Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement

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 intervals, which occur at Bearing Characteristic Frequencies (BCF). Since BCF contain very little energy, and are usually overwhelmed by noise and higher levels of macro-structural vibrations, they are difficult to find in their frequency spectra when using the common technique of Fast Fourier Transforms (FFT). Therefore, Envelope Detection (ED) is always used with FFT to identify faults occurring at the BCF. However, the computation of ED is complicated, and requires expensive equipment and experienced operators to process. This, coupled with the incapacity of FFT to detect nonstationary signals, makes wavelet analysis a popular alternative for machine fault diagnosis. Wavelet analysis provides multi-resolution in time-frequency distribution for easier detection of abnormal vibration signals. From the results of extensive experiments performed in a series of motor-pump driven systems, the methods of wavelet analysis and FFT with ED are proven to be efficient in detecting some types of bearing faults. Since wavelet analysis can detect both periodic and nonperiodic signals, it allows the machine operator to more easily detect the remaining types of bearing faults which are impossible by the method of FFT with ED. Hence, wavelet analysis is a better fault diagnostic tool for the practice in maintenance.

Download Full-text

Effects of machine speed on the development and detection of rolling element bearing faults

IEEE Power Electronics Letters ◽

10.1109/lpel.2003.814607 ◽

2003 ◽

Vol 1 (1) ◽

pp. 19-21 ◽

Cited By ~ 31

Author(s):

J.R. Stack ◽

T.G. Habetler ◽

R.G. Harley

Keyword(s):

Rolling Element Bearing ◽

Bearing Faults ◽

Rolling Element ◽

Element Bearing ◽

Machine Speed

Download Full-text

Cyclostationary Analysis for Gearbox and Bearing Fault Diagnosis

Shock and Vibration ◽

10.1155/2015/542472 ◽

2015 ◽

Vol 2015 ◽

pp. 1-12 ◽

Cited By ~ 7

Author(s):

Zhipeng Feng ◽

Fulei Chu

Keyword(s):

Rolling Element Bearing ◽

Bearing Faults ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Cyclic Frequency ◽

Fm Signals ◽

Rolling Element ◽

Element Bearing ◽

Bearing Vibration ◽

Cyclic Spectrum

Gearbox and rolling element bearing vibration signals feature modulation, thus being cyclostationary. Therefore, the cyclic correlation and cyclic spectrum are suited to analyze their modulation characteristics and thereby extract gearbox and bearing fault symptoms. In order to thoroughly understand the cyclostationarity of gearbox and bearing vibrations, the explicit expressions of cyclic correlation and cyclic spectrum for amplitude modulation and frequency modulation (AM-FM) signals are derived, and their properties are summarized. The theoretical derivations are illustrated and validated by gearbox and bearing experimental signal analyses. The modulation characteristics caused by gearbox and bearing faults are extracted. In faulty gearbox and bearing cases, more peaks appear in cyclic correlation slice of 0 lag and cyclic spectrum, than in healthy cases. The gear and bearing faults are detected by checking the presence or monitoring the magnitude change of peaks in cyclic correlation and cyclic spectrum and are located according to the peak cyclic frequency locations or sideband frequency spacing.

Download Full-text