지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2018.4
- 수록면
- 450 - 463 (14page)
이용수
초록· 키워드
오류제보하기
Sound based machine fault diagnosis is the process consisting of detecting automatically the damages that affect the machines by analyzing the sounds they produce during their operating time. The collected sounds being inevitably corrupted by random disturbance, the most important part of the diagnosis consists of discovering the hidden elements inside the data that can reveal the faulty patterns. This paper presents a novel feature extraction methodology that combines various digital signal processing and pattern recognition methods for the analysis of the sounds produced by the drills. Using the Fourier analysis, the magnitude spectrum of the sounds are extracted, converted into two-dimensional vectors and uniformly normalized in such a way that they can be represented as 8-bit grayscale images. Histogram equalization is then performed over the obtained images in order to adjust their very poor contrast. The obtained contrast enhanced images will be used as the features of our diagnosis system. Finally, principal component analysis is performed over the image features for reducing their dimensions and a nonlinear classifier is adopted to produce the final response. Unlike the conventional features, the results demonstrate that the proposed feature extraction method manages to capture the hidden health patterns of the sound.
목차
ABSTRACT
1. INTRODUCTION
2. PROPOSED MFD METHOD
3. RESULTS AND DISCUSSION
4. CONCLUSION
REFERENCE
참고문헌 (14)
참고문헌 신청
[학술지(정기간행물)] - M.M. Polycarpou / 1995 / Learning Methodology for Failure Detection and Accommodation / IEEE Control Systems 15 (3) : 16 ~ 24
[학술지(정기간행물)] - N.R. Sakthivel / 2010 / Application of Support Vector Machine (SVM) and Proximal Support Vector Machine (PSVM) for Fault Classification of Mono Block Centrifugal Pump / International Journal of Data Analysis Techniques and Strategies 2 (1) : 38 ~ 61
[학술지(정기간행물)] - Y. Xu / 2011 / A New Feature Selection Method Based on Support Vector Machines for Text Categorization / International Journal of Data Analysis Techniques and Strategies 3 (1) : 1 ~ 20
[학술지(정기간행물)] - N.R. Sakthivel / 2012 / Application of Standalone System and Hybrid System for Fault Diagnosis of Centrifugal Pump Using Time Domain Signals and Statistical Features / International Journal of Data Mining Modeling and Management 4 (1) : 74 ~ 104
[학술지(정기간행물)] - N. Tandon / 1992 / Vibration and Acoustic Monitoring Techniques for the Detection of Defects in Rolling Element Bearings-A Review / The Shock and Vibration Digest 24 (3) : 3 ~ 11
[학술지(정기간행물)] - N.R. Sakthivel / 2010 / Application of Support Vector Machine (SVM) and Proximal Support Vector Machine (PSVM) for Fault Classification of Mono Block Centrifugal Pump / International Journal of Data Analysis Techniques and Strategies 2 (1) : 38 ~ 61
[학술지(정기간행물)] - Y. Xu / 2011 / A New Feature Selection Method Based on Support Vector Machines for Text Categorization / International Journal of Data Analysis Techniques and Strategies 3 (1) : 1 ~ 20
[학술지(정기간행물)] - N.R. Sakthivel / 2012 / Application of Standalone System and Hybrid System for Fault Diagnosis of Centrifugal Pump Using Time Domain Signals and Statistical Features / International Journal of Data Mining Modeling and Management 4 (1) : 74 ~ 104
[학술지(정기간행물)] - N. Tandon / 1992 / Vibration and Acoustic Monitoring Techniques for the Detection of Defects in Rolling Element Bearings-A Review / The Shock and Vibration Digest 24 (3) : 3 ~ 11
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UCI(KEPA) : I410-ECN-0101-2018-004-002056778