MVMD-MOMEDA-TEO Model and Its Application in Feature Extraction for Rolling Bearings.

Weak fault feature extraction of rolling bearings based on improved ensemble noise-reconstructed EMD and adaptive threshold denoising

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.

Multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is a powerful method that can extract the periodic characteristics of signal effectively, but this method needs to evaluate the fault cycle a priori, and moreover, the results obtained in a complex environment are easily affected by noise. These drawbacks reduce the application of MOMEDA in engineering practice greatly. In order to avoid such problems, in this paper, we propose an adaptive fault diagnosis method composed of two parts: fault information integration and extracted feature evaluation. In the first part, a Teager energy spectrum amplitude factor (T‐SAF) is proposed to select the intrinsic mode function (IMF) components decomposed by ensemble empirical mode decomposition (EEMD), and a combined mode function (CMF) is proposed to further reduce the mode mixing. In the second part, the particle swarm optimization (PSO) taking fractal dimension as the objective function is employed to choose the filter length of MOMEDA, and then the feature frequency is extracted by MOMEDA from the reconstructed signal. A cyclic recognition method is proposed to appraise the extracted feature frequency, and the evaluation system based on threshold and weight coefficient removes the wrong feature frequency. Finally, the feasibility of the method is verified by simulation data, experimental signals, and on‐site signals. The results show that the proposed method can effectively identify the bearing state.

Incipient fault feature extraction of rolling bearings based on the MVMD and Teager energy operator

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After a prolonged use of a faulty bearing, cracks are created on more than one parts of the bearing, which is a compound fault condition. This situation is tougher than the single fault condition. This combined faulty bearing creates a complex vibration signal with significant amount of noise, where it becomes very difficult to identify the fault frequencies by signal processing methods. This paper deals with a novel machine learning method for the compound fault diagnosis of Rolling bearing, where compound fault signals are decomposed into Intrinsic Mode Functions (IMF) by Ensemble Empirical Mode Decomposition (EEMD). The proposed method uses Convolution NeuralNetwork (CNN) based technique, which receives the decomposed signals of compound fault signal as input to CNN. These IMFs consists of groups of different frequencies. When these IMFs are given as input to CNN it classifies it effectively into different faults existing on bearing. CNN yields almost 96% accuracy which is better than any other previous performance for compound faultclassification.

Fault diagnosis of rolling bearings is of great significance for the monitoring and maintenance of mechanical systems. Spectral amplitude modulation is an automated nonlinear filtering method known for its good robustness. However, under different magnitude order (MO) weights, the amplitudes of interference components may be amplified, which can affect the accuracy of fault diagnosis. Therefore, selecting an optimal range for MO is crucial. To address the above issue, an adaptive spectral amplitude modulation (ASAM) for rolling bearing fault feature extraction based on encoder signals is proposed. Firstly, the encoder signal is processed using the forward difference method to obtain the instantaneous angular speed signal, followed by the Fourier Transform to derive its amplitude and phase. Subsequently, while maintaining the phase unchanged, different weights are assigned to the amplitude, with the MO range set from −0.5 to 10. An inverse Fourier Transform is then performed to obtain the modified signal. The normalized squared envelope spectrum of the modified signal is obtained through the Hilbert Transform. Next, a Normalized Hoyer Envelope Spectrum Energy Ratio index and an adaptive threshold are established to characterize and differentiate the richness of fault information in the modified signals, enabling adaptive selection of the MO range. Finally, a normalized squared envelope spectrum with an adaptively selected MO range is generated to extract the fault features of rolling bearings. The effectiveness of the ASAM is validated through simulation and experiments.

The working environment of rotating machines is complex, and their key components are prone to failure. The early fault diagnosis of rolling bearings is of great significance; however, extracting the single scale fault feature of the early weak fault of rolling bearings is not enough to fully characterize the fault feature information of a weak signal. Therefore, aiming at the problem that the early fault feature information of rolling bearings in a complex environment is weak and the important parameters of Variational Modal Decomposition (VMD) depend on engineering experience, a fault feature extraction method based on the combination of Adaptive Variational Modal Decomposition (AVMD) and optimized Multiscale Fuzzy Entropy (MFE) is proposed in this study. Firstly, the correlation coefficient is used to calculate the correlation between the modal components decomposed by VMD and the original signal, and the threshold of the correlation coefficient is set to optimize the selection of the modal number . Secondly, taking Skewness (Ske) as the objective function, the parameters of MFE embedding dimension M, scale factor S and time delay T are optimized by the Particle Swarm Optimization (PSO) algorithm. Using optimized MFE to calculate the modal components obtained by AVMD, the MFE feature vector of each frequency band is obtained, and the MFE feature set is constructed. Finally, the simulation signals are used to verify the effectiveness of the Adaptive Variational Modal Decomposition, and the Drivetrain Dynamics Simulator (DDS) are used to complete the comparison test between the proposed method and the traditional method. The experimental results show that this method can effectively extract the fault features of rolling bearings in multiple frequency bands, characterize more weak fault information, and has higher fault diagnosis accuracy.

