Diagnosis of Rolling Elements Bearing Based on Inverse Autoregressive Filter

Luigi Garibaldi; Guo Feng Wang; Stefano Marchesiello; Alessandro Fasana

doi:10.4028/www.scientific.net/KEM.413-414.635

Paper Titles

Fault Diagnostics Based on Pattern Spectrum Entropy and Proximal Support Vector Machine
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Signal Processing by Energy Normalization Method Based on Wavelet Packet
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Application of Cyclic Correlation Analysis to Gear Damage Detection
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Feature Extraction for Damage Detection in Structures Based on Nonlinearity Analysis
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Diagnosis of Rolling Elements Bearing Based on Inverse Autoregressive Filter
p.635

Time-Varying Output-Only Identification of a Cracked Beam
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Defects Diagnosis of Bearing by Means of Acoustic Emission and Continuous Wavelet Transform
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Particle Swarm vs. Evolutionary Optimization Techniques in a Multiobjective Framework for Damage Identification
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Damage Identification Using Response Surface Methodology
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HomeKey Engineering MaterialsKey Engineering Materials Vols. 413-414Diagnosis of Rolling Elements Bearing Based on...

Diagnosis of Rolling Elements Bearing Based on Inverse Autoregressive Filter

Abstract:

Diagnosis of rolling elements bearing plays an important role on the running and maintenance of mechanical equipments. To enhance the feature of fault and to further diagnose the status of bearings with a small fault size so as to realize the early recognition, the method of inverse filter based on Autoregressive model is presented in this paper, and the corresponding criterion of order selection is also discussed. Analysis of simulation signals and real data show that this method could enhance feature of impulse signal. Meanwhile, it is also found that for small size fault, the root mean square feature is more effective than kurtosis value, which is considered very useful for early diagnosis of rolling elements bearings.

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Periodical:

Key Engineering Materials (Volumes 413-414)

Pages:

635-642

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.413-414.635

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Online since:

June 2009

Authors:

Luigi Garibaldi, Guo Feng Wang, Stefano Marchesiello, Alessandro Fasana

Keywords:

AIC, Autoregressive, FPE, Inverse Filter, Rolling Element Bearing

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