Journal of the Korean Society of Manufacturing Technology Engineers (한국생산제조학회지)

The Korean Society of Manufacturing Technology Engineers (한국생산제조학회)
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Detection of Main Spindle Bearing Defects in Machine Tool by Acoustic Emission Signal via Neural Network Methodology

AE 신호 및 신경회로망을 이용한 공작기계 주축용 베어링 결함검출

  • 정의식 (대전산업대학교 기계설계 공학과)
  • Published : 1997.12.01
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Abstract

This paper presents a method of detection localized defects on tapered roller bearing in main spindle of machine tool system. The feature vectors, i.e. statistical parameters, in time-domain analysis technique have been calculated to extract useful features from acoustic emission signals. These feature vectors are used as the input feature of an neural network to classify and detect bearing defects. As a results, the detection of bearing defect conditions could be sucessfully performed by using an neural network with statistical parameters of acoustic emission signals.

Keywords

References

  1. Tribology v.17 no.1 Vibration Monitoring of Rolling Element Bearing by the High Frequency Resonance Techique - A Review McFadden, P.D.;Smith, J.D.
  2. ASME Paper No. 75-DET-46 Applications for Early Detection of Rolling Element Bearing Failures Using the High Frequency Resonance Techinique Darlow, M. S.;Badgley, R.H.
  3. AGMA Paper No.109 High Frequency Vibration Monitoring Techniques for Gear/Bearing System Failure Detection Drago, R. J.;Board, D. B.
  4. Elektrotekhnika v.52 no.1 Impact-impulse Diagnostics Method for Initial Damage to Rolling Contact Bearings Kovarskii, E.M.;Malygin, V. M.;Gorbunov, A.G.
  5. Progress in Acoustic Emission, Japan Soc. for NDI Estimate of the Fatigue Condition on Rolling Bearing by AE Nishimoto, S.;Kameno, R.
  6. Materials Evaluation The Detection of Incipient Failure in Bearing Balderston, H.L.
  7. Trans. of the ASME, J. of Mechanical Design v.100 Detection of Rolling Element Bearing Damage by Statistical Vibration Analysis Dyer, D.;Stewart, R. M.
  8. Trans. of the ASME, J. of Engineering for Industry v.111 On-Line Detection of Localized Defects in Bearing by Pattern Recognition Analysis Li, C.J.;Wu, S. M.
  9. Detection of Ball Bearing Conditions by an A.I. Approach, Sensor, Controls, and Quality Issues in Manufacturing Liu, T.I.;Mengel, J. M.
  10. 한국정밀공학회 v.12 no.5 신경회로망을 이용한 공작기계 주축용 베어링의 고장검지 정의식(외3명)