Effective Intrinsic Mode Functions Research Articles

Permutation fuzzy entropy based ICEEMDAN de-noising for inertial sensors

As one of the critical factors affecting the accuracy of inertial navigation system (INS) positioning, random noise will lead to the accumulation of INS positioning errors, even the failure of the system. Previous studies mainly focus on achieving noise reduction by adopting either the wavelet decomposition method or some relevant methods of the empirical mode decomposition (EMD). However, the former is limited by Heisenberg’s uncertainty principle when processing non-stationary signals, and the latter exhibits numerous mode-mixing phenomena during the decomposition process. In this paper, a permutation fuzzy entropy (PFE) based improved complete ensemble EMD with adaptive noise (ICEEMDAN) de-noising method is proposed to utilize the ICEEMDAN method for signal decomposition, and the PFE as an index to distinguish the effective intrinsic mode functions (IMFs) and the noisy IMFs during the decomposition process. In addition, simulated signals with different noise levels and field data with different durations are utilized to design a group of experiments to test the de-noising performance of the PFE-based ICEEMDAN de-noising method. The experimental results demonstrate that the advantages of our proposed method consists of three main parts: (1) The ICEEMDAN method more effectively alleviates the mode-mixing problem compared to the complete ensemble EMD with adaptive noise (CEEMDAN), thereby enhancing the accuracy of inertial measurement unit (IMU) signal reconstruction; (2) the PFE is more preferable than the permutation entropy for classify IMFs decomposed by the ICEEMDAN method; (3) our proposed method can effectively reduce IMU signal noise and improve the positioning accuracy of inertial sensor.

Research on motor rotation anomaly detection based on improved VMD algorithm

PurposeThe electromechanical brake system is leading the latest development trend in railway braking technology. The tolerance stack-up generated during the assembly and production process catalyzes the slight geometric dimensioning and tolerancing between the motor stator and rotor inside the electromechanical cylinder. The tolerance leads to imprecise brake control, so it is necessary to diagnose the fault of the motor in the fully assembled electromechanical brake system. This paper aims to present improved variational mode decomposition (VMD) algorithm, which endeavors to elucidate and push the boundaries of mechanical synchronicity problems within the realm of the electromechanical brake system.Design/methodology/approachThe VMD algorithm plays a pivotal role in the preliminary phase, employing mode decomposition techniques to decompose the motor speed signals. Afterward, the error energy algorithm precision is utilized to extract abnormal features, leveraging the practical intrinsic mode functions, eliminating extraneous noise and enhancing the signal’s fidelity. This refined signal then becomes the basis for fault analysis. In the analytical step, the cepstrum is employed to calculate the formant and envelope of the reconstructed signal. By scrutinizing the formant and envelope, the fault point within the electromechanical brake system is precisely identified, contributing to a sophisticated and accurate fault diagnosis.FindingsThis paper innovatively uses the VMD algorithm for the modal decomposition of electromechanical brake (EMB) motor speed signals and combines it with the error energy algorithm to achieve abnormal feature extraction. The signal is reconstructed according to the effective intrinsic mode functions (IMFS) component of removing noise, and the formant and envelope are calculated by cepstrum to locate the fault point. Experiments show that the empirical mode decomposition (EMD) algorithm can effectively decompose the original speed signal. After feature extraction, signal enhancement and fault identification, the motor mechanical fault point can be accurately located. This fault diagnosis method is an effective fault diagnosis algorithm suitable for EMB systems.Originality/valueBy using this improved VMD algorithm, the electromechanical brake system can precisely identify the rotational anomaly of the motor. This method can offer an online diagnosis analysis function during operation and contribute to an automated factory inspection strategy while parts are assembled. Compared with the conventional motor diagnosis method, this improved VMD algorithm can eliminate the need for additional acceleration sensors and save hardware costs. Moreover, the accumulation of online detection functions helps improve the reliability of train electromechanical braking systems.

