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Analog circuit is an crucial component of electronic equipment, and the ability to diagnose its fault state quickly and accurately is essential for ensuring the safety and reliability of these electronic equipment. This paper addresses the problems of low diagnostic accuracy and the difficulties associated with model parameter selection in traditional fault diagnosis methods, particularly when dealing with nonlinear and non-stationary fault signals. A fault diagnosis method for analog circuits is proposed, which utilizes Ensemble Empirical Mode Decomposition (EEMD) for features extraction, the Maximum Information Coefficient algorithm (MIC) for features selection, and Particle Swarm Optimization (PSO) for optimizing Support Vector Machine (SVM) classification. Firstly, EEMD is used to adaptively decompose the fault signals in the circuit to extract multi-scale fault features. Secondly, the extracted features are quantitatively evaluated using the Pearson correlation coefficient and energy value analysis, leading to the construction of a fault feature vector is constructed. On this basis, the MIC feature selection algorithm is applied to further optimize the feature vector. Finally, an efficient fault classification model is developed by optimizing the hyperparameters of the SVM model using PSO. The simulation results show that the proposed method effectively overcome the problems of model complexity and low classification accuracy caused by improper selection of wavelet basis function. The accuracy of fault diagnosis and the efficiency of model training are significantly superior to those of traditional methods.

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With the increase of service time, the rotation period of rotating machinery may become irregular, and the Ensemble Empirical Mode Decomposition (EEMD)can effectively reflect its periodic state. In order to accurately evaluate the working state of the Rotate Vector (RV) reducer, the torque transfer formula of the RV reducer is first derived to theoretically prove periodicity of torque transfer in normal operation. Then, EEMD is able to effectively reflect the characteristics of data periodicity. A fault diagnosis model based on EEMD-MPA-KELM was proposed, and a bearing experimental dataset from Xi'an Jiaotong University was used to verify the performance of the model. In view of the characteristics of the industrial robot RV reducer fault was not obvious and the sample data is few, spectrum diagram was used to diagnose the fault from the RV reducer measured data. The EEMD decomposition was performed on the data measured by the RV reducer test platform to obtain several Intrinsic Mode Functions (IMF). After the overall average checking and optimization of each IMF, several groups of eigenvalues were obtained. The eigenvalues were input into the Kernel Extreme Learning Machine (KELM) optimized by the Marine Predators Algorithm (MPA), and the fault diagnosis model was established. Finally, compared with other models, the prediction results showed that the proposed model can judge the working state of RV reducer more effectively. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The vibration signals on marine blowers are non-linear and non-stationary. In addition, the equipment in marine engine room is numerous and affects each other, which makes it difficult to extract fault features of vibration signals in the time domain. This paper proposes a fault diagnosis method based on the combination of Ensemble Empirical Mode Decomposition (EEMD), an Autoregressive model (AR model) and the correlation coefficient method. Firstly, a series of Intrinsic Mode Function (IMF) components were obtained after the vibration signal was decomposed by EEMD. Secondly, effective IMF components were selected by the correlation coefficient method. AR models were established and the power spectrum was analysed. It was verified that blower failure can be accurately diagnosed. In addition, an intelligent diagnosis method was proposed based on the combination of EEMD energy and a Back Propagation Neural Network (BPNN), with a correlation coefficient method to get effective IMF components, and the energy components were calculated, normalised as a feature vector. Finally, the feature vector was sent to the BPNN for training and state recognition. The results indicated that the EEMD-BPNN intelligent fault diagnosis method is suitable for higly accurate fault diagnosis of marine blowers.

When the gearbox fails， its vibration signal is unstable and nonlinear. The commonly applied gearbox fault diagnosis methods are almost all based on the single-channel vibration signal analysis，which is easy to cause the loss of fault information，so the practicality in industrial production is limited. In order to overcome this defect， the multivariate multiscale dispersion entropy is introduced into the gearbox fault diagnosis. Meanwhile， the coarse graining method of it is improved， and improved multivariate multiscale dispersion entropy（IMMDE） is proposed to extract fault information of multi-channel vibration signal of gearbox. Based on this，a new gearbox fault diagnosis method based on ensemble empirical mode decomposition （EEMD），IMMDE and genetic algorithm optimization support vector machine（GA-SVM） is proposed. Through the analysis of experimental data， it can be seen that this method has higher accuracy and stability compared with multivariate multiscale sample entropy（MMSE） and multivariate multiscale fuzzy entropy （MMFE），and has obvious advantages in dealing with short time series.

