Rolling Bearing Performance Degradation Prediction Based on FBG Signal

This study proposes a noise reduction technique that combines empirical mode decomposition (EMD) with wavelet thresholding to remove noise interference generated by metal slag in the damage signal of steel cord conveyor belts. The initial step involves decomposing the original signal into high-frequency and low-frequency intrinsic mode function (IMF) components using empirical mode decomposition (EMD), guided by the metal slag signal characteristics analysis. The high-frequency noise is filtered away while the low-frequency IMF components are kept intact through the wavelet threshold de-noising technique. The slag noise-containing IMF components are deleted, and the filtered signal and the low-frequency IMF components are utilized for signal reconstruction to provide the optimum noise reduction result. The study demonstrates that this method is more effective in suppressing metal slag noise, environmental noise, and other noise interferences than traditional EMD decomposition noise reduction and wavelet threshold noise reduction methods. This research provides a valuable reference for noise reduction in steel cord conveyor belt detection data.

Time-frequency analysis is of necessity for wrist pulse signal due to its complexity, among which, empirical mode decomposition (EMD) algorithm and its improved noise-assisted versions (such as ensemble EMD, noise-assisted multivariate EMD (NA-MEMD) and very recently median EMD) are deemed to be the most representative ones. In this study, we provide an in-depth evaluation of these well-established noise-assisted EMD algorithms in computational pulse analysis for the first time. In particular, we compare the performance of the different algorithms systematically and quantitatively based on objective quantitative criteria: number and central frequency of intrinsic mode function (IMF) components, total orthogonality index and mode mixing. Rather than using synthetic signals with visual inspection in most existing literature, the wrist pulse signals used in the evaluation are real recorded samples acquired from both healthy and patient subjects. Through extensive experiments, we found that: 1) Advanced EMD algorithm that has the best performance in other areas may not be the most suitable method for pulse signal analysis, which indicates its high dependence on the type of signal to be analyzed; 2) Adding noise can significantly improve algorithm performance, but tends to produce physiologically not relevant components, which however are usually neglected throughout the intelligent pulse diagnosis literature. Therefore, excluding redundant components and then extracting features is expected to improve performance further. Together, currently NA-MEMD achieves a better performance consistently, potential to become a powerful tool for computational pulse analysis, which in itself have not been applied in wrist pulse analysis before. We believe our works can bring up a new perspective to application of EMD-like algorithms in computational pulse analysis/diagnosis with effective information and guidance. Additionally, considering the similarity between physiological signals, especially such as photoplethysmogram/electrocardiogram, our research can be extended to wearable health monitoring technologies, including smart watches and fitness trackers, and their potential future applications, such as in heart rate estimation and evaluate various cardiovascular-related diseases. The present study underscored the necessity of evaluation noise-assisted EMDs or other adaptive decomposition algorithms based on real recorded signals with more objective measures. Especially, caution the possible redundant components that are introduced.

In distributed generation (DG) connected grid system, there is serious harmonic pollution caused by the wide use of inverter units and nonlinear loads. The traditional detection methods (such as Fast Fourier Transform and wavelet analysis) heavily depend on synchronous signal detection or basis functions selection, which don’t satisfy the requirements of the harmonic detection of the micro-grid environment. Therefore, a novel method combining discrete wavelet transform (DWT) with Hilbert–Huang transform (HHT) is proposed to detect and analyze the harmonic. The method uses multi resolution analysis of DWT to process the original signal. Then, a series of intrinsic mode function (IMF) components are obtained from the processed signal by using Empirical Mode Decomposition (EMD) algorithm. Thirdly, the fundamental component and harmonic are extracted from the IMF components. Finally, the frequency and amplitude of the signal are calculated by using the HHT. Simulation results demonstrate that the proposed algorithm has high precision and good real-time characteristics. Furthermore, the proposed algorithm satisfies the requirements of the harmonic detection of the DG connected grid system.

In order to solve the fault location problem of single phase grounding fault in non-solidly earthed distribution network, the constitution, principle and application condition of a new fault locating method are presented in this paper. This method is realized by the distribution automation system (DAS), feeder monitoring unit has been deployed to acquire fault zero sequence current waveform and send the waveform to DAS master station. The locating workstation deployed on master station to get intrinsic mode function (IMF) component from zero sequence current waveform by using empirical mode decomposition (EMD) algorithm, then calculated similarity coefficient of IMF component by analyzing waveform similarity algorithm, finally located fault section according to similarity coefficient. The actual operation results verified that the locating method is reasonable and effective.

