Search

Aiming at the facts that the modulation signal bispectrum (MSB) might only process stationary signals, a novel method for fault feature extraction of rolling bearings was proposed based on the WAEEMD and MSB. Firstly, vibration signals of roiling bearings were decomposed into a list of intrinsic mode uncrowns (IMFs) by ensemble empirical mode decomposition (EEMD). Subsequently, the IMFs were reconstructed into the WAEEMD inhered signals using the weighted average method based on Teager energy kurtosis (TEK). Finally, the MSB was used to decompose the modulated components in the WAEEMD filtered signals and extract the fault characteristic frequencies. The analysis results illustrate that the WAEEMD-MSB has a superior performance over fast kurtogram (FK) and EEMD-MSB in extracting bearing fault features. [ABSTRACT FROM AUTHOR]

In this paper, a novel model for fault detection of rolling bearing is proposed. It is based on a high-performance support vector machine (SVM) that is developed with a multifeature fusion and self-regulating particle swarm optimization (SRPSO). The fundamental of multikernel least square support vector machine (MK-LS-SVM) is overviewed to identify a classifier that allows multidimension features from empirical mode decomposition (EMD) to be fused with high generalization property. Then the multidimension parameters of the MK-LS-SVM are configured by the SRPSO for further performance improvement. Finally, the proposed model is evaluated through experiments and comparative studies. The results prove its effectiveness in detecting and classifying bearing faults. [ABSTRACT FROM AUTHOR]

Transient impulses are important information for machinery fault diagnosis. However, the transient features contained in the vibration signals generated by planetary gearboxes are usually immersed by a large amount of background noise and harmonic components. Even mathematical morphology (MM) is an excellent anti-noise signal processing method that can directly extract the geometry of impulse features in the time domain, but the four basic operators of MM can only extract one-way impulses while cannot extract the bidirectional impulses effectively at the same time. To accurately extract the impulse feature information, a novel method for fault detection of planetary gearbox based on an enhanced average (EAVG) filter and modulated signal bispectrum (MSB) is proposed. Firstly, the properties of the extracted impulses based on the four basic operators of MM will be divided into two categories of enhanced average operators. The four EAVG filters consist of the average weighted combination of enhanced average operators, and then the best EAVG filter is selected based on correlation coefficient to implement on the original vibration signal. It allows EAVG filter to extract positive and negative impulses of vibration signal, thereby improving the accuracy of planetary gearbox fault detection. Subsequently, the performance of the EAVG filter is influenced by the length of its structural element (SE), which is adaptively determined using an indicator based kurtosis. Then, the EAVG filter selects the optimal SE length to eliminate the interference of background noise and harmonic components to enhance the impulse components of the vibration signal. However, the nonlinear modulation components that are related to the fault types and severities are not extracted exactly and still remained in the filtered signal by EAVG. Finally, the MSB is utilized to the EAVG filtered signal to decompose the modulated components and extract the fault features. The advantages of EAVG over average (AVG) filter are clarified in the simulation study. In addition, the EAVG-MSB is validated by analyzing the vibration signals of planetary gearboxes with sun gear chipped tooth, sun gear misalignment and bearing inner race fault. The results indicate that the EAVG-MSB is effective and accurate in feature extraction compared with the combination morphological filter-hat transform (CMFH) and average combination difference morphological filter (ACDIF), and the feasibility of the EAVG-MSB are proved for planetary gearbox condition monitoring and fault diagnosis. • EAVG filter is used to extract the positive and negative impulses. • Kurtosis is taken as a novel criterion to optimize the SE length of EAVG. • MSB is used to extract fault feature for planetary gearbox fault detection. • Experimental results prove that the EAVG-MSB outperforms the ACDIF and CMFH. [ABSTRACT FROM AUTHOR]

As the critical parts of wind turbines, rolling bearings are prone to faults due to the extreme operating conditions. To avoid the influence of the faults on wind turbine performance and asset damages, many methods have been developed to monitor the health of bearings by accurately analyzing their vibration signals. Stochastic resonance (SR)-based signal enhancement is one of effective methods to extract the characteristic frequencies of weak fault signals. This paper constructs a new SR model, which is established based on the joint properties of both Power Function Type Single-Well and Woods-Saxon (PWS), and used to make fault frequency easy to detect. However, the collected vibration signals usually contain strong noise interference, which leads to poor effect when using the SR analysis method alone. Therefore, this paper combines the Fourier Decomposition Method (FDM) and SR to improve the detection accuracy of bearing fault signals feature. Here, the FDM is an alternative method of empirical mode decomposition (EMD), which is widely used in nonlinear signal analysis to eliminate the interference of low-frequency coupled signals. In this paper, a new stochastic resonance model (PWS) is constructed and combined with FDM to enhance the vibration signals of the input and output shaft of the wind turbine gearbox bearing, make the bearing fault signals can be easily detected. The results show that the combination of the two methods can detect the frequency of a bearing failure, thereby reminding maintenance personnel to urgently develop a maintenance plan. [ABSTRACT FROM AUTHOR]

