Diagnosis of Elevator Faults with LS-SVM Based on Optimization by K-CV

Zhou Wan; Shilin Yi; Kun Li; Ran Tao; Min Gou; Xinshi Li; Shu Guo

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Diagnosis of Elevator Faults with LS-SVM Based on Optimization by K-CV

机译：基于K-CV优化的LS-SVM诊断电梯故障

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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.

机译：用最小二乘支持向量机（LS-SVM）诊断了几种常见的电梯故障。在获取各种电梯功能的振动信号后，通过对最优小波包进行理论分析，提取其能量特征和时域指标，以构建故障特征向量，作为LS-SVM的输入来识别故障原因。同时，通过K-fold交叉验证（K-CV）对关于LS-SVM的参数进行了优化。在诊断出电梯导轨偏斜，导靴形状偏斜，拖拉机运行不正常，牵引滑轮绳槽错误，导轮偏斜以及钢丝绳张紧度后，得出基于K-CV优化的LS-SVM为诊断电梯故障的有效方法之一。

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来源
《Journal of electrical and computer engineering》 |2015年第2015期|935038.1-935038.8|共8页
作者
Zhou Wan; Shilin Yi; Kun Li; Ran Tao; Min Gou; Xinshi Li; Shu Guo;
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Kunming University of Science and Technology, Kunming, Yunnan 650500, China;

Kunming University of Science and Technology, Kunming, Yunnan 650500, China;

Kunming University of Science and Technology, Kunming, Yunnan 650500, China;

Yunnan Special Equipment Safety Inspection and Research Institute, Kunming, Yunnan, China;

Kunming University of Science and Technology, Kunming, Yunnan 650500, China;

Yunnan Special Equipment Safety Inspection and Research Institute, Kunming, Yunnan, China;

Yunnan Special Equipment Safety Inspection and Research Institute, Kunming, Yunnan, China;

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Diagnosis of Elevator Faults with LS-SVM Based on Optimization by K-CV

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