Mechanical Incipient Fault Detection and Performance Analysis Using Adaptive Teager-VMD Method

For large rotating machinery with low speed and heavy load, the incipient fault characteristics of rolling bearings are particularly weak, making it difficult to identify them effectively by direct signal processing methods. To resolve this issue, we propose a novel approach to detecting incipient fault features that combines signal energy enhancement and signal decomposition. First, the structure of a conventional Teager algorithm is modified to further increase the energy of the micro-impact component and hence the impact amplitude. Then, a kind of composite chaotic mapping is constructed to extend the original fruit fly optimization algorithm (FOA) framework, improving the FOA’s randomness and search power. The effective intrinsic mode functions (IMFs) are determined by searching for the optimal combination values of the key parameters of the variational mode decomposition (VMD) with the improved chaotic FOA (ICFOA). The kurtosis index is then used to select the IMFs that are most relevant to the fault characteristics information. Finally, the sensitive components are analyzed to identify multiple early fault characteristics and determine detailed information about the faults. Moreover, the approach is evaluated by a simulation signal and a measured signal. The comprehensive evaluation indicates that the approach has clear advantages over other excellent methods in extracting the incipient fault feature information of the equipment and has great potential for application in engineering.

Research on a Novel Denoising Method for Negative Pressure Wave Signal Based on VMD

The key to accurately locate pipeline leakage is to effectively reduce the noise in the leakage signal. However, the leakage signal has the characteristics of nonlinear and dynamic change, and the denoising effect of the traditional method is limited. In order to reduce the positioning error caused by noise, a novel denoising method based on variational mode decomposition (VMD) is proposed. First, the correlation coefficient is used to screen effective intrinsic mode function components. Then, an optimization method of VMD decomposition layers <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> by using minimum information entropy is utilized. Thus, the optimal number of layers and noise reduction signal can be obtained. Finally, the leakage point can be obtained by the principle of negative pressure wave (NPW). Simulation results show that the SNR can effectively improved by using the method in this paper. In laboratory experiments, this method can be used to effectively denoise the pressure signal while preserving the original signal characteristics as much as possible. Furthermore, From the perspective of positioning accuracy, compared other methods, the proposed method method can achieve better positioning effect, and the average relative positioning error is 2.03%.

An Early Chatter Detection Method Based on Multivariate Variational Mode Decomposition and Chatter Correlation Factor

Machining chatter is a self-excited vibration between the tool and the workpiece that affects surface quality and productivity. In this article, we propose a novel approach for early detection of machining chatter based on multivariate variational mode decomposition (MVMD) and chatter correlation factor (CCF). The proposed approach is featured by early chatter detection, and it is not susceptible to machining parameters. In order to obtain effective intrinsic mode functions (IMFs) and avoid mode mixing problem in MVMD, an automatic selection method of MVMD's parameters based on the difference of correlation coefficients (DCC) is developed. By using MVMD, multichannel signals are decomposed into multiple IMF groups with mode-alignment to extract common frequency components across all channel signals. Because multichannel signals are highly correlated at the chatter frequency when chatter occurs, the proposed CCF derived by correlation coefficients between the selected chatter-sensitive IMFs is sensitive to early chatter and can simultaneously measure the level of chatter. A support vector machine chatter identification model is obtained based on the CCF. The effectiveness of the proposed method is demonstrated by simulation and experimental findings.

A FCEEMD Energy Kurtosis Mean Filtering-Based Fault Feature Extraction Method

Aiming at the problem that fault feature extraction is susceptible to background noises and burrs, we proposed a new feature extraction method based on a new decomposition method and an effective intrinsic mode function (IMF) selection method. Firstly, pairs of white noises with opposite signs were introduced to neutralize the residual noises in ensemble empirical mode decomposition (EEMD) and suppress mode mixing. Both the reconstruction error (1.8445 × 10−17) and decomposition time (0.01 s) were greatly reduced through fast, complementary ensemble empirical mode decomposition (FCEEMD). Secondly, we integrated the energy and kurtosis of the IMF and proposed an effective IMF selection method based on energy kurtosis mean filtering, and the background noise of the signal was greatly suppressed. Finally, the periodic impacts were extracted from the IMF reconstruction signal by multipoint optimal minimum entropy deconvolution adjusted (MOMEDA). The fault frequencies were extracted from the periodic impacts through Hilbert demodulation, and the relative errors between the measured values and the theoretical values were all less than 0.05. The experimental results show that the proposed method can extract fault features more efficiently and provide a novel method for the fault diagnosis of rotating machinery.

Incipient fault diagnosis for the cam-driven absolute gravimeter.

