웨이블릿 스펙트럼을 이용한스마트 팩토리 설비의 이상감지 및 진단

Abstract

Purpose: Condition-based maintenance (CBM) is widely used to decrease the risk of equipment failures. A signal data indicating the health status of equipments is continuously measured in CBM. This article proposes a fault detection and diagnosis approach for smart factory equipments based on the signal processing and feature extraction techniques using a support vector machine (SVM). Methods: We propose a discrete wavelet transform (DWT) as one of signal processing methods. After processing the signal data, we derive the representative energy spectrum through various measures such as mean, median, variance, and interquartile range (IQR). Finally, the SVM is used to classify two classes based on Gaussian radial basis function (RBF) kernel. Results: we applied the proposed method to signal data collected from the equipment. We compared the classification accuracy of the SVM. At window length of , the wavelet spectrum through the variance measure provides the best classification accuracy for the signal data of the equipment. Conclusion: In this article, fault detection and diagnosis methods for smart factory equipments are proposed.