Research on Circular plot analysis method and gear fault intelligent diagnosis based on time synchronous averaging
Gear’s Circular plot is a result presentation method which needs to be combine with time synchronous averaging (TSA), which can clearly display gear meshing vibration waveform extracted by TSA. Aiming at the problem of parameter setting of gear’s Circular plot and lack of the quantitative index, <...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Strength
2025-06-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2025.06.002 |
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| Summary: | Gear’s Circular plot is a result presentation method which needs to be combine with time synchronous averaging (TSA), which can clearly display gear meshing vibration waveform extracted by TSA. Aiming at the problem of parameter setting of gear’s Circular plot and lack of the quantitative index, <italic>F</italic><sub>i</sub> index for waveform edge recognition and <italic>Y</italic><sub>i</sub> index based on Hu-moments were proposed. Firstly, TSA algorithm was used to extract the gear meshing vibration signal, and the upper and lower edges of the vibration signal waveform were determined by calculating the minimum <italic>F</italic><sub>i</sub> index.Secondly, Circular plot of gears were drawn by the upper and lower edge parameters. Then, the Circular plot of the gear was divided into four parts, and <italic>Y</italic><sub>i</sub> index of the Circular plot was obtained by calculating Hu-moments of the picture after segmentation. Finally, based on the <italic>Y</italic><sub>i</sub> and <italic>F</italic><sub>i</sub> indices extracted from the gear Circular plot, a K-nearest neighbors (KNN) classifier was utilized to classify the gear vibration signals. The results show that there is a significant difference between the <italic>Y</italic><sub>i</sub> and <italic>F</italic><sub>i</sub> indices of the vibration signals of normal gears and those of abnormal gears. By combining with the KNN classifier， it is possible to distinguish between normal and abnormal gear signals， which proves the effectiveness of this method. |
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| ISSN: | 1001-9669 |