scholarly journals ตัวบ่งชี้สำหรับปรับมอร์เลต์เวฟเลตเพื่อใช้ในการตรวจจับความเสียหายแบบเฉพาะที่ของตลับลูกปืน (A New Indicator for Morlet Wavelet Filter Adjustment in Rolling Element Bearing Localized Defect Detection)

2011 ◽  
Vol 3 (1) ◽  
pp. 17-34
Author(s):  
นันธพงศ์ กุลดิลกไพบูลย์ ◽  
ชัยโรจน์ คุณพนิชกิจ
Keyword(s):  
Defect Detection ◽  
Morlet Wavelet ◽  
Rolling Element Bearing ◽  
Wavelet Filter ◽  
Rolling Element ◽  
Element Bearing

Refined composite multiscale fuzzy entropy: Localized defect detection of rolling element bearing

2019 ◽  
Vol 33 (1) ◽  
pp. 109-120 ◽  
Author(s):  
Yongjian Li ◽  
Bingrong Miao ◽  
Weihua Zhang ◽  
Peng Chen ◽  
Jihua Liu ◽  
...  
Keyword(s):  
Defect Detection ◽  
Fuzzy Entropy ◽  
Rolling Element Bearing ◽  
Rolling Element ◽  
Element Bearing

Rolling Element Bearing Defect Detection and Diagnostics Using Displacement Transducers

2001 ◽  
Author(s):  
John J. Yu ◽  
Donald E. Bently ◽  
Paul Goldman ◽  
Kenwood P. Dayton ◽  
Brandon G. Van Slyke
Keyword(s):  
Defect Detection ◽  
Amplitude Ratio ◽  
Spectral Characteristics ◽  
Rolling Element Bearing ◽  
Time Base ◽  
Outer Race ◽  
Bearing Defect ◽  
Rolling Element ◽  
Element Bearing ◽  
Displacement Transducers

This paper introduces the methodology of rolling element bearing defect detection using high-gain displacement transducers. The nature of defect influence on the outer race deflection in the vicinity of the transducer tip in time base has been established. Inner race, outer race, and rolling element (ball/roller) defects, which often occur sequentially, can be clearly identified according to spike signals on the time-varying outer race deflection curve along with known bearing frequencies. The developed techniques are fully corroborated by experimental data. Spike-to-deflection amplitude ratio, which is almost independent of changes in speed and load for a given defect, is used to judge the defect severity. Spectral characteristics due to these defects have also been found. It is shown that this direct measurement by using displacement transducers without casing influence, which would be inevitable by using accelerometers mounted on the casing, is a reliable approach to detect bearing defects as well as their severity and locations.

Compound faults detection of the rolling element bearing based on the optimal complex Morlet wavelet filter

2017 ◽  
Vol 232 (10) ◽  
pp. 1786-1801 ◽  
Author(s):  
Xiaohui Gu ◽  
Shaopu Yang ◽  
Yongqiang Liu ◽  
Feiyue Deng ◽  
Bin Ren
Keyword(s):  
Particle Filtering ◽  
Morlet Wavelet ◽  
Previous Method ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Wavelet Filter ◽  
Outer Race ◽  
Rolling Element ◽  
Complex Morlet Wavelet ◽  
Envelope Spectrum

Wavelet filter is widely used in extracting fault features embedded in the noisy vibration signal, especially the complex Morlet wavelet. In most occasions, the filter parameters are optimized adaptively with a suitable objective function. And then, with the Hilbert transform demodulation analysis, the single localized fault in rolling element bearings can be detected. To extend it for compound faults detection, a novel index deduced from the different intervals of the prominent bearing fault frequencies and subsequent harmonics in the envelope spectrum is proposed. By maximizing the ratio of correlated kurtosis to kurtosis of the envelope spectrum amplitudes of the filtered signal, the optimal complex Morlet wavelet filters corresponding to the different faults are designed by the particle filtering method, respectively. Two cases of real signals are analyzed to evaluate the performance of the proposed method, which include one case of experiment signal with artificial outer race fault coupled with roller fault, as well as one case of engineering data with outer race fault coupled with inner race fault. Furthermore, some comparisons with a previous method are also conducted. The results demonstrate the effectiveness and robustness of the method in compound faults diagnosis of the rolling element bearings.

Double Impulses Extraction of Faulty Rolling Element Bearing Based on EEMD and Complex Morlet Wavelet

2014 ◽  
Vol 904 ◽  
pp. 437-441 ◽  
Author(s):  
You Bing Kong ◽  
Yu Guo ◽  
Wu Xing
Keyword(s):  
High Frequency ◽  
Random Noise ◽  
Ensemble Empirical Mode Decomposition ◽  
Morlet Wavelet ◽  
Rolling Element Bearing ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Element Bearing ◽  
Continuous Noise ◽  
Complex Morlet Wavelet

A method for the double impulses extraction of faulty rolling element bearing (REB) is proposed in this paper. In the proposed approach, the (Ensemble Empirical Mode Decomposition) EEMD are employed for filtering the random noise and high-frequency continuous noise without any mode mixing. Then, the extracted signal is filtered with complex Morlet wavelet, which enhanced the double impulses greatly. As a result, clear double impulses can be obtained through the envelope. The effectiveness of the approach is demonstrated by the simulation study.

