An Enhanced Diagnostic Scheme for Bearing Condition Monitoring

@article{Liu2010AnED,
  title={An Enhanced Diagnostic Scheme for Bearing Condition Monitoring},
  author={Jie Liu and Wilson Wang and M. Farid Golnaraghi},
  journal={IEEE Transactions on Instrumentation and Measurement},
  year={2010},
  volume={59},
  pages={309-321}
}
Rolling-element bearings are widely used in various mechanical and electrical facilities; accordingly, a reliable real-time bearing condition-monitoring system is very useful in industries to detect bearing defects at both incipient and advanced levels to prevent machinery performance degradation and malfunctions. The objective of this paper is to develop an enhanced diagnostic (ED) scheme for bearing fault diagnostics. This scheme consists of modules of classification and prediction. A… 

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References

SHOWING 1-10 OF 37 REFERENCES

An Enhanced Diagnostic System for Gear System Monitoring

  • Wilson Wang
  • Engineering
    IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
  • 2008
A neurofuzzy (NF) paradigm is adopted for pattern classification of the features from the energy, amplitude, and phase domains, and the diagnostic reliability is enhanced by properly integrating predicted future machinery states that are forecast by recurrent NF predictors.

An Intelligent System for Machinery Condition Monitoring

  • Wilson Wang
  • Computer Science
    IEEE Transactions on Fuzzy Systems
  • 2008
Test results have shown that the developed extended neurofuzzy (ENF) system is a robust condition monitoring tool that has good adaptive capabilities to accommodate different machinery conditions.

ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROTATING MACHINERY USING WAVELET TRANSFORMS AS A PREPROCESSOR

Abstract The purpose of condition monitoring and fault diagnostics are to detect and distinguish faults occurring in machinery, in order to provide a significant improvement in plant economy, reduce

ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROLLING ELEMENT BEARINGS USING TIME-DOMAIN FEATURES

The proposed procedure requires only a few features extracted from the measured vibration data either directly or with simple preprocessing, leading to faster training requiring far less iterations making the procedure suitable for on-line condition monitoring and diagnostics of machines.

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.

Multiple Band-Pass Autoregressive Demodulation for Rolling-Element Bearing Fault Diagnosis

A novel method to enhance the detection and diagnosis of low-speed rolling-element bearing faults based on discrete wavelet packet analysis (DWPA) with a significantly improved signal-to-noise ratio compared to its high-pass counterpart, with an exceptional capacity to exclude contaminating sources of vibration.

Fault Detection of Helicopter Gearboxes Using the Multi-Valued Influence Matrix Method

The results indicate that the MVIM method provides excellent results when the full range of faults effects on the measurements are included in the training set.

Fault-signature modeling and detection of inner-race bearing faults

This paper develops a fault-signature model and a fault-detection scheme for using machine vibration to detect inner-race defects. To motivate this research, it is explained and illustrated with