SVM-RBM based Predictive Maintenance Scheme for IoT-enabled Smart Factory

Soonsung Hwang, Jongpil Jeong, Youngbin Kang
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引用次数: 18

Abstract

Fault diagnosis of facility maintenance is very important. Unexpected equipment failures during the process lead to significant losses to the plant. In this paper, in order to detect defects and fault patterns, Support Vector Machine (SVM) which is one of the machine learning algorithms, classifies the data received from the equipment as normal or abnormal. After learning only normal data by using Restricted Boltzmann Machine (RBM). We propose a model to identify the data, and then we analyze the faults of facilities in real-time.
基于SVM-RBM的物联网智能工厂预测性维护方案
设备维护中的故障诊断是非常重要的。在这个过程中,意外的设备故障会给工厂带来重大损失。在本文中,为了检测缺陷和故障模式,机器学习算法之一的支持向量机(SVM)将设备接收到的数据分为正常和异常两类。使用受限玻尔兹曼机(RBM)学习正常数据后。提出了一种数据识别模型,对设备故障进行实时分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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