Diagnosis of Elevator Faults with LS-SVM Based on Optimization by K-CV
Several common elevator malfunctions were diagnosed with a least square support vector machine (LS-SVM). After acquiring vibration signals of various elevator functions, their energy characteristics and time domain indicators were extracted by theoretically analyzing the optimal wavelet packet, in o...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
|
| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2015/935038 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Several common elevator malfunctions were diagnosed with a least square support vector machine (LS-SVM). After acquiring vibration signals of various elevator functions, their energy characteristics and time domain indicators were extracted by theoretically analyzing the optimal wavelet packet, in order to construct a feature vector of malfunctions for identifying causes of the malfunctions as input of LS-SVM. Meanwhile, parameters about LS-SVM were optimized by K-fold cross validation (K-CV). After diagnosing deviated elevator guide rail, deviated shape of guide shoe, abnormal running of tractor, erroneous rope groove of traction sheave, deviated guide wheel, and tension of wire rope, the results suggested that the LS-SVM based on K-CV optimization was one of effective methods for diagnosing elevator malfunctions. |
|---|---|
| ISSN: | 2090-0147 2090-0155 |