Multiscale Dispersion Entropy Research Articles

Empirical Variational Mode Decomposition Based on Binary Tree Algorithm.

Aiming at non-stationary signals with complex components, the performance of a variational mode decomposition (VMD) algorithm is seriously affected by the key parameters such as the number of modes , the quadratic penalty parameter and the update step . In order to solve this problem, an adaptive empirical variational mode decomposition (EVMD) method based on a binary tree model is proposed in this paper, which can not only effectively solve the problem of VMD parameter selection, but also effectively reduce the computational complexity of searching the optimal VMD parameters using intelligent optimization algorithm. Firstly, the signal noise ratio (SNR) and refined composite multi-scale dispersion entropy (RCMDE) of the decomposed signal are calculated. The RCMDE is used as the setting basis of the , and the SNR is used as the parameter value of the . Then, the signal is decomposed into two components based on the binary tree mode. Before decomposing, the and need to be reset according to the SNR and MDE of the new signal. Finally, the cycle iteration termination condition composed of the least squares mutual information and reconstruction error of the components determines whether to continue the decomposition. The components with large least squares mutual information (LSMI) are combined, and the LSMI threshold is set as 0.8. The simulation and experimental results indicate that the proposed empirical VMD algorithm can decompose the non-stationary signals adaptively, with lower complexity, which is O(n2), good decomposition effect and strong robustness.

A new hybrid prediction model of air quality index based on secondary decomposition and improved kernel extreme learning machine

Air quality index (AQI) prediction is important to control air pollution. To improve its accuracy, a new hybrid prediction model of AQI based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), multivariate multiscale dispersion entropy (mvMDE), variational mode decomposition optimized by bald eagle search (BES) algorithm (BVMD) and kernel extreme learning machine optimized by rat swarm optimizer (RSO) algorithm (RSO-KELM), named CEEMDAN-mvMDE-BVMD-RSO-KELM, is proposed. Firstly, AQI series is decomposed by CEEMDAN to obtain multiple intrinsic mode function (IMF) components, and each IMF component's complexity is calculated by mvMDE. Secondly, VMD optimized by BES algorithm, named BVMD, is proposed to solve the problem of choosing the decomposition level K and penalty factor α of VMD, and BVMD is used to perform the secondary decomposition of high complexity components. Thirdly, the penalty coefficient and kernel parameter of KELM optimized by RSO algorithm, named RSO-KELM, is proposed, and all IMF components are predicted by RSO-KELM. Finally, the final prediction results are obtained by reconstructing the prediction results of all IMF components. The objective of this study is to propose a new hybrid prediction model of AQI based on secondary decomposition and improved KELM. Taking Shanghai, Beijing and Xi'an as examples, the results show that compared with the comparison models, the proposed model has the highest prediction accuracy.

Study of the Load Forecasting of a Wet Mill Based on the CEEMDAN-Refined Composite Multiscale Dispersion Entropy and LSTM Nerve Net

To address the problem of wet ball milling in a strong noise environment, it is difficult to accurately detect the internal load parameters of the cylinder during grinding. In this paper, a mill load parameter prediction method is proposed based on complementary ensemble empirical mode decomposition (CEEMDAN)-refined composite multiscale dispersion entropy (RCMDE) and-long and short-term memory (LSTM) neural networks. Using this method, the vibration signals of the mill barrel under strong noise were decomposed using the CEEMDAN algorithm, sensitive modal components with strong correlation with the original signal were selected for reconstruction using the correlation coefficient method, and features of the reconstructed signals under different load parameters were extracted through RCMDE. The load characteristic vector of an RCMDE mill was used as the input of LSTM neural networks, and the filling rate, material and ball ratio, and grinding concentration were used as the output to establish the internal load prediction model of wet mill. Experiment results show that the prediction method has a high accuracy, with average absolute percentage errors of the filling rate, feed-to-ball ratio, and grinding concentration of 6.08%, 3.50%, and 3.47%, and average absolute errors were of 0.0167, 0.0146, and 0.0146, respectively.

