Bearing Diagnostics Using Time-Frequency Filtering and EEMD

2018 ◽  
pp. 44-55
Author(s):  
Hafida Mahgoun ◽  
Ridha Ziani
Keyword(s):  
Frequency Filtering ◽  
Time Frequency ◽  
Bearing Diagnostics

Comparison Between Time-Frequency Techniques to Predict Ball Bearing Faults in Drives Executing Arbitrary Motion Profiles

2008 ◽  
Author(s):  
Marco Cocconcelli ◽  
Cristian Secchi ◽  
Riccardo Rubini ◽  
Cesare Fantuzzi ◽  
Luca Bassi
Keyword(s):  
Velocity Profile ◽  
Cross Correlation ◽  
Fault Prediction ◽  
Variable Velocity ◽  
Outer Race ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Bearing Diagnostics ◽  
Packaging Machine ◽  
Expected Signal

In this paper Wavelet Transform (WT) and Hilbert-Huang Transform (HHT) are used as bearing diagnostics tools in drives executing arbitrary motion profiles. This field is increasingly drawing the attention of the industries because the modern electric motors work as electric cams inducing the shaft to move with a cyclic variable-velocity profile. The literature papers take into account only a constant velocity profile and they are not suitable for such applications. In fact literature methods analyse the signal only in the frequency domain, while in variable-velocity condition the bearing diagnostics should be performed in time domain. Both WT and HHT are time-frequency techniques which describe an input signal as a sum of specific functions. These functions are compared with a signal which simulates the expected vibrations of a bearing with a given fault, e.g. on the outer race. The comparison is done through a cross-correlation between the expected signal and the time-frequency techniques output. WT and HHT are used separately in an industrial case, which consists in bearing fault prediction in an automated packaging machine. In the end of the paper the WT and HHT results are discussed to analyse the different responses.

Seismic ground roll time–frequency filtering using the gaussian wavelet transform

2003 ◽  
Vol 318 (3-4) ◽  
pp. 551-561 ◽  
Author(s):  
G. Corso ◽  
P.S. Kuhn ◽  
L.S. Lucena ◽  
Z.D. Thomé
Keyword(s):  
Wavelet Transform ◽  
Frequency Filtering ◽  
Time Frequency ◽  
Ground Roll

scholarly journals Applications of fractional lower order S transform time frequency filtering algorithm to machine fault diagnosis

PLoS ONE ◽  
2017 ◽  
Vol 12 (4) ◽  
pp. e0175202 ◽  
Author(s):  
Junbo Long ◽  
Haibin Wang ◽  
Daifeng Zha ◽  
Peng Li ◽  
Huicheng Xie ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Lower Order ◽  
Frequency Filtering ◽  
Time Frequency ◽  
Filtering Algorithm ◽  
Machine Fault Diagnosis ◽  
S Transform ◽  
Machine Fault

scholarly journals S-TRANSFORM RIDGE FILTERING

2019 ◽  
Vol 2 (3) ◽  
pp. 168-173
Author(s):  
Aleksander Serdyukov ◽  
Anton Azarov ◽  
Alexandr Yablokov
Keyword(s):  
Seismic Data ◽  
Point Of View ◽  
Time Averaging ◽  
Signal Recovery ◽  
Frequency Filtering ◽  
Problem Of Time ◽  
Time Frequency ◽  
Filtering Method ◽  
S Transform ◽  
Temporal Localization

The problem of time-frequency filtering of seismic data on the basis of S-conversion is considered. S-transform provides a frequency-dependent resolution, while maintaining a direct connection with the Fourier spectrum. S-conversion is widely used in seismic processing. The standard filtering method based on S-conversion is based on its reversibility. From the point of view of temporal localization, this method is not optimal, since the calculation of the inverse S-transform includes time averaging. We propose an alternative filtering method based on signal recovery from S-transform peaks.

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