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Outer diameter of bearing is Thickness is 0. Rolling body diameter is Damage diameter of outer ring is 0. The number of the rolling element is 9, and the contact angle is 0, the sampling frequency is 12k Hz, the sampling points are The time domain waveform of the measured fault signal: a from inner race, b from outer race, c from rolling body.

Multiband Envelope Spectra Extraction for Fault Diagnosis of Rolling Element Bearings

According to the theory, the vibration failure frequency can be calculated by the following equations [21]. We respectively deal with the fault signal coming from inner race, outer race and rolling body. Analysis results of the inner ring fault signal are revealed firstly. It can be seen from these figures, the time-frequency resolution and the time-frequency concentration of Fig. In Fig. Using the time-frequency spectrum shown in Fig. There is some obvious peak near Hz, Hz and Hz in Fig.

Compared with Fig. Time-frequency spectrum in S domain and the power spectrum of the inner ring fault signal before and after resolution-improvement processing. Similar to the analysis of Fig. There is a fault in the outer ring. The analytical results are consistent with the theoretical results. The second harmonic and the third harmonic are clearer than that in Fig. Their side frequency information is more abundant than that in Fig. Same conclusion can be obtained from the analysis of Fig.

Obviously, the reconstructed signal after resolution-improvement processing fully utilizes the advantage of the generalized S transform. After reallocating the energy of obtained time-frequency spectrum, the reconstructed vibration signal has higher resolution not only in time domain and but also in frequency domain.

From the power spectrum of reconstructed signal, bearing condition and fault type can be clearly distinguished and the efficiency of fault diagnosis of bearing is effectively improved. Time-frequency spectrum in S domain and the power spectrum of the outer ring fault signal before and after resolution-improvement processing. Time-frequency spectrum in S domain and the power spectrum of the rolling body fault signal before and after resolution-improvement processing.

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The S domain time-frequency analysis has advantages in the bearing fault diagnosis. The time-frequency spectrum in S domain has a strong ability to characterize the dynamically change of energy distribution of bearing fault signal. The nonlinear dynamic characteristics of vibration signal are well portrayed using the S transform. These characteristics suggest that S domain time-frequency analysis method has the ability to completely illustrate the local time-frequency characteristics of vibration signal and provide a new way to improve analysis accuracy.

The proposed method to redistribute the energy of vibration signal is effective. The energy distribution coefficient is recalculated and the signal with high resolution can be reconstructed from the processed time-frequency spectrum.

1. Introduction

The processed result of the actual fault signal from the rolling element, inner ring and outer ring affirmatively verify the effectiveness of this method in bearing fault diagnosis. Time-frequency analysis method based on generalized S transformation improves the detection accuracy for bearing fault diagnosis. Acknowledgements The authors wish to acknowledge the assistance and support of all those who contributed to our effort to enhance and develop the described system.

The authors express their appreciation for the financial support provided by the National Natural Science Foundation of China Project No. Time-frequency analysis method of bearing fault diagnosis based on the generalized S transformation Jianhua Cai 1 , Yongliang Xiao 2. This is an open access article distributed under the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Views Reads Downloads Introduction Rolling bearing is an important part of the rotating machinery and the working condition of bearing directly affects the operation and the function of equipment. This is an open access article distributed under the terms of the Creative Commons Attribution License 4. Data correspond to usage on the plateform after The current usage metrics is available hours after online publication and is updated daily on week days. Open Access. Current usage metrics About article metrics Return to article.


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Time-frequency analysis method of bearing fault diagnosis based on the generalized S transformation

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