Sleep staging classification based on a new parallel fusion method of multiple sources signals

Objective. In the field of medical informatics, sleep staging is a challenging and time consuming task undertaken by sleep experts. The conventional method for sleep staging is to analyze Polysomnograms (PSGs) recorded in a sleep lab, but the sleep monitoring with polysomnography (PSG) severely degrades the sleep quality. Despite recent significant progress in the development of automatic sleep staging methods, building a good model still remains a big challenge for sleep studies due to the data-variability and data-inefficiency issues. Electrooculograms (EOGs) and electrocardiograms (ECGs) and are much easier to record and may offer an attractive alternative for home sleep monitoring. PSGs from the Sleep Heart Health Study database were used. This study aims to establish an new automatic sleep staging algorithm by using electrooculogram (EOG) and electrocardiogram (ECG). Approach. First, the heart rate variability (HRV) is extracted from EOG with the Weight Calculation Algorithm and an ‘NRRD’ RR interval detection algorithm. Second, three feature sets were extracted from HRV segments and EOG segments: time-domain features, frequency-domain features and nonlinear-domain features. The frequency domain features and nonlinear-domain features were extracted by using Discrete Wavelet Transform, Autoregressive (AR), and Power Spectral entropy, and Refined Composite Multiscale Dispersion Entropy. Third, a new ‘Parallel Fusion Method’ (PFM) for sleep stage classification is proposed. Three kinds of feature sets from EOG and HRV segments are fused by using PFM. Fourth, Extreme Gradient Boosting (XGBoost) is employed for sleep staging. Main results. Our experimental results show significant performance improvement on automatic sleep staging on the target domains achieved with the new sleep staging approach. The performance of the proposed method is tested by evaluating the average accuracy, Kappa coefficient. The average accuracy of sleep classification results by using XGBoost classification model with PFM is 83% and the kappa coefficient is 0.7. Experimental results show that the performance of the proposed method is competitive with the most current methods and results, and the recognition rate of S1 stage is significantly improved. Significance. As a consequence, it would enable one to improve the quality of automatic sleep staging models when the EOG and HRV signals are fused, which can be beneficial for monitor sleep quality and keep abreast of health conditions. Besides, our study provides good research ideas and methods for scholars, doctors and individuals.

Intelligent Diagnosis of Rolling Element Bearing Based on Refined Composite Multiscale Reverse Dispersion Entropy and Random Forest.

Rolling bearings are the vital components of large electromechanical equipment, thus it is of great significance to develop intelligent fault diagnoses for them to improve equipment operation reliability. In this paper, a fault diagnosis method based on refined composite multiscale reverse dispersion entropy (RCMRDE) and random forest is developed. Firstly, rolling bearing vibration signals are adaptively decomposed by variational mode decomposition (VMD), and then the RCMRDE values of 25 scales are calculated for original signal and each decomposed component as the initial feature set. Secondly, based on the joint mutual information maximization (JMIM) algorithm, the top 15 sensitive features are selected as a new feature set and feed into random forest model to identify bearing health status. Finally, to verify the effectiveness and superiority of the presented method, actual data acquisition and analysis are performed on the bearing fault diagnosis experimental platform. These results indicate that the presented method can precisely diagnose bearing fault types and damage degree, and the average identification accuracy rate is 97.33%. Compared with the refine composite multiscale dispersion entropy (RCMDE) and multiscale dispersion entropy (MDE), the fault diagnosis accuracy is improved by 2.67% and 8.67%, respectively. Furthermore, compared with the RCMRDE method without VMD decomposition, the fault diagnosis accuracy is improved by 3.67%. Research results prove that a better feature extraction technique is proposed, which can effectively overcome the deficiency of existing entropy and significantly enhance the ability of fault identification.

Bearing failure of reciprocating compressor sub-health recognition based on CAGOA-VMD and GRCMDE

The bearing vibration signal of reciprocating compressor has complex, non-stationary, nonlinear, and feature coupling characteristics. A method for sub-health recognition of sliding bearings based on curve adaptive grasshopper optimization algorithm optimize the parameters of variational mode decomposition (CAGOA-VMD) and generalized refine composite multiscale dispersion entropy (GRCMDE) is used. First, the CAGOA was used to search the best influence parameter combination of the VMD algorithm, and determine the bandwidth parameters and the number of decompositions that need to be set by the VMD algorithm, decompose the bearing fault signal to obtain a series of IMF. Then, the kurtosis and correlation coefficient criteria are used to select a group of components that contain the most information, and the fault signal is reconstructed on this component, and then the reconstructed signal is analyzed by GRCMDE to form a fault eigenvector. Finally, KPCA is used for dimensionality reduction to select input features and input into KELM for classification and recognition. The experimental results show that this method can effectively extract the bearing fault features of reciprocating compressors, and the eigenvectors have good separability, and realize the sub-health recognition of bearing fault features of reciprocating compressors.

