Name
Title | ||
|---|---|---|
Study on degradation state recognition for rotating machinery early fault based on PCA-FCM |
Abstract | ||
|---|---|---|
Fault diagnosis commonly only carries out the recognition between the fault and the normal states not include the different states classification of the same fault, which is a problem of the fuzzy degradation process. PCA for rotating machinery the early fault feature extraction and the application of FCM for different fault states recognition are mainly introduced to solve the above problem in the paper. Collecting the rotating machinery shaft misalignment signal based on the experiments, through the time-domain analysis, PCA is carried out to obtain PCs which reflect the changes of time domain eigenvalues; and then use FCM algorithm to cluster these eigenvalues. By using fuzzy closeness degree to recognize the different fault states, calculate Euclidean distance between each sample and fault clustering centers in the paper, thus we can obtain the results of diagnosis. The diagnosis results show that the method proposed in the paper can identify the different states of the same fault. © 2011 IEEE. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICNC.2011.6022265 | ICNC |
Keywords | DocType | Volume |
early fault diagnosis pca,fcm,rotating machine | Conference | 3 |
Issue | ISSN | Citations |
null | null | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Liang | 1 | 30 | 5.39 |
| Xiaoyu Sun | 2 | 95 | 16.54 |
| Laibin Zhang | 3 | 0 | 0.34 |