Bearing-shaft systems are essential components in various automated manufacturing processes, primarily designed for the efficient rotation of a main shaft by a motor. Accurate fault detection is critical for operating manufacturing processes, yet challenges remain in sensor selection and optimization regarding types, locations, and positioning. Sound signals present a viable solution for fault detection, as microphones can capture mechanical sounds from remote locations and have been traditionally employed for monitoring machine health. However, recordings in real industrial environments always contain non-negligible ambient noise, which hampers effective fault detection. Utilizing a high-performance microphone for noise cancellation can be cost-prohibitive and impractical in actual manufacturing sites, therefore to address these challenges, we proposed a convolution neural network-based methodology for fault detection that analyzes the mechanical sounds generated from the bearing-shaft system in the form of Log-mel spectrograms. To mitigate the impact of environmental noise in recordings made with commercial microphones, we also developed a denoising autoencoder that operates without requiring any expert knowledge of the system. The proposed DAE-CNN model demonstrates high performance in fault detection regardless of whether environmental noise is included(98.1%) or not(100%). It indicates that the proposed methodology effectively preserves significant signal features while overcoming the negative influence of ambient noise present in the collected datasets in both fault detection and fault type classification.

1. 서 론
2. 관련 연구
3. 기계음 분석을 통한 고장 감지 모델 프레임
4. 분석 대상 및 수집 데이터
    4.1 베어링 샤프트 시스템의 정상 및 고장 상태 정의
    4.2 소리 데이터 수집 및 증강
    4.3 소리 데이터의 Log-mel Spectrogram 변환
5. 고장 감지 모델 구축 및 분석 결과
    5.1 환경 소음 제거를 위한 Autoencoder 기반Denoising 모델 구축
    5.2 기계음을 이용한 고장 감지 모델 구축
    5.3 고장 감지 결과
6. 결 론
References

• Min Hee Lee(Department of Industrial & Management Engineering, Hanbat National University) | 이민희 (국립한밭대학교 산업경영공학과)
• Gun Chang Lee(Department of Industrial & Management Engineering, Hanbat National University) | 이건창 (국립한밭대학교 산업경영공학과)
• Su Jeong Oh(Department of Industrial & Management Engineering, Hanbat National University) | 오수정 (국립한밭대학교 산업경영공학과)
• Sujeong Baek(Department of Industrial & Management Engineering, Hanbat National University) | 백수정 (국립한밭대학교 산업경영공학과) Corresponding author