Earticle

초록

영어

The automobile service experts often assess the health condition of the motorcycles based on the sound produced by taking test rides. To be effective, this process of fault diagnosis needs to be automated. The purpose of this paper is to present a method for fault detection of motorcycles that employs the slopes of the pseudospectral segments as features. Further, the estimated pseudospectrum of a sound signal is divided into eight segments, and the slope of each segment is computed. Artificial neural network (ANN) classifier is used for classification. The experimental results show that the proposed method achieves satisfactory results with an average accuracy of 78% for healthy motorcycles and 89% for faulty motorcycles. The study can be extended to locate the faults in subsystems of vehicles. The proposed work finds applications in allied areas such as fault diagnosis of machinery, musical instruments, electronic gadgets etc.

목차

Abstract
 1. Introduction
 2. Background
 3. Proposed Methodology
  3.1. Recording of Sound Samples
  3.2. Segmentation of Sound Signals
  3.3. Feature Extraction
 4. Results and Discussion
 5. Conclusion
 Acknowledgement
 References

키워드

저자

• Basavaraj S. Anami [ KLE Institute of Technology, Hubli-580 030, Karnataka State, India ]
• Veerappa B. Pagi [ Basaveshwar Engineering College, Bagalkot – 587 102, Karnataka State, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605