作者机构：
[Li, Biwen] Univ South China, Sch Nucl Sci & Technol, Hengyang, Hunan, Peoples R China.;[Zhang, Chunliang; Li, Biwen; Hu, Liangbin] Univ South China, Sch Mech Engn, Hengyang, Hunan, Peoples R China.;[Xiao, Jinfeng] Univ South China, Sch Elect Engn, Hengyang, Hunan, Peoples R China.
通讯机构：
[Li, Biwen] U;Univ South China, Sch Nucl Sci & Technol, Hengyang, Hunan, Peoples R China.
会议名称：
International Conference on Manufacturing Engineering and Automation
会议时间：
DEC 07-09, 2010
会议地点：
Guangzhou, PEOPLES R CHINA
会议主办单位：
[Li, Biwen] Univ South China, Sch Nucl Sci & Technol, Hengyang, Hunan, Peoples R China.^[Li, Biwen;Zhang, Chunliang;Hu, Liangbin] Univ South China, Sch Mech Engn, Hengyang, Hunan, Peoples R China.^[Xiao, Jinfeng] Univ South China, Sch Elect Engn, Hengyang, Hunan, Peoples R China.
会议论文集名称：
Advanced Materials Research
关键词：
Error prediction;Herringbone gear;Noise reduction;Nuclear power turbine;Slotting cutter
摘要：
Relative to gear shaping and gear hob, using gear slotting produces herringbone gear which is used in nuclear power turbine speed redactor has more obvious technical and economic benefits, but profile angle error of slotting cutter causes profile error and base pitch deviation of herringbone gear. It will result in high-frequency noise which damages the tactical and technical performance of warship. By analyzed the wire cutting system of rack cutter used in MAAG type gear machining for herringbone gear, the mathematical model of error prediction for rack cutter profile angle was founded. On the base of model of error prediction, the principle of error control and the method of revision control are presented. Finally, the example of prediction and control is provided.
摘要：
In this paper, an approach of rolling bearing fault diagnosis based on artificial immune system (AIS) is presented. The features extracted from vibration signals are normalized as the original antigens, and an advanced clone selection algorithm (CSA) is applied to train the antibodies. Then use the antibody set to recognize the faults online. The experiments on rolling bearing show the approach is effective and feasible.
会议名称：
International Conference on Manufacturing Engineering and Automation
会议时间：
DEC 07-09, 2010
会议地点：
Guangzhou, PEOPLES R CHINA
会议主办单位：
[Li, Jian] Univ S China Hengyang, Sch Mech Engn, Hengyang 421001, Peoples R China.^[Zhang, Chunliang] Univ Guangzhou, Sch Mech & Elect Engn, Guangzhou PT-510006, Guangdong, Peoples R China.^[Yue, Xia] S China Univ Technol, Sch Mech & Automot Engn, Guangzhou 510641, Guangdong, Peoples R China.
会议论文集名称：
Advanced Materials Research
关键词：
Data compression;Partitioned iterated function systems;Vibration signals
摘要：
The partitioned iterated function systems (PIFS) were introduced into the compression of vibration signal. The actual vibration data of ZLH600-2 pump were adopted to verify the performance of PIFS. Also the compression ratios and the computation time were good, but the spectrum and amplitude, as important performances, were deformed after compression. If the concerned frequency of users was significantly lower than the sampling frequency and the required compression ratios was not more than 2, the compression using PIFS in vibration signal of rotating machinery was comfortable. Otherwise, the information loss in the compression could not be ignored. The decoded signals with the different compression parameters were listed in the paper and it was a meaningful exploration of the IFS on diagnosis and laid the foundation for further research.
会议名称：
International Conference on Manufacturing Engineering and Automation
会议时间：
DEC 07-09, 2010
会议地点：
Guangzhou, PEOPLES R CHINA
会议主办单位：
[Li, Sheng] Univ S China, Sch Mech Engn, Hengyang 421001, Peoples R China.^[Zhang, Chunliang] Guangzhou Univ, Sch Mechan & Elect Engn, Guangzhou 510006, Guangdong, Peoples R China.^[Yue, Xia] S China Univ Technol, Sch Mechan & Automot Engn, Guangzhou 510641, Guangdong, Peoples R China.
会议论文集名称：
Advanced Materials Research
关键词：
Fault Diagnosis;Feature Selection;Fisher’s Discriminant Ratio (FDR);Support Vector Machine (SVM)
摘要：
To effectively avoid the loss of useful information, in this paper, feature information has been extracted from the fault signal of rotating machinery in different aspects such as amplitude-domain, time-domain and time-frequency domain. Then, for the multi-dimensional feature extraction was prone to the problem of “dimension disaster”, the principles of FDR was introduced in data mining to determine the classification ability of each individual feature, and the cross correlation coefficient was adopted to solve the problem that dealing with individual feature. Neglected the interrelationship between the features, a new feature selection algorithm was constructed. Finally, the eigenvectors were used for training and recognizing of SVM model. The experimental results showed the fault diagnosis system was valid and robust.
