Detection of minute signs of a small fault in a periodic or a quasi-periodic signal by the harmonic wavelet transform

Takumi Inoue, Atsuo Sueoka, Hiroyuki Kanemoto, Satoru Odahara, Yukitaka Murakami

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

If a machine in operation has a fault, signs of the fault appear in the monitored signal and are usually synchronised with the operating speed. The signs are very small when the fault is at an early stage. The fast Fourier transform (FFT) is often utilised to detect these signs, but it is very difficult to detect minute signs. In this paper, harmonic wavelet transform is utilised to detect the minute signs of small faults in a monitored signal. The monitored signal of a machine element, in ordinary operation, is regarded as periodic or quasi-periodic. It is important for the effectual detection of the minute signs to reduce the obstructive noise and the end effects in the signal. The end effect is a peculiar phenomenon to wavelet transform and hampers effective detection. Useful methods to reduce the obstructive noise and the end effects are devised herein by the authors. Even if no visible information pertaining to a fault appears in the monitored waveform, the present method is able to detect a minute sign of a small fault. It is demonstrated that the present method is capable of detecting minute signs arising from slight collisions caused by a loose coupling and a fatigue crack.

Original languageEnglish
Pages (from-to)2041-2055
Number of pages15
JournalMechanical Systems and Signal Processing
Volume21
Issue number5
DOIs
Publication statusPublished - Jul 1 2007

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Signal Processing
  • Civil and Structural Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Detection of minute signs of a small fault in a periodic or a quasi-periodic signal by the harmonic wavelet transform'. Together they form a unique fingerprint.

Cite this