Rolling Bearing Fault Diagnosis Based on an Improved HTT Transform
When rolling bearing failure occurs, vibration signals generally contain different signal components, such as impulsive fault feature signals, background noise and harmonic interference signals. One of the most challenging aspects of rolling bearing fault diagnosis is how to inhibit noise and harmon...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-04-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/4/1203 |
| _version_ | 1811299416742559744 |
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| author | Bin Pang Guiji Tang Tian Tian Chong Zhou |
| author_facet | Bin Pang Guiji Tang Tian Tian Chong Zhou |
| author_sort | Bin Pang |
| collection | DOAJ |
| description | When rolling bearing failure occurs, vibration signals generally contain different signal components, such as impulsive fault feature signals, background noise and harmonic interference signals. One of the most challenging aspects of rolling bearing fault diagnosis is how to inhibit noise and harmonic interference signals, while enhancing impulsive fault feature signals. This paper presents a novel bearing fault diagnosis method, namely an improved Hilbert time–time (IHTT) transform, by combining a Hilbert time–time (HTT) transform with principal component analysis (PCA). Firstly, the HTT transform was performed on vibration signals to derive a HTT transform matrix. Then, PCA was employed to de-noise the HTT transform matrix in order to improve the robustness of the HTT transform. Finally, the diagonal time series of the de-noised HTT transform matrix was extracted as the enhanced impulsive fault feature signal and the contained fault characteristic information was identified through further analyses of amplitude and envelope spectrums. Both simulated and experimental analyses validated the superiority of the presented method for detecting bearing failures. |
| first_indexed | 2024-04-13T06:34:44Z |
| format | Article |
| id | doaj.art-6c8c13e7b05f44b6be1eaa61290d0797 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T06:34:44Z |
| publishDate | 2018-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6c8c13e7b05f44b6be1eaa61290d07972022-12-22T02:57:57ZengMDPI AGSensors1424-82202018-04-01184120310.3390/s18041203s18041203Rolling Bearing Fault Diagnosis Based on an Improved HTT TransformBin Pang0Guiji Tang1Tian Tian2Chong Zhou3School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding 071000, ChinaSchool of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding 071000, ChinaSchool of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding 071000, ChinaSchool of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding 071000, ChinaWhen rolling bearing failure occurs, vibration signals generally contain different signal components, such as impulsive fault feature signals, background noise and harmonic interference signals. One of the most challenging aspects of rolling bearing fault diagnosis is how to inhibit noise and harmonic interference signals, while enhancing impulsive fault feature signals. This paper presents a novel bearing fault diagnosis method, namely an improved Hilbert time–time (IHTT) transform, by combining a Hilbert time–time (HTT) transform with principal component analysis (PCA). Firstly, the HTT transform was performed on vibration signals to derive a HTT transform matrix. Then, PCA was employed to de-noise the HTT transform matrix in order to improve the robustness of the HTT transform. Finally, the diagonal time series of the de-noised HTT transform matrix was extracted as the enhanced impulsive fault feature signal and the contained fault characteristic information was identified through further analyses of amplitude and envelope spectrums. Both simulated and experimental analyses validated the superiority of the presented method for detecting bearing failures.http://www.mdpi.com/1424-8220/18/4/1203improved Hilbert TT transformHilbert TT transformprincipal component analysisrolling bearingfault diagnosis |
| spellingShingle | Bin Pang Guiji Tang Tian Tian Chong Zhou Rolling Bearing Fault Diagnosis Based on an Improved HTT Transform Sensors improved Hilbert TT transform Hilbert TT transform principal component analysis rolling bearing fault diagnosis |
| title | Rolling Bearing Fault Diagnosis Based on an Improved HTT Transform |
| title_full | Rolling Bearing Fault Diagnosis Based on an Improved HTT Transform |
| title_fullStr | Rolling Bearing Fault Diagnosis Based on an Improved HTT Transform |
| title_full_unstemmed | Rolling Bearing Fault Diagnosis Based on an Improved HTT Transform |
| title_short | Rolling Bearing Fault Diagnosis Based on an Improved HTT Transform |
| title_sort | rolling bearing fault diagnosis based on an improved htt transform |
| topic | improved Hilbert TT transform Hilbert TT transform principal component analysis rolling bearing fault diagnosis |
| url | http://www.mdpi.com/1424-8220/18/4/1203 |
| work_keys_str_mv | AT binpang rollingbearingfaultdiagnosisbasedonanimprovedhtttransform AT guijitang rollingbearingfaultdiagnosisbasedonanimprovedhtttransform AT tiantian rollingbearingfaultdiagnosisbasedonanimprovedhtttransform AT chongzhou rollingbearingfaultdiagnosisbasedonanimprovedhtttransform |