The principal objective of this paper is to detect and automatically monitor switch open-circuit faults in a two-level three-phase voltage source inverter fed induction motor from the processing of its current signals. The proposed diagnostic method uses both signal processing techniques and machine learning techniques in order to detect and localize the switch under an open-circuit fault. First, the Hilbert-Huang transform using the empirical ensemble mode decomposition is employed for each phase current signal, which leads to extracting the intrinsic mode functions. In order to optimally choose the function indicating the open-circuit fault harmonic, two factors, namely, the root mean square and the correlation coefficient are calculated out for each function. In this regard, two criteria are proposed that lead to choose the optimal function giving better information about the defected phase. The spectral envelope of the optimal function permits extra0cting the fault harmonic of the switch. Second, different machine learning techniques are applied to locate and classify the switch open-circuit faults with the hyper-parameters optimization for a better design of the different models. Finally, a comparative study of the different machine learning techniques is carried out for determining the best classifier for the open-circuit faults. The experimental results effectively demonstrate a very high classification rate of 98.98%. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the increase of service time, the rotation period of rotating machinery may become irregular, and the Ensemble Empirical Mode Decomposition (EEMD)can effectively reflect its periodic state. In order to accurately evaluate the working state of the Rotate Vector (RV) reducer, the torque transfer formula of the RV reducer is first derived to theoretically prove periodicity of torque transfer in normal operation. Then, EEMD is able to effectively reflect the characteristics of data periodicity. A fault diagnosis model based on EEMD-MPA-KELM was proposed, and a bearing experimental dataset from Xi‘an Jiaotong University was used to verify the performance of the model. In view of the characteristics of the industrial robot RV reducer fault was not obvious and the sample data is few, spectrum diagram was used to diagnose the fault from the RV reducer measured data. The EEMD decomposition was performed on the data measured by the RV reducer test platform to obtain several Intrinsic Mode Functions (IMF). After the overall average checking and optimization of each IMF, several groups of eigenvalues were obtained. The eigenvalues were input into the Kernel Extreme Learning Machine (KELM) optimized by the Marine Predators Algorithm (MPA), and the fault diagnosis model was established. Finally, compared with other models, the prediction results showed that the proposed model can judge the working state of RV reducer more effectively.

Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To solve the problem that fault features are difficult to extract and the time-frequency features cannot fully represent the state information, a novel method is proposed in this paper based on the whale optimization algorithm (WOA) and the kernel extreme learning machine (KELM). First, the vibration signals are processed by the ensemble empirical mode decomposition and sample entropy to obtain the feature vectors. Based on this, a KELM model for fault diagnosis is established. Then, the penalty factor and the kernel parameters in the KELM are optimized by WOA to improve the stability and classification accuracy. Taking faults of a ball-screw pair on a linear feed table as a case, the experimental results indicate that the proposed method can effectively extract the fault features of the ball-screw pair, and it can achieve higher classification accuracy, faster convergence speed, and greater convergence precision than the existing fault diagnosis methods.

In view of the nonlinear,non-stationary and massive noise features of reciprocating pump power of rolling bearing vibration signal,a fault diagnosis method of rolling bearing based on EEMD,distance factor,correlation coefficient and wavelet packet decomposition is proposed. By measuring the vibration signals of bearings in the bearing life test and decomposing the signals by using the method of EEMD,a number of IMF components is got. Then,the energy characteristic signal vectors is obtained through wavelet packet decomposing and constructing of reconstructed vibration signals,which got from the selecting and reconstructing of IMF components using the method of combination of distance factor and correlation coefficient. At last,the type of failure is determined based on the absolute value of the difference in the correlation coefficient of the energy characteristic signal vectors. Compared with the direct correlation coefficient analysis of the bearing vibration signal,this method has a high fault recognition rate,and doesn’t require a large amount of data training needed by neural networks,it is a better bearing fault identification method.

