A two-stage reliability allocation method for remanufactured machine tools integrating neural networks and remanufacturing coefficient

2021 ◽  
pp. 107834
Author(s):  
Yanbin Du ◽  
Guoao Wu ◽  
Ying Tang ◽  
Shihao Liu
Keyword(s):  
Neural Networks ◽  
Machine Tools ◽  
Two Stage ◽  
Reliability Allocation ◽  
Allocation Method

Reliability Analysis of CNC Machine Tools Based on Fuzzy Theory

2015 ◽  
Vol 713-715 ◽  
pp. 7-10 ◽  
Author(s):  
Li Tao
Keyword(s):  
Machine Tools ◽  
Comprehensive Evaluation ◽  
Fuzzy Theory ◽  
Fuzzy Comprehensive Evaluation ◽  
Cnc Machine Tools ◽  
Uncertain Information ◽  
Cnc Machine ◽  
Allocation Model ◽  
Reliability Allocation ◽  
Allocation Method

There are many factors influencing the reliability allocation of CNC machine tools and some are difficult to analyze quantitatively, a comprehensive reliability allocation method is proposed using interval analysis, fuzzy comprehensive evaluation and analytic hierarchy process. Many influence factors were considered and endowed with different weight respectively according to the influence degree. In this paper, a reliability allocation model is proposed for CNC machine tools, which used interval number instead of real number to express uncertain information. It could make use of the field test information and experts’ experience comprehensively. Finally, the application of this method is illustrated with an example, and the results showed that the allocation method is feasible and effective for the reliability allocation of CNC machine tools.

Minimum Effort Reliability Allocation Method Considering Fuzzy Cost of Punching Machine Tools

2013 ◽  
Vol 13 (20) ◽  
pp. 4107-4113 ◽  
Author(s):  
Wang Ji-li ◽  
Yang Zhao-jun ◽  
Chen Fei ◽  
Li Guo-fa ◽  
Chen Chuan-hai
Keyword(s):  
Machine Tools ◽  
Reliability Allocation ◽  
Allocation Method ◽  
Fuzzy Cost

scholarly journals Patch-Based Change Detection Method for SAR Images with Label Updating Strategy

2021 ◽  
Vol 13 (7) ◽  
pp. 1236
Author(s):  
Yuanjun Shu ◽  
Wei Li ◽  
Menglong Yang ◽  
Peng Cheng ◽  
Songchen Han
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Change Detection ◽  
Detection Method ◽  
Training Data ◽  
Synthetic Aperture ◽  
Learning Method ◽  
Sar Images ◽  
Two Stage ◽  
Second Stage

Convolutional neural networks (CNNs) have been widely used in change detection of synthetic aperture radar (SAR) images and have been proven to have better precision than traditional methods. A two-stage patch-based deep learning method with a label updating strategy is proposed in this paper. The initial label and mask are generated at the pre-classification stage. Then a two-stage updating strategy is applied to gradually recover changed areas. At the first stage, diversity of training data is gradually restored. The output of the designed CNN network is further processed to generate a new label and a new mask for the following learning iteration. As the diversity of data is ensured after the first stage, pixels within uncertain areas can be easily classified at the second stage. Experiment results on several representative datasets show the effectiveness of our proposed method compared with several existing competitive methods.

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