Fault diagnosis of rolling bearings is still a very important and difficult research task on engineering. After analyzing the shortcomings of current bearing fault diagnosis technologies, a new approach based on Empirical Mode Decomposition (EMD) and blind equalization eigenvector algorithm (EVA) for rolling bearings fault diagnosis is proposed. In this approach, the characteristic high-frequency signal with amplitude and channel modulation of a rolling bearing with local damage is first separated from the mechanical vibration signal as an Intrinsic Mode Function (IMF) by using EMD, then the source impact vibration signal yielded by local damage is extracted by means of a EVA model and algorithm. Finally, the presented approach is used to analyze an impacting experiment and two real signals collected from rolling bearings with outer race damage or inner race damage. The results show that the EMD and EVA based approach can effectively detect rolling bearing fault.

Aiming at the problem that the influence of environmental noise and insensitive damage factor make damage identification difficult and imaging error large for composite plate, a damage factor based on empirical mode decomposition(EMD) and correlation coefficient was proposed. The air-couple probe was used to obtain Lamb wave signal before and after the damage, and a group of intrinsic mode function (IMF) components of signal was obtained by EMD. According to the correlation coefficient, the IMF component which has the greatest correlation with the signal was obtained, and the relative change of its energy value was defined as the damage factor. The validity of the proposed algorithm was assessed by identifying damage and scaning imaging at composite plate. The results show that after EMD, the IMF component with the greatest correlation with the original signal is most sensitive to damage, and can be used as a damage factor to identify damage. Combining this damage factor with probability imaging algorithm for the air-coupled Lamb wave scanning can not only effectively image the defect in composite plate, but also has good noise resistance in simulated strong noise environment.

This paper investigates the use of advance signal processing techniques for the classification of four common reciprocating compressor conditions. Non-stationary vibration signals from the compressor are decomposed based on WPT (Wavelet Packet Transform) and EMD (Empirical Mode Decomposition) and harmonic changes of the optimal components of each technique is used for condition monitoring and diagnostics of the machine. Performance comparison of WPT and EMD is done for the envelope analysis of the optimal wavelet packet and the IMF (Intrinsic Mode Function) component that can extract salient features of the four compressor conditions including one normal and three fault conditions (intercooler leakage, discharge valve leakage and the combination of intercooler and discharge valve leakage). Harmonic changes produced after envelope analysis of the best packet and IMF component are used for classification and results show that WPT technique is more robust and effective in fault classification compared to EMD.

This paper proposed a method for roller bearing fault diagnosis using Empirical Mode Decomposition (EMD) algorithm and decision tree. First, to obtain the Intrinsic Mode Functions (IMFs) of bearing vibration signal processed by EMD and processing the IMFs with autocorrelation for noise elimination, then extract the principal frequency as features in frequency domain. Second, build up decision tree with C4.5 algorithm and forecast samples. Feedback the node attributes information to EMD for reducing the calculation of needless attribute and optimizing signal processing algorithm. The experimental bearing vibration signal in accordance with the following conditions: normal, inner race fault, outer race fault and balls fault. The experiment result illustrated that the correct ratio of diagnosis is high and efficient. The method proposed is high accurate and useful for safe production.

In order to extract the vibration feature of remanufactured engines, the vibration signal is decomposed by ensemble empirical mode decomposition(EEMD), and this method is applied to study vibration mode of remanufactured engines. Based on the decomposition of vibration signal, correlation coefficient is introduced to study the correlation between IMF(Intrinsic Mode Function) components and original signal, and sensitive factors of IMF components are calculated using correlation coefficients. Hilbert transform is made with sensitive IMF components. The study results show that vibration feature of remanufactured engines is include in IMF components decomposed by EEMD, the vibration modes of remanufactured engines can be differentiated by Hilbert-Huang spectrum and marginal spectrum based on sensitive IMF components. According to vibration feature, the vibration state of remanufactured engines is divided into cylinder wall, cylinder cover and crankshaft vibration modes, which are important to improve the remanufacture level of engines.

