Equipment diagnosis system and method based on deep learning

• Main Authors: KWAK HA-NOCK, OH KYOUNG-WHAN, LEE HO-YOUL, PARK WON-KI, ZHANG BYOUNG-TAK, CHOI SEONG-HO, JANG YOUNG-IL, SEO JONG-HWI
• Format: Patent
• Language: Chinese; English
• Published: 15.10.2019
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES; PHYSICS; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR

An equipment diagnosis system and method, which use abnormal data and normal data of equipment and accurately and effectively perform diagnosis on the equipment, are provided. The equipment diagnosissystem includes a data acquisition unit to acquire time series data of equipment, a preprocessing unit convert the time series data into frequency data including a temporal component through a Fouriertransform, a deep learning unit to perform deep learning through a convolution neural network (CNN) by using the frequency data, and a diagnosis unit to determine a state of the equipment to be a normal state or a breakdown state based on the deep learning. 提供了设备诊断系统和方法，其使用设备的异常数据和正常数据，并准确有效地执行对设备的诊断。该设备诊断系统包括：数据采集单元，用于采集设备的时间序列数据；预处理单元，通过傅里叶变换将时间序列数据转换为包括时间分量的频率数据；深度学习单元，通过卷积神经网络(CNN)通过使用频率数据执行深度学习；以及诊断单元，基于深度学习将设备的状态确定为正常状态或故障状态。