To realize the accurate fault detection of rolling element bearings, a novel fault detection method based on non-stationary vibration signal analysis using weighted average ensemble empirical mode decomposition (WAEEMD) and modulation signal bispectrum (MSB) is proposed in this paper. Bispectrum is a third-order statistic, which can not only effectively suppress Gaussian noise, but also help identify phase coupling. However, it cannot effectively decompose the modulation components which are inherent in vibration signals. To alleviate this issue, MSB based on the modulation characteristics of the signals is developed for demodulation and noise reduction. Still, the direct application of MSB has some interfering frequency components when extracting fault features from non-stationary signals. Ensemble empirical mode decomposition (EEMD) is an advanced nonlinear and non-stationary signal processing approach that can decompose the signal into a list of stationary intrinsic mode functions (IMFs). The proposed method takes advantage of WAEEMD and MSB for bearing fault diagnosis based on vibration signature analysis. Firstly, the vibration signal is decomposed into IMFs with a different frequency band using EEMD. Then, the IMFs are reconstructed into a new signal by the weighted average method, called WAEEMD, based on Teager energy kurtosis (TEK). Finally, MSB is applied to decompose the modulated components in the reconstructed signal and extract the fault characteristic frequencies for fault detection. Furthermore, the efficiency and performance of the proposed WAEEMD-MSB approach is demonstrated on the fault diagnosis for a motor bearing outer race fault and a gearbox bearing inner race fault. The experimental results verify that the WAEEMD-MSB has superior performance over conventional MSB and EEMD-MSB in extracting fault features and has precise and effective advantages for rolling element bearing fault detection. [ABSTRACT FROM AUTHOR]

Abstract: In this study, a novel fault diagnosis system for rotating machinery using thermal imaging is proposed. This system consists of bi-dimensional empirical mode decomposition (BEMD) for image enhancement, a generalized discriminant analysis (GDA) for feature reduction, and a relevance vector machine (RVM) for fault classification. Firstly, the thermal image obtained from machine conditions is decomposed into intrinsic mode functions (IMFs) by using BEMD. At each decomposed level, the IMF is expanded and fused with the residue by gray-scale transformation and principal component analysis fusion technique, respectively. The enhanced image is then formed by the improved IMFs in reconstruction process. Subsequently, feature extraction is applied for the enhanced images to obtain histogram features which characterize the thermal image and contain useful information for diagnosis. The high dimensionality of the achieved feature set can be reduced by GDA implementation. Moreover, GDA also assists in the increase of the feature cluster separation. Finally, the diagnostic results are performed by RVM. The proposed system is applied and validated with the thermal images of a fault simulator. A comparative study of the classification results obtained from RVM, support vector machines, and adaptive neuro-fuzzy inference system is also performed to appraise the accuracy of these models. The results show that the proposed diagnosis system is capable of improving the classification accuracy and efficiently assisting in rotating machinery fault diagnosis. [Copyright &y& Elsevier]

• A multiscale denoising branch is developed to extract multi-level information and reduce noise interference. • An improved flow direction strategy-based adaptive resonance branch is introduced to learn fault-related periodic impulsive features. • Case studies are conducted to validate the efficacy of the developed IFD-MDCN. Rolling bearings are the core components of rotating machinery, and their normal operation is crucial to the entire industrial production. Most existing condition monitoring methods have been devoted to extracting discriminative features from vibration signals that reflect bearing health status information. However, the complex working conditions of rolling bearings often make the periodic impulsive characteristics related to fault information easily buried in noise interferences. Therefore, it is challenging for existing approaches to learning discriminative fault-related features in these scenarios. To address this issue, a novel multibranch CNN named IFD-MDCN is developed in this paper, which represents multibranch denoising convolutional networks (MDCN) with an improved flow direction (IFD) strategy. The main contributions of this work include: (1) designing a multiscale denoising branch to extract multi-level information and reduce noise impact. More specifically, the multiscale denoising branch adopts a Gaussian multi-level noise reduction procedure to represent vibration signals at multiple levels and filter out the noise components, and then it uses a multiscale convolutional module to extract abundant features from these denoised signal representations; (2) establishing an improved flow direction strategy-based adaptive resonance branch to learn periodic impulsive features associated with fault information from vibration signals. Extensive experimental results reveal that the IFD-MDCN outperforms five state-of-the-art approaches, especially in strong noise scenarios. [ABSTRACT FROM AUTHOR]

Highlights • A novel multi-stage noise reduction approach is developed. • EEMD and WT based denoising method is applied. • All IMFs are investigated and utilized for fault feature extraction. • MSB is used to extract fault feature for bearing fault diagnosis accurately. Abstract To extract impulsive feature from vibration signals with strong background noise and interference components for accurate bearing diagnostics. A multi-stage noise reduction method is proposed based on ensemble empirical mode decomposition (EEMD), wavelet thresholding (WT) and modulation signal bispectrum (MSB). Firstly, noisy vibration signals are applied with EEMD to obtain a list of intrinsic mode functions (IMFs) that leverage the desired modulation components to different degrees. Then, a number of initial IMFs in the high frequency range, which are separated by using the mean of the standardized accumulated modes (MSAM) to have more modulation contents, are further denoised by a wavelet thresholding approach. These cleaned IMFs in the high frequency are combined with the low frequency IMFs to construct a pre-denoised signal that maintains the modulation properties of the raw signal. Finally, modulation signal bispectrum (MSB) is used to extract the modulation signature by suppressing further the residual random noise and deterministic interference components. This multi-stage noise reduction method was validated through a simulation study and two experimental fault cases studies of rolling element bearing. The results were more accurate and reliable in diagnosing the bearing inner and outer race defects in comparison with the individual use of the state-of-the-art EEMD and MSB. [ABSTRACT FROM AUTHOR]