Aiming to address the nonlinear and non-stationary characteristics of rolling bearing fault vibration signals, traditional diagnostic methods face challenges such as mode mixing, insufficient feature discrimination, and sensitivity to classifier parameters, which limit further improvements in diagnostic accuracy. This paper proposes a comprehensive optimization framework for fault diagnosis, covering signal decomposition and fault feature classification. First, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is applied to decompose the signal into intrinsic mode functions (IMF), effectively suppressing mode mixing. Next, the correlation coefficient and multiscale permutation entropy (MPE) are employed to quantify the dynamic complexity of each IMF across multiple scales, enabling the extraction of highly discriminative multidimensional feature vectors. Finally, the black-winged kite algorithm (BKA) is used to optimize the penalty factor and kernel parameters of the least squares support vector machine (LSSVM). BKA achieves a dynamic balance between local exploitation and global exploration, and its integration with Cauchy mutation improves robustness to noise, thereby enhancing convergence speed and stability. Experimental results from publicly available datasets and a custom experimental platform show that the proposed approach effectively distinguishes among various fault types, offering superior performance, improved interpretability, and enhanced robustness.

To realize accurate fault diagnosis of rolling bearing under random noise, a novel fault diagnosis method based on ensemble empirical mode decomposition (EEMD) and optimized Elman_AdaBoost is proposed in this paper. First, the EEMD method is used to decompose the original vibration signal into several intrinsic mode functions (IMFs). Then, the correlation coefficient and kurtosis of IMFs are used to remove any excess ingredients, and the signal is reconstructed by using the rest of IMFs. In addition, based on the analysis of the time–frequency characteristics of the reconstructed signal, the root-mean-square value and the power spectrum center are extracted as a 2-D feature vector from each reconstructed signal and regarded as the input characteristic vector of the Elman neural network. Combined with the AdaBoost classification algorithm, the Elman_AdaBoost algorithm is improved and proposed to establish the strong classifier. Finally, based on the vibration data recorded in the bearing center of the Case Western Reserve University, three kinds of bearings with different faults and normal bearings are selected as the sample data and the training samples and test samples are constructed. Experimental results show that the proposed EEMD method and optimized Elman_AdaBoost model can effectively diagnose the rolling bearing under random noise, and has the advantages of fast speed, small error, and stable performance. Comparing with the traditional Elman_AdaBoost and single Elman neural network algorithm, this paper with fewer characteristics can get better accuracy and real-time processing performance, and presents a simple and practical resolution for the fault diagnosis of rolling bearings.

Automatic monitoring of group‐housed pigs in real time through porcine acoustic signals has played a crucial role in automated farming. In the process of data collection and transmission, acoustic signals are generally interfered with noise. In this paper, an effective porcine acoustic signal denoising technique based on ensemble empirical mode decomposition (EEMD), independent component analysis (ICA), and wavelet threshold denoising (WTD) is proposed. Firstly, the porcine acoustic signal is decomposed into intrinsic mode functions (IMFs) by EEMD. In addition, permutation entropy (PE) is adopted to distinguish noise‐dominant IMFs from the IMFs. Secondly, ICA is employed to extract the independent components (ICs) of the noise‐dominant IMFs. The correlation coefficients of ICs and the first IMF are calculated to recognize noise ICs. The noise ICs will be removed. Then, WTD is applied to the other ICs. Finally, the porcine acoustic signal is reconstructed by the processed components. Experimental results show that the proposed method can effectively improve the denoising performance of porcine acoustic signal.

The analysis of electroencephalogram (EEG) signals is becoming more important because of the time-consuming and large bias of traditional visual detection technology, especially in diagnosis of epilepsy. Based on the nonlinear and non-stationary of the EEGs, empirical mode decomposition (EMD) is applied to decompose the original signals into intrinsic mode functions (IMFs). In this paper, after getting the IMFs, the fluctuation index and variation coefficient are calculated to analyze the amplitude change of IMFs. In order to better reflect the information of EEG signals, Hilbert transform is applied to obtain the instantaneous frequency for each IMF. Then, the novel feature named fluctuation index and variation coefficient of instantaneous frequency for IMFs are calculated. Furthermore, feature based on sample entropy of the first order difference is extracted. Finally, both of the calculated features will put together as a fusion feature into SVM for classification. The proposed method is evaluated using the Boon epileptic dataset and the highest average classification accuracy is 99.59%, showing a powerful method to detect seizure.

Quantitative assessment for characterization of breast lesion tissues using adaptively decomposed ultrasound RF images

In power distributed generation system, analysis of power quality is a major issue and hence it is essential to implement an efficient power quality system. To address and end this issue, the proposed work is mainly focused on the detection and classification of power quality disturbances or events by applying Hilbert–Huang transform (HHT) technique. HHT technique is a novel signal processing algorithm that comprises two processes. First is the empirical mode decomposition (EMD), which is an iterative process where N number of power quality signals are decomposed into intrinsic mode functions (IMFs). The second process, Hilbert transform, is applied to each IMF component. The advantage of using these two processes makes it an attractive power tool for quality event analysis. Once the IMF is obtained, it is easy to construct a time–frequency representation model. Then, the significant features are extracted from amplitude, phase and frequency instantly, which are the contour of IMFs of each power quality disturbance. For classification, adaptive neuro-fuzzy system (NFS) classifier is used. It is an intelligent system used for the combination of neural network and fuzzy logic. Power quality features are given as input to the NFS system. The experimental simulation is conducted in a MATLAB environment, and proposed HHT has higher efficiency compared to existing methods.

Identification of planetary gearbox weak compound fault based on parallel dual-parameter optimized resonance sparse decomposition and improved MOMEDA