This study presents a differential protection scheme based on ensemble empirical mode decomposition (EEMD) for AC microgrid. The fault current level is significantly lower during the islanded operation of a microgrid, which leads to the malfunction of the traditional over-current protection scheme. The proposed differential protection scheme uses the spectral differential energy of the first intrinsic mode function extracted from the decomposition of the current signal using EEMD for effective fault detection in the AC microgrid. The proposed EEMD-based differential protection scheme is validated on 10 bus and modified IEEE 34-bus AC microgrid test systems during various shunt faults. Moreover, the performance of the proposed EEMD-based differential protection scheme is evaluated under high fault impedance scenarios. The simulation results reveal that the proposed differential protection scheme can effectively detect the faulty line in an AC microgrid during seamless islanded and grid-tied operations. [ABSTRACT FROM AUTHOR]

Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this work, a novel bearing fault diagnosis method based on EEMD and adaptive redundant lifting scheme packet is proposed. Firstly, EEMD method is used to decompose rolling bearing signals of different fault types, and the correlation coefficient criterion method is carried out in order to screen effective IMF components and reconstruct them. Then, the adaptive redundant lifting scheme packet method is used to denoise the reconstructed signal, and the energy characteristics of different fault signals are extracted. Finally, the bearing fault diagnosis system is constructed by BP neural network diagnosis. The results show that the diagnostic method proposed in this paper has better diagnosis efficiency and precision than the traditional wavelet packet. [ABSTRACT FROM AUTHOR]

Purpose: Since reliability and extended service life of rotating machinery are the industries´ major concerns, fault diagnosis systems are constantly being improved, especially by artificial intelligence methods. Current paper proposes a diagnostic method integrating stationary and non-stationary signal processing techniques, selection of multiple attributes, and classification by machine-learning algorithm. The technique was applied to a small number of measured signals. Method: The integrated method uses the ensemble empirical mode decomposition (EEMD) (which handles nonlinear and non-stationary data) for signal processing, and the support vector machine (SVM) for the classification of the machinery condition with a small number of signals. Augmented data and feature selection with a genetic algorithm are used to improve the accuracy of the analysis. Results and Conclusions: Evaluation was obtained by vibration signals from a rotor test rig with different types of faults. Experimental results showed that the proposed method successfully identifies the rotor´s faults with accuracy of 95.19%. [ABSTRACT FROM AUTHOR]

Fault diagnosis of rope tension is of great significance for safety in hoisting systems. A novel diagnosis method based on the vibration signals of the head sheaves is proposed. First, the signal is decomposed by the ensemble empirical mode decomposition (EEMD); then the main intrinsic module functions (IMFs) are extracted by correlation analysis. Second, the energy and the permutation entropy (PE) of the main IMFs were calculated to create the feature vector. Third, a particle swarm optimization - support vector machine (PSO-SVM) is applied to classify tension states. The effectiveness and advantage of the proposed method are validated by experiments. Compared with the conventional force-sensor-based method, it has clear advantages in sensor installation, data transmission, safety, and reliability. [ABSTRACT FROM AUTHOR]

Blade faults are considered as the most common cause of failure in turbomachines. Any fault occurs in impeller's blades gives the breakdown in these machines and creates undesired vibration. This paper presents a new method which combines wavelet transform (WT) with ensemble empirical mode decomposition (EEMD) method for early identification of blade state. The vibration signals measured from a blade rotor are filtered using the WT, and then the obtained signals are decomposed into intrinsic mode functions (IMFs) by EEMD method to obtain multichannel signals. The correlation coefficient is used as an index to select the effective IMFs. The selected IMFs are then reconstituted and its spectrum is generated. Experimental results validate the usefulness of the proposed method for detecting the blade faults. [ABSTRACT FROM AUTHOR]

The condition monitoring and fault detection of rolling bearing are of great significance to ensure the safe and reliable operation of rotating machinery system. In the past few years, deep neural network (DNN) has been recognized as an effective tool to detect rolling bearing faults. However, It is too complex to directly feed the original vibration signal to the DNN neural network, and the accuracy of fault identification is not high. By using the signal preprocessing technology, the original signal can be effectively removed and preprocessed without losing the key diagnosis information. In this paper, a new EEMD-LSTM bearing fault diagnosis method is proposed, which combines the signal preprocessing technology with the EEMD method that can get clear fault feature signals, and LSTM technology to extract fault features automatically that improves the efficiency of fault feature extraction. In the case of small sample size, this method can significantly improve the accuracy of fault diagnosis. [ABSTRACT FROM AUTHOR]