Automated Recognition of Sleep Apnea-Hypopnea Syndrome Using Continuous Wavelet Transform-Based Multiscale Dispersion Entropy of Single-Lead ECG Signal

A new method is proposed for recognition of the sleep apnea-hypopnea syndrome (SAHS) using electrocardiograms (ECG) signal in order to find an alternative with the same performance of polysomnography (PSG). Heart rate variability (HRV) signals generated from an ECG signal are used to examine a wide range of indices. A novel aspect of this work is the use of a method to decompose the HRV spectrum into total power spectrum (TP), high frequency (HF), low frequency (LF) and very low frequency (VLF) sub-band signals, and correlates their energy content with sympathetic and parasympathetic activity. The HRV signal was decomposed using the continuous wavelet transform (CWT) followed by the inverse continuous wavelet transform (ICWT), and sub-band signals were extracted from 5-minute episodes. In this regard, the suggested technique provides novel indices based on the mean of small (1 to 5), medium (6 to 10) and large (11 to 20) time scales of multiscal dispersion entropy (MDE) for each sub-band signals. In order to choose the best classifier, the indices of the MDE are submitted to a t-test technique and categorized using three classifiers: decision trees (DT), support vector machines (SVM-RBF) and K-nearest neighbor (KNN). The proposed method is evaluated using the combination of Physionet Apnea–ECG database and the University College Dublin Sleep Apnea Database. Using a 10-fold cross validation technique, the SVM-RBF classification approach achieves an average sensitivity, specificity, and accuracy of 93.91%, 96.92% and 93.94%, respectively. The results demonstrate that the approach presented is as precise as the best contemporary methods investigated using the same ECG datasets.

Improved Multivariate Hierarchical Multiscale Dispersion Entropy: A New Method for Industrial Rotating Machinery Fault Diagnosis

This paper proposes an Improved Multivariate Multiscale Dispersion Entropy(IMMDE) combined with Hierarchical Entropy(HE) for vibration signal feature extraction. The traditional coarse-grained calculation is missing the relationship between neighboring sample points in the shift operation, which may lead to missing fault information. Secondly, as the scale increases, the original sequence is gradually shortened, leading to instability and inaccuracy in entropy estimation when dealing with short-term sequences. The improved coarse-grained calculation method overcomes its limitations to improve stability, and the HE method extracts deep fault frequency information from the high and low-frequency components of the multivariate signal. Then, the extracted features are dimensioned using the Max-Relevance Min-Redundancy (mRMR) to create a new set of fault features to improve diagnosis efficiency. Finally, the Support Vector Machine(SVM) determines the degree and type of fault. Experiments were conducted with three examples, the results show that IMHMDE can effectively extract the feature information according to mechanical faults’ features and improve the efficiency of fault diagnosis.

A Novel Bearing Faults Detection Method Using Generalized Gaussian Distribution Refined Composite Multiscale Dispersion Entropy

Rolling element bearings are a critical component of rotating machines and the presence of defects in the bearings may eventually lead to machine failure. Hence, early identification of such defects and severity assessment may avoid malfunctioning and breakdown of machines. Vibration signal features are often used to build fault diagnosis and fault classification systems. In this paper, a novel Refined Composite Multiscale Dispersion Entropy (RCMDE) based feature is proposed using a nonlinear mapping approach using the Generalized Gaussian Distribution (GGD)–Cumulative Distribution Function (CDF) with the different shape parameter β. This work combines the GGD Dispersion Entropy (DE) algorithm within the RCMDE framework with a feature selection algorithm, which is then used as input to a Multi-class Support Vector Machine (MCSVM) model for categorizing rolling element bearings fault conditions. The proposed method is validated using Case Western Reserve University (CWRU), Jiangnan University (JNU), and Southeast University (SEU) datasets. The experimental analysis shows that the GGD–RCMDE features are better in terms of classification accuracy, precision, recall and F1–score as compared to existing approaches.