The vibration disturbance caused by incipient faults is an important factor affecting the measurement accuracy of the cam-driven absolute gravimeter. Based on the characteristics of the cam-driven absolute gravimeter, such as the small amplitude of the incipient faults, the inadequate representation of features for the faults, and hard-to-find in the noise, a novel method for incipient fault diagnosis of the cam-driven absolute gravimeter is put forward in this paper, which integrates the parameter-optimized Variational Mode Decomposition (VMD) with Light Gradient Boosting Machine (LightGBM). The sparrow search algorithm is used to optimize the VMD parameters. The parameter-optimized VMD algorithm is used to adaptively decompose the vibration signals of the gravimeter under different cases, and then an effective intrinsic mode function (IMF) is selected based on the Pearson correlation coefficient. Some high-frequency IMFs are subjected to adaptive noise reduction combined with low-frequency IMF reconstruction, and then the multi-scale permutation entropy with sensitive characteristics under different time scales is extracted as the fault feature vectors. The extracted multi-dimensional vector matrix is entered into the LightGBM classifier to realize the accurate diagnosis of the incipient faults for the cam-driven absolute gravimeter. The test results show that this method can effectively detect various incipient failures of the cam-driven absolute gravimeter, with an identification accuracy of 98.41%. With this method, the problem of low measurement accuracy for the cam-driven absolute gravimeter caused by the incipient faults is solved, and the rapid tracing and accurate positioning of these faults for the gravimeter are realized, promising a good prospect for engineering application.

Rolling Bearing Performance Degradation Prediction Based on FBG Signal

Aiming at the problems of low accuracy and low robustness in current prediction methods of rolling bearing performance degradation, this paper propose a performance degradation prediction method based on empirical mode decomposition (EMD) and support vector data description (SVDD). Unlike the features extracted by wavelet analysis, the EMD algorithm has better adaptive capability and can better avoid the interference of high frequency noise. fiber Bragg grating (FBG) are used to extract the bearing vibration signal. The use of FBG sensors can better avoid the interference of electromagnetic noise, so that the vibration signal can be accurately extracted in the industrial strong magnetic environment. The Empirical Mode Decomposition is used to decompose data and extract effective intrinsic mode function (IMF) components. Six features of Intrinsic Mode Function components are extracted to form feature vectors and used as training data. Because the IMF component obtained by EMD decomposition has removed most of the noise signals, the signal features are more significant and thus effectively improve the accuracy of subsequent recognition. This paper use Support Vector Data Description for performance degradation prediction and evaluates the degradation performance of the rolling bearing by calculating the degradation value. Experiments show the proposed method can detect the performance degradation process of the rolling bearing and effectively predict the remaining service life. Due to the high recognition accuracy, the proposed method is able to identify the current state of ball bearings and predict their remaining service life.

Research on leak location method of water supply pipeline based on negative pressure wave technology and VMD algorithm

Noise makes the inflection point of the negative pressure wave (NPW) difficult to be extracted. Therefore, an adaptive denoising method based on VMD (ADM-VMD) is proposed. ADM-VMD uses correlation coefficient to select effective intrinsic mode function (IMF) components, and obtains the optimal denoising signal according to the minimum information entropy. The traditional inflection point extraction method has the defect of poor robustness. Thus, an adaptive inflection point extraction method based on VMD (IPEM-VMD) is proposed. IPEM-VMD selects the optimal detail signal by kurtosis and extracts the inflection point according to the maximum peak. The experimental conditions with two pipe diameters were set up (0.08 m and 0.125 m). The results show that ADM-VMD can effectively smooth the noise and obtain a clear pressure drop curve. IPEM-VMD obtains a unique and clear maximum peak, which has good robustness to small noise. The average positioning error of the proposed method is 6.42%.

Application of VMD in Pipeline Leak Detection Based on Negative Pressure Wave

Leakage problems are common in the water supply pipeline system, which will threaten the health of residents and cause economic losses. Negative pressure wave (NPW) technology calculates the time difference through the inflection point to locate the leak. However, due to the nonlinear and nonstationary characteristics of the pressure signal, it is difficult to obtain an accurate inflection point of the NPW by the traditional method. Therefore, the advantages of applying variational mode decomposition (VMD) in NPW technology are explored. Firstly, the correlation coefficient and permutation entropy (PE) are used for effective intrinsic mode function (IMF) component selection and parameter optimization. Thus, an adaptive denoising method based on VMD (AD-VMD) is presented. Then, to effectively separate the detail features of the NPW, a novel inflection point extraction method based on VMD (IPE-VMD) is proposed. Simulation and experimental results show that AD-VMD can effectively suppress noise interference and conserve the mutation characteristic of the leakage. IPE-VMD can obtain a distinct maximum peak at the inflection point and has good robustness to noise interference. This method can calculate the time difference precisely and stably. In addition, the accuracy of the leak location is verified. The average relative positioning error is 5.13%.