A novel analysis method is proposed based on ensemble empirical mode decomposition (EEMD) and support vector machines (SVMs) for the fault diagnosis of bevel gears. Firstly, the EEMD method is used to decompose the fluctuations in the original gear noise signals into different timescales so as to obtain several intrinsic mode functions (IMFs). The meshing frequency components in the decomposition results are reconstructed to eliminate the influence of interference noise. Then, time-synchronous averaging (TSA) is applied in further denoising to weaken signals independent of the gear meshing frequency. After denoising, various signal characteristics are calculated. Obvious signal characteristics for different fault states are selected as a set of feature vectors. Finally, a particle optimisation method is used to optimise SVM parameters and the feature vectors are input as training samples into an SVM in order to achieve fault recognition. The experimental results show that this novel analysis method can effectively diagnose different conditions of the bevel gear and achieve an identification rate for gear faults of 98.33%. [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]

Although gearboxes with composite gear trains have been widely used in industrial production, it remains difficult to extract their fault signal features due to relatively complex vibration signals. This paper proposes an effective fault feature extraction method based on ensemble empirical mode decomposition (EEMD) and translation-invariant multiwavelet neighbouring coefficients, through which a clear envelope spectrum of gearbox vibration signals can be obtained. Compared with EEMD denoising or translation-invariant multiwavelet denoising using neighbouring coefficients alone, the method combining both of the denoising approaches can not only effectively suppress the signal noise but also fully retain the fault feature information. The presented method was further experimentally verified using a test rig for a gearbox with a composite gear train. Fault diagnosis was conducted with a single fault, such as snaggletooth and abrasion, as well as mixed faults at different locations. The results have shown that this method can effectively extract the fault features and improve the fault detection rate of a gearbox with a composite gear train. [ABSTRACT FROM AUTHOR]

Summary: Fault detection of rotating machinery is very important for its performance degradation assessment. In this work, an effective feature learning and detecting method based on the ensemble empirical mode decomposition (EEMD) and Gaussian process classifier (GPC) is put forward. Compared with the traditional parameter optimization methods of GPC, this work proposed a bacterial foraging optimization as the optimal solution of the hyperparameters of GP model. To find a valid feature vector, this work also utilized EEMD to decompose the vibration signals and get some time‐frequency features. Then, treelet transform is proposed to reduce the feature dimension. The results of some applications indicate that the EEMD has stronger processing capability of the status signals of rotating machinery. Treelet can transform the high‐dimensional vector to low‐dimensional space, which is used as the input of the proposed BFO‐GP model. The proposed diagnosis method can identify not only the optimal feature vector but also the fault locations. [ABSTRACT FROM AUTHOR]

Aiming at the impact feature in fault signals of the rolling bearings,The improved algorithm diagnosis method EEMD-RA-KU was proposed to capture the impact feature. Firstly,Fault signals were decomposed into a series of intrinsic mode functions（ IMFs） with ensemble empirical mode decomposition（ EEMD）. Secondly,The IMFs were selected to reconstruct signals based on relative analysis（ RA） and kurtosis（ KU）. Thirdly,The reconstructed signals were filtered by the filter that the parameters were determined with the spectral kurtosis. Finally,The fault can be diagnosed by comparing the frequency of the envelope spectrum with the fault characteristic frequency of the bearing. The result shows that the fault feature information of the signal can be extracted accurately when there was a lot of background noise.

Detection technology of underwater pipeline leakage plays an important role in the subsea production system. In this paper, a new method based on the acoustic leak signal collected by a hydrophone is proposed to detect pipeline leakage in the subsea production system. Through the pipeline leakage test, it is found that the radiation noise is a continuous spectrum of the medium and high-frequency noise. Both the increase in pipe pressure and the diameter of the leak hole will narrow the spectral structure and shift the spectrum center towards the low frequencies. Under the same condition, the pipe pressure has a greater impact on the noise; every 0.05 MPa increase in the pressure, the radiation sound pressure level increases by 6-7 dB. The time-frequency images were obtained by processing the acoustic signals using the Ensemble Empirical Mode Decomposition (EEMD) and Hilbert–Huang transform (HHT), and fed into a two-layer Convolutional Neural Network (CNN) for leakage detection. The results show that CNN can correctly identify the degree of pipeline leakage. Hence, the proposed method provides a new approach for the detection of pipeline leakage in underwater engineering applications.

