[논문]MFCCs를 이용한 입력 변환과 CNN 학습에 기반한 운영 환경 변화에 강건한 베어링 결함 진단 방법

서양진

doi:10.3745/ktsde.2022.11.4.179

[국내논문] MFCCs를 이용한 입력 변환과 CNN 학습에 기반한 운영 환경 변화에 강건한 베어링 결함 진단 방법
An Input Transformation with MFCCs and CNN Learning Based Robust Bearing Fault Diagnosis Method for Various Working Conditions 원문보기

정보처리학회논문지. KIPS transactions on software and data engineering. 소프트웨어 및 데이터 공학, v.11 no.4, 2022년, pp.179 - 188

서양진 ((주)이포즌)

초록
AI-Helper

기계의 주요 부품인 베어링 결함 진단에 딥러닝을 활용하는 연구가 활발하게 진행되어 좋은 성능을 달성하였으나, 학습 데이터와 테스트 데이터의 운영 환경 차이로 인해 기계가 실제로 가동되는 환경에서는 성능 저하가 발생하는 문제가 있다. 학습 데이터와 테스트 데이터의 분포 차이 문제를 다루는 방법으로 데이터 적응이 제안되어 좋은 결과를 보여주고 있으나, 각 방법이 가정하고 있는 특정 적용 시나리오를 벗어나기 어렵다는 제약이 있다. 이에 본 연구는 MFCCs를 이용한 입력 데이터의 변환과 간단한 CNN 구조를 이용해 원시 도메인 데이터로부터 생성된 모델에 대해 추가적인 학습이나 조정 없이 타겟 도메인 데이터에 대한 테스트를 강건하게 수행하는 방법을 제안하였으며, 대표적인 베어링 결함 진단 데이터셋인 CWRU 베어링 데이터를 이용해 제안한 방법에 대한 실험 및 분석을 수행하였다. 실험 결과 전이 학습 기반의 방법들과 대등한 성능을 보였으며, 입력 변환 기반의 베이스라인 방법보다는 최소 15% 정도의 높은 성능을 달성하였다.

Abstract ▼ AI-Helper

There have been many successful researches on a bearing fault diagnosis based on Deep Learning, but there is still a critical issue of the data distribution difference between training data and test data from their different working conditions causing performance degradation in applying those methods to the machines in the field. As a solution, a data adaptation method has been proposed and showed a good result, but each and every approach is strictly limited to a specific applying scenario or presupposition, which makes it still difficult to be used as a real-world application. Therefore, in this study, we have proposed a method that, using a data transformation with MFCCs and a simple CNN architecture, can perform a robust diagnosis on a target domain data without an additional learning or tuning on the model generated from a source domain data and conducted an experiment and analysis on the proposed method with the CWRU bearing dataset, which is one of the representative datasests for bearing fault diagnosis. The experimental results showed that our method achieved an equal performance to those of transfer learning based methods and a better performance by at least 15% compared to that of an input transformation based baseline method.

Keyword

표/그림 (17)

그림 Fig. 1. Domain Adaptive Fine-tuning[21]
그림 Fig. 2. Framework of [27]
그림 Fig. 3. Simplified Architecture of the Proposed Method
그림 Fig. 4. MFCCs Processing
그림 Fig. 5. A Simplified Architecture of CNN
그림 Fig. 6. Input Examples
그림 Fig. 7. Applied CNN Architecture
그림 Fig. 8. Experimental Setup for CWRU Dataset[40]
표 Table 1. The Details of the Applied CNN Architecture
표 Table 2. The Details of Fault Classes
표 Table 3. Statistics of Vibration Signal Data for a Bearing with a Same Fault Type in Different Working Conditions
표 Table 4. Accuracy Comparison with Related Works
표 Table 5. Classification accuracy of baseline
표 Table 6. The details of the mfCNN architecture
표 Table 7. Accuracy comparison between different architectures
그림 Fig. 9. Vibration Signal and Its MFCCs for 1730rpm
그림 Fig. 10. Vibration Signal and Its MFCCs for 1750rpm

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