Shannon wavelet spectrum analysis on truncated vibration signals for machine incipient fault detection

  title={Shannon wavelet spectrum analysis on truncated vibration signals for machine incipient fault detection},
  author={Jie Liu},
  journal={Measurement Science and Technology},
  • Jie Liu
  • Published 1 May 2012
  • Engineering
  • Measurement Science and Technology
Although a variety of methods have been proposed in the literature for machine fault detection, it still remains a challenge to extract prominent features from random and nonstationary vibratory signals, a typical representative of which are the resonance signatures generated by incipient defects on the rolling elements of ball bearings. Due to its random and nonstationary nature, the involved signal generally possesses a low signal-to-noise ratio, where the classical signal processing methods… 

Research on a Signal Analysis Method based on Wavelet Theory and Approximate Entropy Algorithm

The wavelet theory and approximate entropy algorithm are introduced into the signal analysis in order to propose a new vibration signal analysis (WTAEAVSA) method, which can extract the characteristic vector from vibration signal, visually and sharply reflect the changes of the mechanical states.

A Reliable Fault Diagnosis Method for a Gearbox System with Varying Rotational Speeds

A novel gearbox fault diagnosis method based on an adaptive noise reducer–based Gaussian reference signal (ANR-GRS) technique that can significantly reduce noise and improve classification from a one-against-one, multiclass support vector machine (OAOMCSVM) for the fault types of a gearbox.

Analysis of the Vibration Signal Using Time-Frequency Methods

The advantage of the wavelet application is shown with an example and manifolds of wavelets reported in this application are reviewed and the case study implies that the optimal wavelets are db8 and db7 and the optimal level of decomposition is the fifth level.

Wavelet-based features for prognosis of degradation in rolling element bearing with non-linear autoregressive neural network

  • V. Nistane
  • Engineering, Computer Science
    Australian Journal of Mechanical Engineering
  • 2019
ABSTRACT In rotary machine, the frequently failure component is the rolling element bearing (REBs).The timely identifying the potential fault can prevent the breakdown and failure of rotary machine.

Diagnosing simultaneous faults using the local regularity of vibration signals

The regularity of the vibration signals measured from a rotating machine is often affected by the condition of the machine. The fractional order of regularity can be measured using the definition of

Fault detection and diagnosis in rotating machinery by vibration monitoring using FFT and Wavelet techniques

Experimental results confirm that WT-FFT serves as a good tool to online faults detection and diagnosis of rotating machines.

Vibration Analysis Based Feature Extraction for Bearing Fault Detection

Rolling element bearings are widely used in various rotary machines. Most rotary machine failures are attributed to unexpected bearing faults. Accordingly, reliable bearing fault detection is



Wavelet Analysis and Envelope Detection For Rolling Element Bearing Fault Diagnosis—Their Effectiveness and Flexibilities

The components which often fail in a rolling element bearing are the outer-race, the inner-race, the rollers, and the cage. Such failures generate a series of impact vibrations in short time

Wavelet spectrum analysis for bearing fault diagnostics

Test results show that this new signal processing technique, wavelet spectrum analysis, is an effective bearing fault detection method, which is especially useful for non-stationary feature extraction and analysis.

Wavelet transform with spectral post-processing for enhanced feature extraction [machine condition monitoring]

A new approach, based on a combined wavelet and Fourier transformation, is presented in this paper, which provides significantly improved feature extraction capability over the spectral technique.

Wavelet transform with spectral post-processing for enhanced feature extraction

  • Changting WangR. Gao
  • Engineering
    IMTC/2002. Proceedings of the 19th IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.00CH37276)
  • 2002
The quality of machine condition monitoring techniques as well as their applicability in the industry are determined by the effectiveness and efficiency with which characteristic signal features are

Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings

Abstract Fatigue faults on the surface of rolling bearing elements are some of the most frequent causes of malfunctions and breakages of rotating machines. In normal operating conditions this kind of

Application of the Laplace-Wavelet Combined With ANN for Rolling Bearing Fault Diagnosis

A new technique for an automated detection and diagnosis of rolling bearing faults is presented. The time-domain vibration signals of rolling bearings with different fault conditions are preprocessed

Bearing Signature Analysis as a Medium for Fault Detection: A Review

Rolling element bearings find widespread domestic and industrial application. Defects in bearing unless detected in time may lead to malfunctioning of the machinery. Different methods are used for