Feature extraction is one of the challenging problems in fault diagnosis, and it has a direct bearing on the accuracy of fault diagnosis. Therefore, in this paper, a new method based on ensemble empirical mode decomposition (EEMD), wavelet semi-soft threshold (WSST) signal reconstruction, and multi-scale entropy (MSE) is proposed. First, the EEMD method is applied to decompose the vibration signal into intrinsic mode functions (IMFs), and then, the high-frequency IMFs, which contain more noise information, are screened by the Pearson correlation coefficient. Then, the WSST method is applied for denoising the high-frequency part of the signal to reconstruct the signal. Secondly, the MSE method is applied for calculating the MSE values of the reconstructed signal, to construct an eigenvector with the complexity measure. Finally, the eigenvector is input to a support vector machine (SVM) to find the fault diagnosis results. The experimental results prove that the proposed method, with a better classification performance, can better solve the problem of the effective signal and noise mixed in high-frequency signals. Based on the proposed method, the fault types can be accurately identified with an average classification accuracy of 100%.

Fault diagnosis of rope tension is significantly important for hoisting safety, especially in mine hoists. Conventional diagnosis methods based on force sensors face some challenges regarding sensor installation, data transmission, safety, and reliability in harsh mine environments. In this paper, a novel fault diagnosis method for rope tension based on the vibration signals of head sheaves is proposed. First, the vibration signal is decomposed into some intrinsic mode functions (IMFs) by the ensemble empirical mode decomposition (EEMD) method. Second, a sensitivity index is proposed to extract the main IMFs, then the de-noised signal is obtained by the sum of the main IMFs. Third, the energy and the proposed improved permutation entropy (IPE) values of the main IMFs and the de-noised signal are calculated to create the feature vectors. The IPE is proposed to improve the PE by adding the amplitude information, and it proved to be more sensitive in simulations of impulse detecting and signal segmentation. Fourth, vibration samples in different tension states are used to train a particle swarm optimization−support vector machine (PSO-SVM) model. Lastly, the trained model is implemented to detect tension faults in practice. Two experimental results validated the effectiveness of the proposed method to detect tension faults, such as overload, underload, and imbalance, in both single-rope and multi-rope hoists. This study provides a new perspective for detecting tension faults in hoisting systems.

As an important part of rotating machinery, bearings play an important role in large-scale mechanical equipment. Abnormal bearing conditions may cause the machine to malfunction, or even evolve into a serious accident. Therefore, the accurate and timely fault diagnosis of the bearing is of great significance. Based on EMD, this paper introduces the working principles and characteristics of EEMD and CEEMDAN, respectively. Then the signal was decomposed by EEMD and CEEMDAN respectively. The simulation results show that CEEMDAN has better effect on signal decomposition. Then, comparing the effect of CEEMDAN and EEMD on bearing fault feature frequency extraction, the experiment proves that CEEMDAN has a better ability to preserve original signal and eliminate noise than EEMD method, and can extract bearing fault feature more accurately and timely. [ABSTRACT FROM AUTHOR]

Acceleration sensors are frequently applied to collect vibration signals for bearing fault diagnosis. To fully use these vibration signals of multi-sensors, this paper proposes a new approach to fuse multi-sensor information for bearing fault diagnosis by using ensemble empirical mode decomposition (EEMD), correlation coefficient analysis, and support vector machine (SVM). First, EEMD is applied to decompose the vibration signal into a set of intrinsic mode functions (IMFs), and a correlation coefficient ratio factor (CCRF) is defined to select sensitive IMFs to reconstruct new vibration signals for further feature fusion analysis. Second, an original feature space is constructed from the reconstructed signal. Afterwards, weights are assigned by correlation coefficients among the vibration signals of the considered multi-sensors, and the so-called fused features are extracted by the obtained weights and original feature space. Finally, a trained SVM is employed as the classifier for bearing fault diagnosis. The diagnosis results of the original vibration signals, the first IMF, the proposed reconstruction signal, and the proposed method are 73.33%, 74.17%, 95.83% and 100%, respectively. Therefore, the experiments show that the proposed method has the highest diagnostic accuracy, and it can be regarded as a new way to improve diagnosis results for bearings. [ABSTRACT FROM AUTHOR]

