Fault Diagnosis of Rotating Machinery Based on Wavelet Domain Denoising and Metric Distance
In the monitoring process of petrochemical equipment rotating machinery, the collected large data easily lead to valuable data loss in the pre-processing process and affecting the accuracy of the fault diagnosis. This paper proposes a method for the fault diagnosis of the rotating machinery based on...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8731983/ |
| _version_ | 1818877623301832704 |
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| author | Naiquan Su Xiao Li Qianghua Zhang |
| author_facet | Naiquan Su Xiao Li Qianghua Zhang |
| author_sort | Naiquan Su |
| collection | DOAJ |
| description | In the monitoring process of petrochemical equipment rotating machinery, the collected large data easily lead to valuable data loss in the pre-processing process and affecting the accuracy of the fault diagnosis. This paper proposes a method for the fault diagnosis of the rotating machinery based on the wavelet-domain denoising and metric distance. The wavelet-domain denoising uses wavelet coefficients of signal and noise that have different properties on different scales and process noisy signal wavelet coefficients. Metric distance is to compare two independent statistical samples with each other after denoising to determine whether they belong to the same sample. First, the denoising of the vibration time-domain signal is based on the wavelet-domain denoising method. Then, the tested fault samples are compared with the known fault samples by metric distance. Finally, the fault types are identified according to the metric distance. Verification of the algorithm performance and the simulation experiment of petrochemical large units show that the method is not only simple and effective but also has better faults recognition. It can guide the faults diagnosis of large petrochemical units and other large units rotating machinery. |
| first_indexed | 2024-12-19T14:01:14Z |
| format | Article |
| id | doaj.art-c5930b4cef834b5ba2933673e979c13f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T14:01:14Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c5930b4cef834b5ba2933673e979c13f2022-12-21T20:18:27ZengIEEEIEEE Access2169-35362019-01-017732627327010.1109/ACCESS.2019.29209398731983Fault Diagnosis of Rotating Machinery Based on Wavelet Domain Denoising and Metric DistanceNaiquan Su0https://orcid.org/0000-0002-4220-0540Xiao Li1Qianghua Zhang2School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, ChinaSchool of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, ChinaGuangdong Province Key Laboratory of Petrochemical Equipment Fault Diagnosis, Maoming, ChinaIn the monitoring process of petrochemical equipment rotating machinery, the collected large data easily lead to valuable data loss in the pre-processing process and affecting the accuracy of the fault diagnosis. This paper proposes a method for the fault diagnosis of the rotating machinery based on the wavelet-domain denoising and metric distance. The wavelet-domain denoising uses wavelet coefficients of signal and noise that have different properties on different scales and process noisy signal wavelet coefficients. Metric distance is to compare two independent statistical samples with each other after denoising to determine whether they belong to the same sample. First, the denoising of the vibration time-domain signal is based on the wavelet-domain denoising method. Then, the tested fault samples are compared with the known fault samples by metric distance. Finally, the fault types are identified according to the metric distance. Verification of the algorithm performance and the simulation experiment of petrochemical large units show that the method is not only simple and effective but also has better faults recognition. It can guide the faults diagnosis of large petrochemical units and other large units rotating machinery.https://ieeexplore.ieee.org/document/8731983/Fault diagnosiswavelet domain denoisingmetric distancerotating machinery |
| spellingShingle | Naiquan Su Xiao Li Qianghua Zhang Fault Diagnosis of Rotating Machinery Based on Wavelet Domain Denoising and Metric Distance IEEE Access Fault diagnosis wavelet domain denoising metric distance rotating machinery |
| title | Fault Diagnosis of Rotating Machinery Based on Wavelet Domain Denoising and Metric Distance |
| title_full | Fault Diagnosis of Rotating Machinery Based on Wavelet Domain Denoising and Metric Distance |
| title_fullStr | Fault Diagnosis of Rotating Machinery Based on Wavelet Domain Denoising and Metric Distance |
| title_full_unstemmed | Fault Diagnosis of Rotating Machinery Based on Wavelet Domain Denoising and Metric Distance |
| title_short | Fault Diagnosis of Rotating Machinery Based on Wavelet Domain Denoising and Metric Distance |
| title_sort | fault diagnosis of rotating machinery based on wavelet domain denoising and metric distance |
| topic | Fault diagnosis wavelet domain denoising metric distance rotating machinery |
| url | https://ieeexplore.ieee.org/document/8731983/ |
| work_keys_str_mv | AT naiquansu faultdiagnosisofrotatingmachinerybasedonwaveletdomaindenoisingandmetricdistance AT xiaoli faultdiagnosisofrotatingmachinerybasedonwaveletdomaindenoisingandmetricdistance AT qianghuazhang faultdiagnosisofrotatingmachinerybasedonwaveletdomaindenoisingandmetricdistance |