Empirical mode decomposition-refined composite multiscale dispersion entropy analysis and its application to geophysical well log data

Oil and gas exploration activities often face great challenges due to the nonlinear behavior of the reservoir's physical properties, which is commonly defined as “heterogeneity”. Currently, well log data analysis techniques are a novel approach to unravel such nonlinear behavior, because well log data incorporates considerable geological information that determines reservoir property. However, the current complexity analysis techniques face two challenges: 1) the spatiotemporal multiscale nature of complex geological systems and 2) the superposition of the trends in the geophysical well log data on the analysis results. To fill the research gap, we propose an empirical mode decomposition-refined composite multiscale dispersion entropy analysis (EMD-RCMDEA) to eradicate trends and obtain the complexity results with spatiotemporal characteristics. The proposed method produces more accurate results, and its effectiveness, stability, and efficiency are also verified by the simulation signals and the gamma-ray (GR) well log signals. Compared to previous refined composite multiscale entropy analysis (RCMSEA), the EMD-RCMDEA enhances the stability by 69.3% and efficiency by 53.5%. Additionally, using the GR well log data for reservoirs, this method is also applied to explore the heterogeneity of strata with diverse depositional environments and different composite patterns and acquire the following results. 1) The EMD-RCMDEA values of the GR well log data are positively correlated with the heterogeneity of the strata. 2) The reservoir developed in a delta-front depositional environment has the strongest heterogeneity. 3) The heterogeneity of the composite patterns is much stronger than that of the single heterogeneity patterns. 4) Among the heterogeneities of the composite patterns, the pattern consisting of different facies is stronger than that for single facies. • A novel heterogeneity evaluation method is proposed based on EMD-RCMDEA. • New metrics for evaluating the stability of the multiscale entropy method are proposed. • The EMD-RCMDEA eliminates the drawback of multiscale entropy methods. • Trends superimposed in signals affect complexity analysis results. • The EMD-RCMDEA values of GR well log data positively correlate with reservoir heterogeneity.

Novel Feature Extraction Method of Pipeline Signals Based on Multi-Scale Dispersion Entropy Partial Mean of Multi-Modal Component

Novel Feature Extraction Method of Pipeline Signals Based on Multi-Scale Dispersion Entropy Partial Mean of Multi-Modal Component

Detection of Interturn Short-Circuit Faults in DFIGs Based on External Leakage Flux Sensing and the VMD-RCMDE Analytical Method

Fault detection based on external leakage flux can address various faults, such as short circuit faults in stators and rotors. External leakage flux sensing technology, as a noninvasive method, has been attracting increasing attention and research. However, for a high-power doubly fed induction generator (DFIG), the external leakage flux signals of the generator are easily overwhelmed by a strong noise background, and the fault diagnosis and recognition of stator and rotor interturn short circuits is complicated, which limits the practical engineering application of leakage flux sensing technology. Aiming at addressing interturn short circuit faults in DFIGs, this paper proposes a method for fault feature extraction and recognition of stator and rotor short circuits based on variational mode decomposition (VMD) and the refined composite multiscale dispersion entropy (RCMDE) analytical method for the external flux leakage of the generator. The optimized parameters of VMD are selected automatically by the genetic algorithm (GA), and VMD is adopted to adaptively decompose the external leakage flux signals into a series of intrinsic mode function (IMF) components. The evaluation criterion based on the correlation number in the frequency domain combined with the autocorrelation function is used to select the best IMF components with clear features. Two different components are chosen to reconstruct the flux leakage signal. The characteristic frequency of the reconstructed signal is analyzed by the Hilbert-Huang transform (HHT) and the total harmonic effective value of the characteristic component. To effectively identify and diagnose the generator stator and rotor interturn short circuit fault, RCMDE is used for the flux leakage reconstructed signal. The experimental results under different stator and rotor short-circuit levels show that this diagnosis method can effectively extract the weak feature information from the external flux leakage signals and perform fault feature extraction and recognition for stator and rotor short circuits.

RCMFRDE: Refined Composite Multiscale Fluctuation-Based Reverse Dispersion Entropy for Feature Extraction of Ship-Radiated Noise

Dispersion entropy (DE), as a newly proposed entropy, has achieved remarkable results in its application. In this paper, on the basis of DE, combined with coarse-grained processing, we introduce the fluctuation and distance information of signal and propose the refined composite multiscale fluctuation-based reverse dispersion entropy (RCMFRDE). As an emerging complexity analysis mode, RCMFRDE has been used for the first time for the feature extraction of ship-radiated noise signals to mitigate the loss caused by the misclassification of ships on the ocean. Meanwhile, a classification and recognition method combined with K-nearest neighbor (KNN) came into being, namely, RCMFRDE-KNN. The experimental results indicated that RCMFRDE has the highest recognition rate in the single feature case and up to 100% in the double feature case, far better than multiscale DE (MDE), multiscale fluctuation-based DE (MFDE), multiscale permutation entropy (MPE), and multiscale reverse dispersion entropy (MRDE), and all the experimental results show that the RCMFRDE proposed in this paper improves the separability of the commonly used entropy in the hydroacoustic domain.