• The WT-EEMD method is designed to denoise the fault signal. • A nonlinear threshold function is constructed in the wavelet threshold algorithm. • Applying the wavelet threshold denoising to the high-frequency IMFs of EEMD. • Classification of IMF components using the MSAM based correlation coefficient method. • The frequency characteristics of the sand-laden water flow signal were analyzed. Sediment wear is a significant cause of hydro-turbine failure. The wavelet threshold and ensemble empirical mode decomposition (WT-EEMD) method is proposed to denoising the acoustic vibration signals of hydro-turbine runners under normal and sand-laden water flow conditions. Ensemble empirical mode decomposition (EEMD) is performed on the acquired signals, and the decomposed high-frequency intrinsic mode function (IMF) is denoised using wavelet threshold. A nonlinear threshold function is constructed instead of the traditional threshold function in the wavelet threshold algorithm. The experimental results show that the WT-EEMD method is superior to the EMD, EEMD, and wavelet threshold. Moreover, it was found that when the sand-laden water flows through the hydro-turbine, it causes a change in the frequency spectrum. This study can provide a reference for the study of sand avoidance operation of hydro-turbines and provide a valuable supplement to the existing condition monitoring and fault diagnosis system of hydroelectric generators. [ABSTRACT FROM AUTHOR]

Condition monitoring and fault diagnosis via vibration signal processing play an important role to avoid serious accidents. Aiming at the complexity of multiple faults in a rotor-bearing system and drawback, the characteristic frequency of relevant fault could not be determined effectively with traditional method. The Spectral Kurtosis (SK) is useful for the bearing fault detection. Nevertheless, the simulation of experiment in this paper shows that the SK is unable to identify multi-fault of rotor-bearing system fully when different faults excite different resonance frequencies. A new multi-fault detection method based on EEMD and spectral kurtosis (SK) is proposed in order to overcoming the shortcoming. The proposed method is applied to multi-faults of rotor imbalance and faulty bearings. The superiority of the proposed method based on spectral kurtosis (SK) and EEMD is demonstrated in extracting fault characteristic information of rotating machinery. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper employs a combined ensemble empirical mode decomposition (EEMD) and singular value decomposition (SVD) technique to extract useful fault features from the condition monitoring data of rolling element bearings. The fault features is then classified by a Fuzzy clustering method, Gath-Geva (GG) clustering, to obtain the cluster center and membership matrix of each bearing condition for pattern recognition. The bearing fault recognition is realized by calculating the hamming approach degree between the test samples and the known fault clustering centers from the GG clustering. The proposed algorithm is then evaluated first on several sets of simulated bearing defect data with different signal to noise ratios (SNRs) to represent bearing defects with various degrees of severities. Satisfying diagnosis outcome can be obtained from this set of simulation when the SNR is greater than 1. The algorithm is further evaluated using a set of experimental data from a bearing fault test rig. It is found that the proposed algorithm can diagnose all bearing operation conditions accurately based on the experimental data. [ABSTRACT FROM AUTHOR]

In order to raise the working reliability of rotating machinery in real applications and reduce the loss caused by unintended breakdowns, a new method based on improved ensemble empirical mode decomposition (EEMD) and envelope spectrum analysis is proposed for fault diagnosis in this paper. First, the collected vibration signals are decomposed into a series of intrinsic mode functions (IMFs) by the improved EEMD (IEEMD). Then, the envelope spectrums of the selected decompositions of IEEMD are analyzed to calculate the energy values within the frequency bands around speed and bearing fault characteristic frequencies (CDFs) as features for fault diagnosis based on support vector machine (SVM). Experiments are carried out to test the effectiveness of the proposed method. Experimental results show that the proposed method can effectively extract fault characteristics and accurately realize classification of bearing under normal, inner race fault, ball fault and outer race fault. [ABSTRACT FROM AUTHOR]

