scholarly journals A Simple Continuous Wavelet Transform Method for Detection of Rolling Element Bearing Faults and its Comparison with Envelope Detection

2017 ◽  
Vol 6 (3) ◽  
pp. 1033-1040 ◽  
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Rolling Element Bearing ◽  
Continuous Wavelet ◽  
Transform Method ◽  
Bearing Faults ◽  
Envelope Detection ◽  
Rolling Element ◽  
Element Bearing

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

2001 ◽  
Vol 123 (3) ◽  
pp. 303-310 ◽  
Author(s):  
Peter W. Tse ◽  
Y. H. Peng ◽  
Richard Yam
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Rolling Element Bearing ◽  
Frequency Spectra ◽  
Bearing Faults ◽  
Time Frequency ◽  
Envelope Detection ◽  
Rolling Element ◽  
Element Bearing ◽  
Bearing Characteristic

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.

Bearing Fault Diagnosis Based on Wavelet Transform and ICA

2011 ◽  
Vol 148-149 ◽  
pp. 672-675 ◽  
Author(s):  
Qiang Wu ◽  
Qing Bo He ◽  
Fan Rang Kong ◽  
Yong Bin Liu ◽  
Peng Li
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Rolling Element Bearing ◽  
Continuous Wavelet ◽  
Typical Characteristic ◽  
Low Snr ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Element Bearing ◽  
Envelope Spectrum

The key to fault diagnosis of rolling element bearing is how to find typical characteristic frequencies from low SNR mixed signals. Jointing Continuous Wavelet Transform (CWT) with Independent Component Analysis (ICA)，this paper proposes a method to select wavelet scales with iso-interval frequency and analyze envelope spectrum of independent signal to diagnose the fault of rolling element bearing. Finally, the effectiveness of this method has been verified by practical signal of rolling element bearing.

scholarly journals An improved envelope detection method using particle swarm optimisation for rolling element bearing fault diagnosis

2017 ◽  
Vol 4 (4) ◽  
pp. 305-317 ◽  
Author(s):  
Sunil Tyagi ◽  
S.K. Panigrahi
Keyword(s):  
Fault Diagnosis ◽  
Particle Swarm Optimisation ◽  
Rolling Element Bearing ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Envelope Detection ◽  
Rolling Element ◽  
Element Bearing ◽  
Load Conditions

Abstract Traditionally Envelope Detection (ED) is implemented for detection of rolling element bearing faults by extracting the envelope of band-passed vibration signal and thereafter taking its Fourier transform. The performance of ED is highly sensitive to the envelope window (i.e. central frequency and bandwidth of the passband). This paper employs Particle Swarm Optimisation (PSO) to select the most optimum envelope window to band pass the vibration signals emanating from rotating driveline that was run in normal and with faults induced rolling element bearings. The envelopes of band-passed signals were extracted with the help of Hilbert Transform. The performance of ED whose envelope window was optimised by PSO to identify various commonly occurring bearing faults such as bearing with Outer Race Fault (ORF), Inner Race Fault (IRF) and Rolling Element Fault (REF) were checked under varying load conditions. The performance of ‘ED enhanced by PSO’ was also checked with increase in the severity of defect. It was shown that the improved ED method is successfully able to identify all types of bearing faults under different load conditions. It was shown that the by selecting envelope window by PSO makes ED especially useful to identify bearing faults at the incipient stage of defect. It was also shown by presenting comparative performance that by optimising the envelope window by PSO the performance of ED gets significantly enhanced in comparison to the traditional ED method for bearing fault diagnosis.

Demodulation of single interferograms using a sliding 2-D continuous wavelet transform method

2015 ◽  
Vol 62 (8) ◽  
pp. 633-637 ◽  
Author(s):  
Jésus Villa ◽  
Ismael de la Rosa ◽  
Rumen Ivanov ◽  
Daniel Alaniz ◽  
Efrén González
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Transform Method
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