Abstract
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.
Highlights
With the development of modern society, elevators have achieved rapid development as tools for transporting things up and down inside high-rise buildings
Major indicators [2] impacting if elevators can be taken with comfort include vibration of elevation compartments, noise, temperature, decoration, and starting and braking characteristics, among which the vibration is a major indicator for evaluating whether elevators can be taken comfortably [3]
By using Support vector machine (SVM) as classifier, energy characteristics and time domain indicators of vibration signals were extracted to construct fault feature vectors. They were adopted as input of SVM, and elevator malfunctions were classified with a well-trained SVM
Summary
With the development of modern society, elevators have achieved rapid development as tools for transporting things up and down inside high-rise buildings. The energy distribution was compared with that of vibration signals in normally running system to exact information about characteristics of elevator malfunctions [9]. Kurtosis of vibration signals in Z-axis and peak-to-peak values of vibration signals in X/Y-axes were taken as characteristic parameters in time domain. In this way, vibration characteristics could be extracted for malfunctions. By using SVM as classifier, energy characteristics and time domain indicators of vibration signals were extracted to construct fault feature vectors. They were adopted as input of SVM, and elevator malfunctions were classified with a well-trained SVM
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