A novel method for frequency feature extraction of ship radiated noise based on variational mode decomposition, double coupled Duffing chaotic oscillator and multivariate multiscale dispersion entropy

The processing of ship radiated noise is a research hotspot in the field of military and marine resource exploration. Frequency characteristic is an important characteristic of ship radiated noise. Ship radiated noise consists of line spectrum and continuous spectrum, and the frequency features are mainly reflected by line spectrum. Accurate extraction of line spectrum frequency feature is of great significance to realize ship target classification and recognition. This paper proposes a novel method for frequency feature extraction of ship radiated noise based on variational mode decomposition (VMD), double coupled Duffing chaotic oscillator (DCDCO) and multivariate multiscale dispersion entropy (mvMDE), named VMD-DCDCO-mvMDE. For the first time, DCDCO and mvMDE are applied to the feature extraction of ship radiated noise. Firstly, ship radiated noise is decomposed into a series of intrinsic mode functions (IMFs) by VMD. The decomposition layers K of VMD are determined according to the decomposition results of EMD. Secondly, adjust γand let d = 2 to leave DCDCO in critical chaotic state and detect the frequency of low frequency IMF. The mvMDE of the output signal of chaotic oscillator is calculated in the frequency range of DCDCO in the large scale periodic state, and the accurate frequency is determined by the minimum value of mvMDE. Finally, the extracted frequency feature parameters are input into the self-organizing mapping (SOM) neural network to classify and recognize different ship targets. In the analog signal experiment, the maximum frequency error of VMD-DCDCO-MVMDE extraction is only 0.01%, while the minimum error of the current method is 0.33%. The proposed method can extract frequency feature more accurately. In the measured signal experiment, the results show that the correct rate of the proposed method can reach 100% for the classification and recognition of the three groups of ship radiated noise, while the correct rate of the current method is the highest at 84%. The proposed method can effectively extract the line spectrum frequency feature of ship radiated noise, and the classification accuracy is higher, and the recognition effect is better.

De-Noising of Magnetotelluric Signals by Discrete Wavelet Transform and SVD Decomposition

Magnetotelluric (MT) sounding data can easily be damaged by various types of noise, especially in industrial areas, where the quality of measured data is poor. Most traditional de-noising methods are ineffective to the low signal-to-noise ratio of data. To solve the above problem, we propose the use of a de-noising method for the detection of noise in MT data based on discrete wavelet transform and singular value decomposition (SVD), with multiscale dispersion entropy and phase space reconstruction carried out for pretreatment. No “over processing” takes place in the proposed method. Compared with wavelet transform and SVD decomposition in synthetic tests, the proposed method removes the profile of noise more completely, including large-scale noise and impulse noise. For high levels or low levels of noise, the proposed method can increase the signal-to-noise ratio of data more obviously. Moreover, application to the field MT data can prove the performance of the proposed method. The proposed method is a feasible method for the elimination of various noise types and can improve MT data with high noise levels, obtaining a recovery in the response. It can improve abrupt points and distortion in MT response curves more effectively than the robust method can.

Bearing Fault Diagnosis Using Refined Composite Generalized Multiscale Dispersion Entropy-Based Skewness and Variance and Multiclass FCM-ANFIS.

Bearing vibration signals typically have nonlinear components due to their interaction and coupling effects, friction, damping, and nonlinear stiffness. Bearing faults affect the signal complexity at various scales. Hence, measuring signal complexity at different scales is helpful to diagnosis of bearing faults. Numerous studies have investigated multiscale algorithms; nevertheless, multiscale algorithms using the first moment lose important complexity data. Accordingly, generalized multiscale algorithms have been recently introduced. The present research examined the use of refined composite generalized multiscale dispersion entropy (RCGMDispEn) based on the second moment (variance) and third moment (skewness) along with refined composite multiscale dispersion entropy (RCMDispEn) in bearing fault diagnosis. Moreover, multiclass FCM-ANFIS, which is a combination of adaptive network-based fuzzy inference systems (ANFIS), was developed to improve the efficiency of rotating machinery fault classification. According to the results, it is recommended that generalized multiscale algorithms based on variance and skewness be examined for diagnosis, along with multiscale algorithms, and be used to achieve an improvement in the results. The simultaneous usage of the multiscale algorithm and generalized multiscale algorithms improved the results in all three real datasets used in this study.

