Vibration sensor data denoising using a time-frequency manifold for machinery fault diagnosis

Qingbo He, Xiangxiang Wang, Qiang Zhou

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

Abstract

Vibration sensor data from a mechanical system are often associated with important measurement information useful for machinery fault diagnosis. However, in practice the existence of background noise makes it difficult to identify the fault signature from the sensing data. This paper introduces the time-frequency manifold (TFM) concept into sensor data denoising and proposes a novel denoising method for reliable machinery fault diagnosis. The TFM signature reflects the intrinsic time-frequency structure of a non-stationary signal. The proposed method intends to realize data denoising by synthesizing the TFM using time-frequency synthesis and phase space reconstruction (PSR) synthesis. Due to the merits of the TFM in noise suppression and resolution enhancement, the denoised signal would have satisfactory denoising effects, as well as inherent time-frequency structure keeping. Moreover, this paper presents a clustering-based statistical parameter to evaluate the proposed method, and also presents a new diagnostic approach, called frequency probability time series (FPTS) spectral analysis, to show its effectiveness in fault diagnosis. The proposed TFM-based data denoising method has been employed to deal with a set of vibration sensor data from defective bearings, and the results verify that for machinery fault diagnosis the method is superior to two traditional denoising methods.

Original languageEnglish (US)
Pages (from-to)382-402
Number of pages21
JournalSensors (Switzerland)
Volume14
Issue number1
DOIs
StatePublished - Dec 27 2013
Externally publishedYes

Keywords

  • Bearing
  • Data denoising
  • Machinery fault diagnosis
  • Time-frequency manifold
  • Vibration sensor

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

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