EMD and Singular Value Difference Spectrum Based Bearing Fault Characteristics Extraction

Aiming at extracting rolling bearing defect characteristic effectively under strong background noise, a defect diagnosis technique based on empirical mode decomposition (EMD) and singular value difference spectrum is put forward. Firstly, the non-stationary original bearing vibration experimental data is decomposed into several intrinsic mode functions (IMFs) using EMD technique to eliminate the noise influence. Secondly, the effective intrinsic mode functions are selected by using correlation functions to reconstruct the bearing transient vibration signal. Thirdly, the singular value decomposition of the reconstructed signal is carried out to calculate the singular value difference spectrum (SVDS), and the principal components are selected according to the SVDS. Finally, the envelope spectrum of the reconstructed principal components data is calculated, and rolling bearing defect characteristic is picked up according to the envelope spectrum technique. The results of the experiment exhibit that the proposed technique can be effectively applied to rolling bearing defect characteristics extraction.

Damage detection of a cable-stayed bridge based on combining effective intrinsic mode functions of empirical mode decomposition using the feature selection technique

This paper presents a novel approach for detecting damage in a cable-stayed bridge based on adaptive signal processing and feature extraction. The Empirical Mode Decomposition (EMD) technique is employed in this research to obtain the Intrinsic Mode Functions (IMFs). Several features in time and frequency domains are extracted from IMFs as damage-sensitive features. Next, to reduce false alarms, several spectral parameters are introduced as effective features. A Support Vector Machine (SVM) is employed as a classifier to compare the detection ability of the proposed and conventional features. The second part of this research employs the application of several feature selection techniques that are based on the filter, wrapper, and embedded approaches to obtain the most effective IMFs and then combine them to create a subset that carries the most significant information about the structural damage. To investigate the capability of the proposed approach, vibration acceleration data obtained from a real structure, the Yonghe bridge, is employed. Results indicate that the proposed approach can identify damage with high accuracy.

Application Research of a New Adaptive Noise Reduction Method in Fault Diagnosis

The feature extraction of composite fault of gearbox in mining machinery has always been a difficulty in the field of fault diagnosis. Especially in strong background noise, the frequency of each fault feature is different, so an adaptive time-frequency analysis method is urgently needed to extract different types of faults. Considering that the signal after complementary ensemble empirical mode decomposition (CEEMD) contains a lot of pseudo components, which further leads to misdiagnosis. The article proposes a new method for actively removing noise components. Firstly, the best scale factor of multi-scale sample entropy (MSE) is determined by signals with different signal to noise ratios (SNRs); secondly, the minimum value of a large number of random noise MSE is extracted and used as the threshold of CEEMD; then, the effective Intrinsic mode functions(IMFs) component is reconstructed, and the reconstructed signal is CEEMD decomposed again; finally, after multiple iterations, the MSE values of the component signal that are less than the threshold are obtained, and the iteration is terminated. The proposed method is applied to the composite fault simulation signal and mining machinery vibration signal, and the composite fault feature is accurately extracted.