Application of Generalized Composite Multiscale Lempel–Ziv Complexity in Identifying Wind Turbine Gearbox Faults

Wind turbine gearboxes operate in harsh environments; therefore, the resulting gear vibration signal has characteristics of strong nonlinearity, is non-stationary, and has a low signal-to-noise ratio, which indicates that it is difficult to identify wind turbine gearbox faults effectively by the traditional methods. To solve this problem, this paper proposes a new fault diagnosis method for wind turbine gearboxes based on generalized composite multiscale Lempel–Ziv complexity (GCMLZC). Within the proposed method, an effective technique named multiscale morphological-hat convolution operator (MHCO) is firstly presented to remove the noise interference information of the original gear vibration signal. Then, the GCMLZC of the filtered signal was calculated to extract gear fault features. Finally, the extracted fault features were input into softmax classifier for automatically identifying different health conditions of wind turbine gearboxes. The effectiveness of the proposed method was validated by the experimental and engineering data analysis. The results of the analysis indicate that the proposed method can identify accurately different gear health conditions. Moreover, the identification accuracy of the proposed method is higher than that of traditional multiscale Lempel–Ziv complexity (MLZC) and several representative multiscale entropies (e.g., multiscale dispersion entropy (MDE), multiscale permutation entropy (MPE) and multiscale sample entropy (MSE)).

A Novel Feature Extraction Method for Power Transformer Vibration Signal Based on CEEMDAN and Multi-Scale Dispersion Entropy.

Effective diagnosis of vibration fault is of practical significance to ensure the safe and stable operation of power transformers. Aiming at the traditional problems of transformer vibration fault diagnosis, a novel feature extraction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-scale dispersion entropy (MDE) was proposed. In this paper, CEEMDAN method is used to decompose the original transformer vibration signal. Additionally, then MDE is used to capture multi-scale fault features in the decomposed intrinsic mode functions (IMFs). Next, the principal component analysis (PCA) method is employed to reduce the feature dimension and extract the effective information in vibration signals. Finally, the simplified features are sent into density peak clustering (DPC) to get the fault diagnosis results. The experimental data analysis shows that CEEMDAN-MDE can effectively extract the information of the original vibration signals and DPC can accurately diagnose the types of transformer faults. By comparing different algorithms, the practicability and superiority of this proposed method are verified.

Bearing fault identification based on stacking modified composite multiscale dispersion entropy and optimised support vector machine

Bearing vibration signals have strong non-linear characteristics and contain significant interference noise, however, entropy is an effective means of rationalising the complexity of time-series data and provides a feasible solution for fault feature extraction, classification, and recognition. A novel stacking modified composite multiscale dispersion entropy (SMCMDE) algorithm is proposed to overcome the deficiency of multiscale dispersion entropy. Firstly, the signal is preprocessed by filtering. Then, the stacking operation was applied to the filtered signal, and the standard deviation of the coarse-grained sequence was calculated to replace the original mean operation for correction. The analysis of simulation signals shows that the proposed algorithm has certain advantages over other entropy algorithms. Subsequently, an intelligent bearing fault identification method based on SMCMDE and equilibrium optimiser-based support vector machine (EO-SVM) is proposed. The results from tests on bearings show that the proposed method can not only extract the sensitive features of bearing faults, but also has higher accuracy and stability than other methods.

A bearing fault diagnosis method based on multiscale dispersion entropy and GG clustering

Fault diagnosis of rolling bearings depends on the construction of an effective index and a reasonable identification method of fault features. In this paper, an effective method to identify fault type and degree is presented. Firstly, each element in the training samples is decomposed by ensemble empirical mode decomposition, and then the dispersion entropy of the intrinsic mode function is calculated to construct the feature matrix of the training samples. Secondly, principal component analysis visually reduces the dimension of the feature matrix, and the Gath-Geva clustering method divides the reduced two-dimensional matrix to obtain the clustering center and category of the training sample features. Finally, the normalized clustering distance between the feature matrix of test samples and the clustering center of training samples is used to judge the membership. The effectiveness of the proposed method was verified by the Case Western Reserve University (CWRU) data set, the QPZZ-II platform, and Cincinnati Intelligent Maintenance Systems.