Time-Frequency Spectral Power Assessment of Rolling Element Bearing Faults Using Adaptive Modified Morlet Wavelet Transform

2020 ◽  
Vol 13 ◽  
Author(s):  
O. P. Yadav ◽  
G.L. Pahuja
Keyword(s):  
Wavelet Transform ◽  
Vibration Signal ◽  
Morlet Wavelet ◽  
Rolling Element Bearing ◽  
Time Frequency ◽  
Bearing Defects ◽  
S Transform ◽  
Rolling Element ◽  
Element Bearing ◽  
Morlet Wavelet Transform

Objectives: The main objectives of this manuscript are to investigate and diagnose rolling element bearing defects in its inception time. Methods: Vibration signal generated by induction motor contains series of frequency components that have rich and viable information about bearing health conditions. Recently, maximum energy concentration (MEC) measure of time-frequency spectrum has been employed to investigate the small variations in low frequency biomedical signal spectrum. In this paper, the above technique has been modified and applied to study the bearing defects of induction motor using vibration signal and it is termed as adaptive modified Morlet wavelet (AMMW) transform. Initially, this proposed method was validated on two medium frequency synthetic time series signals in terms of MEC measurement at different signal to noise ratio (SNR). Results: The simulated results have depicted that AMMW method provides excellent time-frequency localization capability over other time-frequency methods like Morlet wavelet transform, modified Morlet wavelet transform, adaptive S-transform and adaptive modified S-transform. Then this method has been applied on standard database of vibration signal to determine of interquartile power for fault detection purpose and also fault index parameter termed as has been analyzed to detect small variation in vibration signals.

Rolling Element Bearing Defect Detection and Diagnostics Using Displacement Transducers

2002 ◽  
Vol 124 (3) ◽  
pp. 517-527 ◽  
Author(s):  
J. J. Yu ◽  
D. E. Bently ◽  
P. Goldman ◽  
K. P. Dayton ◽  
B. G. Van Slyke
Keyword(s):  
Defect Detection ◽  
Amplitude Ratio ◽  
Spectral Characteristics ◽  
Rolling Element Bearing ◽  
Time Base ◽  
Outer Race ◽  
Bearing Defect ◽  
Rolling Element ◽  
Element Bearing ◽  
Displacement Transducers

This paper introduces the methodology of rolling element bearing defect detection using high-gain displacement transducers. The nature of defect influence on the outer race deflection in the vicinity of the transducer tip in time base has been established. Inner race, outer race, and rolling element (ball/roller) defects, which often occur sequentially, can be clearly identified according to spike signals on the time-varying outer race deflection curve along with known bearing frequencies. The developed techniques are fully corroborated by experimental data. Spike-to-deflection amplitude ratio, which is almost independent of changes in speed and load for a given defect, is used to judge the defect severity. Spectral characteristics due to these defects have also been found. It is shown that this direct measurement by using displacement transducers without casing influence, which would be inevitable by using accelerometers mounted on the casing, is a reliable approach to detect bearing defects as well as their severity and locations.

Rolling element bearing fault detection based on the complex Morlet wavelet transform and performance evaluation using artificial neural network and support vector machine

2019 ◽  
Vol 50 (9-11) ◽  
pp. 313-327 ◽  
Author(s):  
Chandrabhanu Malla ◽  
Ankur Rai ◽  
Vaishali Kaul ◽  
Isham Panigrahi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Morlet Wavelet ◽  
Rolling Element Bearing ◽  
Support Vector ◽  
Bearing Fault ◽  
Rolling Element ◽  
Element Bearing ◽  
Complex Morlet Wavelet

Condition monitoring and fault diagnosis of rolling element bearings are very important to ensure proper working of different types of machinery. Condition monitoring of rotating machines is mainly based on the analysis of machine vibration. The vibration signals from the mechanical fault generally comprise periodic impulses with specified characteristic frequency corresponds to a particular defect. But due to heavy noise in the industry, the vibration signals have a very low signal-to-noise ratio. Hence, it requires an appropriate technique to extract the impulses from the noisy signal. This article emphasized on the fault diagnosis of rolling element bearings having some specific size of defects on various bearing elements using the complex Morlet wavelet analysis. The phase and amplitude map of the complex Morlet wavelet are utilized for identification and diagnosis of the fault in the rolling element bearing. The amplitude and phase map corresponding to the complex Morlet wavelet are found to show unique informative signatures in the presence of bearing faults. The classification technique based on artificial neural network and support vector machine for rolling element bearing fault detection is presented in this article. The classification results of bearing faults clearly indicate that support vector machine has a more precise bearing fault classification ability than artificial neural network.

Sign in / Sign up

Export Citation Format

Share Document