[논문]1D CNN 알고리즘 기반의 가속도 데이터를 이용한 머시닝 센터의 고장 분류 기법 연구

김지욱; 장진석; 양민석; 강지헌; 김건우; 조용재; 이재욱

doi:10.14775/ksmpe.2019.18.9.029

1D CNN 알고리즘 기반의 가속도 데이터를 이용한 머시닝 센터의 고장 분류 기법 연구
A Study on Fault Classification of Machining Center using Acceleration Data Based on 1D CNN Algorithm 원문보기

한국기계가공학회지 = Journal of the Korean Society of Manufacturing Process Engineers, v.18 no.9, 2019년, pp.29 - 35

김지욱 (한국생산기술연구원 극한가공기술그룹) , 장진석 (한국생산기술연구원 건설기계부품그룹) , 양민석 (한국생산기술연구원 극한가공기술그룹) , 강지헌 (한국생산기술연구원 극한가공기술그룹) , 김건우 (한국생산기술연구원 건설기계부품그룹) , 조용재 (한국생산기술연구원 극한가공기술그룹) , 이재욱 (한국생산기술연구원 극한가공기술그룹)

Abstract ▼ AI-Helper

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.

주제어

AI 본문요약
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가설 설정

In the second fault condition, the bite direction of the jig to fix the workpiece is set differently and it is shown is Fig. 2. The measured acceleration is shown in Fig. 3, in case of normal signal, amplitude is constant and periodicity. Noise signal at the actual machining site is measured.
4. The process of selectively acquiring and learning the data during the cutting process is difficult, and it is desirable to learn the data of the entire process in order to apply it to the actual workplace. The actual time for the acquisition data is about 2500 seconds, and cutting process is about 2100 seconds.
Normal and fault signal is measured for all processes of the workpiece^[9-11]. In this study, fault is defined as the vibration generated during machining affects the quality of the workpiece. An arbitrary fault condition is created for fault classification.

제안 방법

The purpose of this study is to classify normal and fault signals by using the acceleration data generated during actual machining on the field instead of the test bed. The translational acceleration in three directions of normal and fault signal including external noise signal is measured.

대상 데이터

The sampling frequency is changed from 25kHz to 512Hz by extracting only the 50th signal to operate at low sampling rates. The training data is generated by sliding 300 pieces. Also each data overlaps 50% and it is shown is Fig.

이론/모형

8. The cost function is used cross entropy and Adam optimizer is used to optimize the error^[12,17,18] . The hyper parameters used for the fault classification algorithm are shown in Table 3.

성능/효과

1. Fault signal classification is performed using one-dimensional amplitude data, and the accuracy for the two fault conditions are about 76% and 81%, respectively.

참고문헌 (18)

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상세보기
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상세보기
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원문보기 상세보기
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상세보기
Lee, S. H., Sin, B. B., Kim, Y. J., Kim, J. S., "Deep-Learning based PHM Embedded System Using Noise.Vibration," Korean Society of Computer and Information, Vol. 25, No. 2, pp. 9-10, 2017.
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원문보기 상세보기
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원문보기 상세보기
Ronao, C. A., Cho, S. B., "Human Activity Recognition with Smartphone Sensors using Deep Learning Neural Networks," Expert System with Applications, Vol. 59, pp. 235-244, 2016.

상세보기
Kim, J. W., Lee, J. W., Kim, K. W., Kang, J. H., Yang, M. S., "Fault Diagnosis of Machining Center using Raw Signal of Noise and Vibration," International Symposium on Precision Engineering and Sustainable Manufacturing, Vol. 2019, pp. 071, 2019.

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