The rapid expansion of wind farms has accelerated research into improving the reliability of wind turbines to reduce operational and maintenance costs. A critical component in wind turbine drive-trains is the gearbox, which is prone to different types of failures due to long-term operation under tough environments, variable speeds and alternating loads. To detect gearbox fault early, a method is proposed for an effective fault diagnosis by using improved ensemble empirical mode decomposition (EEMD) and Hilbert square demodulation (HSD). The method was verified numerically by implementing the scheme on the vibration signals measured from bearing and gear test rigs. In the implementation process, the following steps were identified as being important: (1) in order to increase the accuracy of EEMD, a criterion of selecting the proper resampling frequency for raw vibration signals was developed; (2) to select the fault related intrinsic mode function (IMF) that had the biggest kurtosis index value, the resampled signal was decomposed into a series of IMFs; (3) the selected IMF was demodulated by means of HSD, and fault feature information could finally be obtained. The experimental results demonstrate the merit of the proposed method in gearbox fault diagnosis. [ABSTRACT FROM AUTHOR]

Acceleration sensors are frequently applied to collect vibration signals for bearing fault diagnosis. To fully use these vibration signals of multi-sensors, this paper proposes a new approach to fuse multi-sensor information for bearing fault diagnosis by using ensemble empirical mode decomposition (EEMD), correlation coefficient analysis, and support vector machine (SVM). First, EEMD is applied to decompose the vibration signal into a set of intrinsic mode functions (IMFs), and a correlation coefficient ratio factor (CCRF) is defined to select sensitive IMFs to reconstruct new vibration signals for further feature fusion analysis. Second, an original feature space is constructed from the reconstructed signal. Afterwards, weights are assigned by correlation coefficients among the vibration signals of the considered multi-sensors, and the so-called fused features are extracted by the obtained weights and original feature space. Finally, a trained SVM is employed as the classifier for bearing fault diagnosis. The diagnosis results of the original vibration signals, the first IMF, the proposed reconstruction signal, and the proposed method are 73.33%, 74.17%, 95.83% and 100%, respectively. Therefore, the experiments show that the proposed method has the highest diagnostic accuracy, and it can be regarded as a new way to improve diagnosis results for bearings.

A method based on ensemble empirical mode decomposition (EEMD), base-scale entropy (BSE) and clustering by fast search (CFS) algorithm for roller bearings faults diagnosis is presented in this study. Firstly, the different vibration signals were decomposed into a number of intrinsic mode functions (IMFs) by using EEMD method, then the correlation coefficient method was used to verify the correlation degree between each IMF and the corresponding original signals. Secondly, the first two IMF components were selected according to the value of correlation coefficient, each IMF entropy values was calculated by BSE, permutation entropy (PE), fuzzy entropy (FE) and sample entropy (SE) methods. Thirdly, comparing the elapsed time of BSE/PE/FE/SE models, using the first two IMF-BSE/PE/FE/SE entropy values as the input of CFS clustering algorithm. The CFS clustering algorithm did not require pre-set the number of clustering centers, the cluster centers were characterized by a higher density than their neighbors and by a relatively large distance from points with higher densities. Finally, the experiment results show that the computational efficiency of BSE model is faster than that of PE/FE/SE models under the same fault recognition accuracy rate, then the effect of fault recognition for roller bearings is good by using CFS method. [ABSTRACT FROM AUTHOR]

Because planetary gear is characterized by its small size, light weight and large transmission ratio, it is widely used in large-scale, low-speed and heavy-duty mechanical systems. Therefore, the fault diagnosis of planetary gear is a key to ensure the safe and reliable operation of such mechanical equipment. A fault diagnosis method of planetary gear based on the entropy feature fusion of ensemble empirical mode decomposition (EEMD) is proposed. The intrinsic mode functions (IMFs) with small modal aliasing are obtained by EEMD, and the original feature set is composed of various entropy features of each IMF. To address the insensitive features in the original feature set and the excessive feature dimension, kernel principal component analysis (KPCA) is used to process the original feature set. Kernel principal component extraction and feature dimension reduction are performed. The fault diagnosis of planetary gear is eventually realized by applying the extracted kernel principal components and learning vector quantization (LVQ) neural network. The experiments under different operation conditions are carried out, and the experimental results indicate that the proposed method is capable of extracting the sensitive features and recognizing the fault statuses. The overall recognition rate reaches to 96% when the motor output frequency is 45 Hz and the load is 13.5 N m, and the fault recognition rates of the normal gear, the gear with one missing tooth and the broken gear can reach to 100%. The recognition rates of different fault gears under other operation conditions also can achieve better results. Thus, the proposed method is effective for the diagnosis of planetary gear faults. [ABSTRACT FROM AUTHOR]

