A Study on Fault Classification of Machining Center using Acceleration Data Based on 1D CNN Algorithm

서지정보

· 발행기관 : 한국기계가공학회
· 수록지 정보 : 한국기계가공학회지 / 18권 / 9호 / 29 ~ 35페이지
· 저자명 : 김지욱, 장진석, 양민석, 강지헌, 김건우, 조용재, 이재욱

초록

The structure of the machinery industry due to the 4th industrial revolution is changing from precision and durability to intelligent and smart machinery through sensing and interconnection(IoT). There is a growing need for research on prognostics and health management(PHM) that can prevent abnormalities in processing machines and accurately predict and diagnose conditions. PHM is a technology that monitors the condition of a mechanical system, diagnoses signs of failure, and predicts the remaining life of the object. In this study, the vibration generated during machining is measured and a classification algorithm for normal and fault signals is developed. Arbitrary fault signal is collected by changing the conditions of un stable supply cutting oil and fixing jig. The signal processing is performed to apply the measured signal to the learning model. The sampling rate is changed for high speed operation and performed machine learning using raw signal without FFT. The fault classification algorithm for 1D convolution neural network composed of 2 convolution layers is developed.

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