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

Fan is widely used in industry as a very important component of pressure transmission and system cooling. Fan blade is the key part which decides the service life of fan and the whole system. In this paper, the research focuses on the centrifugal fan of high-speed train cooling system and the blades on it. The experimental system and research method of fatigue analysis and fault diagnosis are built. The fan can work in both steady and unsteady state conditions controlled and driven by the test system. At the same time, the signals of strain and acceleration at different positions can be obtained accurately. Based on the static tension and dynamic fatigue test of the material, the accurate simulation of the fan is carried out. By simulation analysis, steady-state test, long-time start-stop test and metallographic test, the prediction of fatigue life of fan blades is conducted with in-depth analysis and comprehensive evaluation. The results show that fan meets the requirements of actual working life. As the actual working state is more diverse and severe than the above test process, some phenomena of damage also appear in the blades after service. Based on real situation, the blade faults are classified. After determination of measuring point position, the vibration signals of four fault states of blade under rated speed are collected. This paper proposes a new fault feature extraction method – the Refined Generalized Multi-Scale Entropy (RMSEσ2) combined with Support Vector Machine (SVM). According to the results of the empirical mode decomposition (EMD), the effective intrinsic mode function (IMF) components are selected by energy distribution and correlation coefficient. The comparative analysis of multi-scale entropy (MSE), generalized multi-scale entropy (MSEσ2) and RMSEσ2 between the original signal and 1–8 IMF components are carried out. Results show the RMSEσ2 of IMF1 component of Z direction of point 1 is the best choice. And it is imported into SVM for pattern recognition with scale factor 20. The optimal SVM model is obtained by choosing kernel function and parameters that are optimized by Particle Swarm Optimization (PSO) method. The results show the faults of centrifugal fan blades can be classified accurately. A set of research flow and method is set up, which provides important reference for fatigue and fault pattern recognition of fan blades in the future.

Online Intelligent Identification of Modal Parameters for Large Cable-Stayed Bridges

Realizing online intelligent identification of bridge modal parameters requires not only the adaptive decomposition of structural response signals but also the enforcement of the automatic identification of modal parameters. Therefore, in this paper, the signal decomposition algorithm-ensemble empirical mode decomposition algorithm (EEMD) is improved to fulfill the above task. First, the adaptive matching algorithm is introduced to deal with the endpoint effect; second, the method of classification is used to avoid modal aliasing. Finally, an index for filtering the effective intrinsic mode function (IMF) components is constructed to realize automatic screening and signal reconstruction of the effective IMF components. At the same time, the first derivative of the singular entropy increment is used to automatically determine the order of the system, and then the spectral clustering algorithm is combined with the stochastic subspace algorithm to ultimately reach the goal of automatic identification of modal parameters.

Multi-Domain Extreme Learning Machine for Bearing Failure Detection Based on Variational Modal Decomposition and Approximate Cyclic Correntropy

Rolling bearings are critical in industrial mining machinery. Due to strong Gaussian noise, frequent random shocks, and disordered loads in industrial settings, it is usually difficult to detect weak fault symptoms in vibration signals from a bearing. To detect incipient bearing faults, this paper proposes a new multi-domain kernel extreme learning machine (MKELM) based on variational modal decomposition (VMD) and a cyclic correntropy function. A normalized approximation algorithm for a cyclic correntropy function (NACCF) was first built to suppress the impulsive background noise. This approach is suitable for machine learning. To eliminate the Gaussian noise effectively, genetic mutation particle swarm optimization (GMPSO) with cyclic information entropy (CIE) was used to optimize the VMD parameters. The CIE was created as a fitness function in GMPSO to search for the best hyperparameters. It can be used to select effective intrinsic mode functions (IMFs) to reconstruct denoised signals. Then, statistical functions based on NACCF were used to extract the cyclic frequency-domain characteristics of the denoised signal, and the singular values of the IMFs were obtained as time-domain features of the signal. Finally, the multi-dimensional features from the two domains were input into MKELM to classify the health of the bearing. Experimental studies were carried out to investigate the proposed method in bearing fault detection and identification. The results demonstrated the effectiveness of the proposed method in motor-bearing failure detection and its robustness to noise when analyzing bearing vibration signals under different working loads.

A New Method of Two-stage Planetary Gearbox Fault Detection Based on Multi-Sensor Information Fusion

Due to their high transmission ratio, high load carrying capacity and small size, planetary gears are widely used in the transmission systems of wind turbines. The planetary gearbox is the core of the transmission system of a wind turbine, but because of its special structure and complex internal and external excitation, the vibration signal spectrum shows strong nonlinearity, asymmetry and time variation, which brings great trouble to planetary gear fault diagnosis. The traditional time-frequency analysis technology is insufficient in the condition monitoring and fault diagnosis of wind turbines. For this reason, we propose a new method of planetary gearbox fault diagnosis based on Compressive sensing, Two-dimensional variational mode decomposition (2D-VMD) and full-vector spectrum technology. Firstly, the nonlinear reconstruction and noise reduction of the signal is carried out by using compressed sensing, and then the signal with multiple degrees of freedom is adaptively decomposed into multiple sets of characteristic scale components by using 2D-VMD. Then, Rényi entropy is used as the optimization index of 2D-VMD analysis performance to extract the effective target intrinsic mode function (IMF) component, reconstruct the dynamics signal in the planetary gearbox, and improve the signal-to-noise ratio. Then, using the full-vector spectrum technique, the homologous information collected by numerous sensors is data layer fused in the spatial domain and the time domain to increase the comprehensiveness and certainty of the fault information. Finally, the Teager–Kaiser energy operator is used to demodulate the potential low-frequency dynamics frequency characteristics from the high-frequency domain and detect the fault characteristic frequency. Furthermore, the correctness and validity of the method are verified by the fault test signal of the planetary gearbox.

