한국소음진동공학회:학술대회논문집 (Proceedings of the Korean Society for Noise and Vibration Engineering Conference)
- 한국소음진동공학회 2000년도 추계학술대회논문집
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- Pages.796-801
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- 2000
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- 1598-2548(pISSN)
웨이브렛변환과 인공신경망 기법을 이용한 소형 왕복동 압축기의 상태 분류
Classification of Normal/Abnormal Conditions for Small Reciprocating Compressors using Wavelet Transform and Artificial Neural Network
- 발행 : 2000.11.16
초록
The monitoring and diagnostics of the rotating machinery have been received considerable attention for many years. The objectives are to classify the machinery condition and to find out the cause of abnormal condition. This paper describes a signal classification method for diagnosing the rotating machinery using the artificial neural network and the wavelet transform. In order to extract salient features, the wavelet transform are used from primary noise signals. Since the wavelet transform decomposes raw time-waveform signals into two respective parts in the time space and frequency domain, more and better features can be obtained easier than time-waveform analysis. In the training phase for classification, self-organizing feature map(SOFM) and learning vector quantization(LVQ) are applied, and the accuracies of them are compared with each other. This paper is focused on the development of an advanced signal classifier to automatise the vibration signal pattern recognition. This method is verified by small reciprocating compressors, for refrigerator and normal and abnormal conditions are classified with high flexibility and reliability.
키워드