To investigate the nonstationary characteristics of strong typhoons, this paper considers the evolutionary spectral characteristics of strong typhoons based on the Hilbert-Huang transform (HHT). Discrete expressions are determined for the evolutionary spectral analysis based on the HHT. The study indicates that the classic empirical mode decomposition (EMD) method fails to extract all of the high-frequency fluctuations from wind velocity data, and the time-averaged power spectrum obtained directly using the HHT cannot provide the true wind velocity spectrum. The degrees of nonstationarity of different-order IMF components are analysed and a synthesized method of analysing the evolutionary spectrum and time-averaged power spectrum of a strong typhoon is proposed. To avoid the energy leakage problem that exists in HHT spectral analyses, the Gram-Schmidt method is used to orthogonalize the intrinsic mode function (IMF) components. The study indicates that when the orthogonalization is implemented in accordance with the sequence from high-order IMF components to low-order ones, the orthogonalized components retain the same good Hilbert property as that of the IMFs. The synthesized method proposed yields a time-averaged power spectrum that is consistent with the Fourier spectrum in value and can produce the energy distributions of a typhoon in the time and frequency domains simultaneously.

A braking intention identification method based on empirical mode decomposition (EMD) algorithm and entropy theory for electric vehicles is proposed. EMD algorithm is given to decompose nonstationary brake pedal signal to stationary intrinsic mode function (IMF), which is the base of data mining. After that, entropy theory is used to extract brake pedal signal features. A braking intention identification model is built based on fuzzy c-means clustering algorithm. The hardware and software for braking intention identification system based on this method is set up to do offline and real-time experiments. The results show that the identification method proposed in this paper has good real-time quality and can distinguish moderate braking intention and gentle braking intention better.

The key to the fault diagnosis of rotating machinery is to extract the fault characteristics from the vibration signal of rotating machinery. Signal analysis and processing are the most commonly used methods for feature extraction. In this paper, a method combining the empirical mode decomposition (EMD) and spectral kurtosis is presented for fault detection of rotating machinery based on vibrational signal. According to the modulation characteristic of the rolling element bearing fault signal and the disadvantage of depending on the experience to select resonance high frequency band, an improved method integrating EMD with spectrum kurtosis for rolling bearing fault diagnosis is put forward. Firstly, the bearing fault signal is decomposed into a series of intrinsic mode function (IMF) through EMD method. Secondly, the false IMF components are eliminated through mutual information, kurtosis and cross-correlation, then the useful IMFs are selected to construct the fault signal. Finally, by designing the optimal band pass filter with the spectral kurtosis, it can be obtained that, the envelope demodulation spectrum of the filtered signal, and the fault feature of rolling bearing are extracted. The experimental result of the rolling bearing fault diagnosis shows that the proposed method can effectively extract the weak fault feature of rolling bearing, which is superior to envelope analysis.

Ultra-short-term wind speed forecasting based on EMD-VAR model and spatial correlation

Comprehensive fatigue estimation and fault diagnosis based on Refined Generalized Multi-Scale Entropy method of centrifugal fan blades

The field of communications has been revolutionized by the advent of optical fiber. Optical fiber now connects most of the world carrying a vast amount of information through a very limited physical medium. To the materials engineer, optical fiber technology has stimulated interest in a new type of micro-sensor application, where the size, weight, and the ability to integrate the sensor into a material structure play a major role. Areas of interest currently include materials characterization, cure monitoring, and structural health monitoring. The fiber Bragg grating (FBG) is such a sensor technology, creating an optical strain gauge within the core of an optical fiber through the use of a wavelength specific filter. As the FBG experiences induced strain along its major axis, its light signal indicates the amount of strain with great accuracy and sensitivity. In FBG applications, where loading may occur in all directions, complex changes take place in the FBG signal. As these changes impact the usefulness of the FBG as a strain sensor, this thesis endeavors to study and demonstrate the effect of transverse load on the FBG structure. A review of the basics of optical fiber fabrication, light transmission, and optical fiber sensor technology is presented first, followed by a detailed discussion of the FBG's manufacturing and operation. The crux of the thesis is presented with the results of experiments conducted to characterize the FBG signal in both unloaded and transversely loaded configurations. Polarization and wavelength based experiments and analyses are conducted to detect changes in the FBG signal. The results of the study are used to propose cause and effect relationships between transverse loading, its effect on the material properties of the FBG, and changes in the FBG signal.