using BP neural network. The topological structure of the neural network based on “681” structure is proposed in the system. The numerical calculation and simulation about classification of ceramic tiles is carried out based on MATLAB software. The results show this algorithm is fast and accurate, which can effectively accomplish the classification of comprehensive detection of ceramic tiles.

Fault Diagnosis Method for the Rolling Bearing Based on Information Fusion and BP Neural Network

2012 ◽  
Vol 538-541 ◽  
pp. 1956-1961 ◽  
Author(s):  
Jin Min Zhang ◽  
Yin Hua Huang ◽  
Si Ming Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Back Propagation Neural Network ◽  
Weighted Fusion ◽  
Diagnosis Method

Abstract. In order to diagnose the fault of rolling bearing by the vibration signal, a new method of fault diagnosis based on weighted fusion and BP (Back Propagation) neural network was put forward. At first, the vibration signal from the sensors was wave filtered through the method of correlation function, then the fused signal was obtained by the classical adaptive weighted fusion method, the multi-type characteristics parameters was to be as a neural network input. Finally, the fault diagnosis of rolling bearing was realized by the BP neural network, and the results show that the multi-sensor information fusion fault diagnosis method can be proved effectively to achieve the fault diagnosis of rolling bearing.

Human-machine-environment information fusion and control compensation strategy for large optical mirror processing system

2020 ◽  
pp. 095440622095968
Author(s):  
Zujin Jin ◽  
Gang Cheng ◽  
Shibiao Chen ◽  
Feng Guo
Keyword(s):  
Neural Network ◽  
Influencing Factors ◽  
Information Fusion ◽  
Bp Neural Network ◽  
Environmental Control ◽  
Processing System ◽  
Parameter Analysis ◽  
Related Factors ◽  
Joint Friction ◽  
Forming Precision

Large optical mirrors require an ultra-precise machining equipment, and a high level of surface-forming precision must be achieved. However, optical mirror processing systems (OMPSs) are susceptible to human behaviors, mechanical structural errors, and processing environments. The factors that affect quality include artificially formulated processes, slurry choice, joint friction, force-induced deformation, ambient temperature, and vibration interference. These factors can lead to a decrease in the accuracy of an OMPS. To study the influence of disturbances in the human-machine-environment (HME) on the OMPS, it is necessary to conduct a fusion analysis of the related factors. A parameter analysis is first conducted on the HME factors that influence the accuracy of OMPS. Then, the factors that influence the accuracy most significantly are determined. Subsequently, with the influencing factors as input parameters, and the output forces of the computer-controlled optical surface (CCOS) grinding system as the output parameters, the HME influencing factors are fused through a BP neural network optimized using a genetic algorithm, and the result is compared with that resulting from the original BP neural network fusion. Finally, according to the results of the fusion, environmental control of the processing system is performed, and the feedforward PD control compensation measures are established for the joint friction. An experimental analysis is also conducted to verify the effect of the information fusion and error compensation on the accuracy of the OMPS.

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