Carriage Error Identification Based on Cross-Correlation Analysis and Wavelet Transformation
This paper proposes a novel method for identifying carriage errors. A general mathematical model of a guideway system is developed, based on the multi-body system method. Based on the proposed model, most error sources in the guideway system can be measured. The flatness of a workpiece measured by t...
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2012-07-01
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Online Access: | http://www.mdpi.com/1424-8220/12/7/9551 |
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doaj-42333f31b9a64102b7937d8023505ca32020-11-24T23:58:56ZengMDPI AGSensors1424-82202012-07-011279551956510.3390/s120709551Carriage Error Identification Based on Cross-Correlation Analysis and Wavelet TransformationXiaofeng WangFeihu ZhangDonghui MuDongju ChenJinwei FanThis paper proposes a novel method for identifying carriage errors. A general mathematical model of a guideway system is developed, based on the multi-body system method. Based on the proposed model, most error sources in the guideway system can be measured. The flatness of a workpiece measured by the PGI1240 profilometer is represented by a wavelet. Cross-correlation analysis performed to identify the error source of the carriage. The error model is developed based on experimental results on the low frequency components of the signals. With the use of wavelets, the identification precision of test signals is very high.http://www.mdpi.com/1424-8220/12/7/9551carriage errormulti-body systemerror identificationcross-correlation analysiswavelet transform |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiaofeng Wang Feihu Zhang Donghui Mu Dongju Chen Jinwei Fan |
spellingShingle |
Xiaofeng Wang Feihu Zhang Donghui Mu Dongju Chen Jinwei Fan Carriage Error Identification Based on Cross-Correlation Analysis and Wavelet Transformation Sensors carriage error multi-body system error identification cross-correlation analysis wavelet transform |
author_facet |
Xiaofeng Wang Feihu Zhang Donghui Mu Dongju Chen Jinwei Fan |
author_sort |
Xiaofeng Wang |
title |
Carriage Error Identification Based on Cross-Correlation Analysis and Wavelet Transformation |
title_short |
Carriage Error Identification Based on Cross-Correlation Analysis and Wavelet Transformation |
title_full |
Carriage Error Identification Based on Cross-Correlation Analysis and Wavelet Transformation |
title_fullStr |
Carriage Error Identification Based on Cross-Correlation Analysis and Wavelet Transformation |
title_full_unstemmed |
Carriage Error Identification Based on Cross-Correlation Analysis and Wavelet Transformation |
title_sort |
carriage error identification based on cross-correlation analysis and wavelet transformation |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2012-07-01 |
description |
This paper proposes a novel method for identifying carriage errors. A general mathematical model of a guideway system is developed, based on the multi-body system method. Based on the proposed model, most error sources in the guideway system can be measured. The flatness of a workpiece measured by the PGI1240 profilometer is represented by a wavelet. Cross-correlation analysis performed to identify the error source of the carriage. The error model is developed based on experimental results on the low frequency components of the signals. With the use of wavelets, the identification precision of test signals is very high. |
topic |
carriage error multi-body system error identification cross-correlation analysis wavelet transform |
url |
http://www.mdpi.com/1424-8220/12/7/9551 |
work_keys_str_mv |
AT xiaofengwang carriageerroridentificationbasedoncrosscorrelationanalysisandwavelettransformation AT feihuzhang carriageerroridentificationbasedoncrosscorrelationanalysisandwavelettransformation AT donghuimu carriageerroridentificationbasedoncrosscorrelationanalysisandwavelettransformation AT dongjuchen carriageerroridentificationbasedoncrosscorrelationanalysisandwavelettransformation AT jinweifan carriageerroridentificationbasedoncrosscorrelationanalysisandwavelettransformation |
_version_ |
1725448830132944896 |