Application of SVM and Multi Sensor Data to Soundness Evaluation of Amusement Facility’s Wire Ropes

Abstract

Discovering regularities from a single species of time-series data used in the abnormality detection technique, it is difficult to perform the identification of discrimination and deterioration degree of the damage factor of the wire rope is a technique for performing fault detection and prediction. In this study focused on correlation of time series data obtained from the plurality of kinds of sensors, the continuous wavelet transform (CWT) and principal component analysis (PCA) from the detected values at the wire rope flaw detector according to experiments using a multi sensor the feature amount extracted, by performing classification by kernel support vector machine (K-SVM), and consider the possibility of an abnormally detection technology and soundness evaluation method of the wire rope.