Research on Turbine Rotor Fault Diagnosis Based on CPSO-BBO Optimization SVM

In order to improve the recognition accuracy and efficiency of the turbine rotor fault diagnosis, a fault diagnosis method based on CPSO-BBO (chaos particle swarm optimization biogeography based optimization) algorithm was proposed to optimize SVM (support vector machine). Firstly, four common faults states of turbine rotors were simulated by ZT-3 rotor test-bench to obtain fault data. Secondly, CEEMD (complementary ensemble empirical mode decomposition) is used to decompose the rotor vibration signal, and the more effective intrinsic mode function (IMF) is screened by combining the variance contribution rate, and the corresponding PE (permutation entropy) is calculated as the fault characteristic value. After that, chaos theory and PSO (particle swarm optimization) algorithm are combined into the theory of BBO (biogeography based optimization) to obtain CPSO-BBO algorithm, which is used to optimize SVM to obtain the optimal parameters of the diagnostic model. Finally, the fault identification is studied by using the acquired fault data.

Simultaneous de-noising and enhancement method for long-span bridge health monitoring data based on empirical mode decomposition and fractal conservation law

This paper introduces a new signal-filtering method, which is aimed at processing long-span bridge structural health monitoring (SHM) data based on empirical mode decomposition (EMD) and fractal conservation law (FCL). The long-span bridge SHM data is selected from vertical vibration data of the Runyang Bridge’s main girder. The key idea of this paper is to enhance the frequency amplitude of the target signal and at the same time to weaken the surroundings-caused signal noise by combining methods as EMD and FCL after confirming the long-span bridge’s natural frequency. EMD operation consists of decomposing the vibration data by EMD, selection of effective intrinsic mode functions (IMFs) and effective IMF recombination. FCL theory will be applied on the filtering function to process effective IMFs in the frequency domain before recombination. This theory is based on a partial differential equation modified by a nonlocal fractional anti-diffusive term of lower order. The effect of the EMDFCL filter is compared with the EMD method, FCL method and filter strategy that combines EMD and low-pass filtering, respectively. Results show that the EMDFCL filter outperforms the other signal filtering methods in natural frequency recognition of long-span bridge SHM data. In particular, the bridge’s natural frequencies distinguished by EMDFCL match well with the calculated frequencies and original fingerprint.

An Extended EEMD Method for Localized Faults Detection of a Planetary Gearbox

Abstract An accurate incipient localized faults diagnosis method is very helpful for preventing serious accidents of planetary gearboxes. Although useful, the ensemble empirical mode decomposition (EEMD) method still has some issues in selecting the effective intrinsic mode functions (IMFs). To overcome these issues, an improved encircled energy EEMD method (EE-EEMD) combined with the EEMD algorithm, mirror extending method, Teager energy operator demodulation method, and EE index selection method is presented to detect localized faults in the planet bearings, ring gear, planet gear, and sun gear of planetary gearboxes. The mirror extending method is applied to address the end extending issue of the EEMD method. The EE index is utilized to determine the effective IMFs from the EEMD method. The energy separation algorithm is used to calculate the instantaneous frequencies of the effective IMFs. The results from the EE index, kurtosis, and weight kurtosis selection methods are compared. The vibration signal models of the localized faults in the ring gear, planet gear, sun gear, planet bearing races, and planet bearing roller are used to illustrate the validity of the presented EE-EEMD method. An experimental investigation for a planetary gearbox considering a spall fault in the sun gear is conducted to validate the presented EE-EEMD method. It seems the presented EE-EEMD method can be utilized to detect localized faults in the components of planetary gearboxes.