Studying vibrational signals is one reliable method for monitoring the situation of rotary machinery. There are various methods for converting vibrational signals into usable information for fault diagnosis, one of which is the empirical mode decomposition method (EMD). This article is about diagnosing bearing faults using the EMD method, employing nondestructive test. Vibration signals are acquired by a bearing test machine. The discrete wavelet bases are used to translate vibration signals of a roller bearing into time-scale representation. Then, an envelope signal can be obtained by envelope spectrum analysis of wavelet coefficients of high scales. Local Hilbert marginal spectrum can be obtained by applying thr EMD method to the envelope signal from which the faults in a roller bearing can be diagnosed and fault patterns can be identified. The results have shown bearing faults frequencies are easily observable. There is a variant of the EMD method called the ensemble EMD (EEMD), which overcomes the mode mixing problem which may occur when the signal to be decomposed is intermittent. The EEMD method is also applied to the acquired signals, and the two methods were compared. While the outcomes of both methods do not differ much, one important merit of the EMD is that it has much less computational processing time than EEMD. [ABSTRACT FROM AUTHOR]

In analyzing signals from a wind turbine gearbox this paper suggests a new signal processing procedure named as CMF-EEMD method which is formed by applying conventional EEMD to a new type of combined mode function (CMF). This CMF consists of a low frequency CMF, denoted as C L , and a high frequency CMF, denoted as C h . Then it optimizes the amplitude of the added noise in decomposing C h and C L using EEMD. Finally, it calculates cyclic autocorrelation function (CAF) for every characteristic IMF from EEMD. The proposed procedure is applied to analyze the multi-faults of a wind turbine gearbox and the results confirm better performances in resolving different signal components by the proposed method than that from the cyclic autocorrelation function (CAF) of a direct EEMD analysis. [ABSTRACT FROM AUTHOR]

In the incipient failure of mechanical equipment, the impulse reflecting the fault feature is too weak to extract from strong noise, which increases the difficulty of fault diagnosis. The ensemble empirical mode decomposition method decomposes a noisy signal into several intrinsic mode functions, including noise and useful signal components. There are difference in autocorrelation function properties between noise and useful signal components. According to this, it presented an adaptive de-noising method, which used the energy concentration ratio to find noise component cut-off point and applied an improved soft threshold method to pickup useful high-frequency signal from noise component. The de-noisy results of simulation signals with different frequency show its efficiency for the intermediate and low frequency signal de-noisy. The noise reduction effect of fault bearing vibration signal proves its feasibility. [ABSTRACT FROM AUTHOR]

Abstract: When early weak fault emerges in rolling bearing the fault feature is too weak to extract using the traditional fault diagnosis methods such as Fast Fourier Transform (FFT) and envelope demodulation. The tunable Q-factor wavelet transform (TQWT) is the improvement of traditional one single Q-factor wavelet transform, and it is very fit for separating the low Q-factor transient impact component from the high Q-factor sustained oscillation components when fault emerges in rolling bearing. However, it is hard to extract the rolling bearing’ early weak fault feature perfectly using the TQWT directly. Ensemble empirical mode decomposition (EEMD) is the improvement of empirical mode decomposition (EMD) which not only has the virtue of self-adaptability of EMD but also overcomes the mode mixing problem of EMD. The original signal of rolling bearing’ early weak fault is decomposed by EEMD and several intrinsic mode functions (IMFs) are obtained. Then the IMF with biggest kurtosis index value is selected and handled by the TQWT subsequently. At last, the envelope demodulation method is applied on the low Q-factor transient impact component and satisfactory extraction result is obtained. [Copyright &y& Elsevier]