Composite Multiscale Weighted Permutation Entropy Research Articles

Bearing fault diagnosis of induction machines using VMD-DWT and composite multiscale weighted permutation entropy

PurposeBearings play a critical role in the reliable operation of induction machines, and their failure can lead to significant operational challenges and downtime. Detecting and diagnosing these defects is imperative to ensure the longevity of induction machines and preventing costly downtime. The purpose of this paper is to develop a novel approach for diagnosis of bearing faults in induction machine.Design/methodology/approachTo identify the different fault states of the bearing with accurately and efficiently in this paper, the original bearing vibration signal is first decomposed into several intrinsic mode functions (IMFs) using variational mode decomposition (VMD). The IMFs that contain more noise information are selected using the Pearson correlation coefficient. Subsequently, discrete wavelet transform (DWT) is used to filter the noisy IMFs. Second, the composite multiscale weighted permutation entropy (CMWPE) of each component is calculated to form the features vector. Finally, the features vector is reduced using the locality-sensitive discriminant analysis algorithm, to be fed into the support vector machine model for training and classification.FindingsThe obtained results showed the ability of the VMD_DWT algorithm to reduce the noise of raw vibration signals. It also demonstrated that the proposed method can effectively extract different fault features from vibration signals.Originality/valueThis study suggested a new VMD_DWT method to reduce the noise of the bearing vibration signal. The proposed approach for bearing fault diagnosis of induction machine based on VMD-DWT and CMWPE is highly effective. Its effectiveness has been verified using experimental data.

Analysis of axial fretting mode and mechanical model for a four-row tapered roller bearing

Studies show that relative sliding of small amplitude may result in fretting wear or fatigue of the contact surface between the roller and the raceway. Generally, fretting mode in a ball-on-flat contact include tangential, radial, torsional and rotational modes. In the present study, the new concept of axial fretting mode and the corresponding mechanical model are proposed for the study of a four-row tapered roller bearing (FTRB) in the roughing mill. The mechanical models of axial thrust and radial load causing axial fretting phenomenon are established and the coupled load distributions are calculated by considering the pre-displacement principle and the elastic contact theory. Moreover, an engineering test rig is built to collect the vibration signals caused by axial fretting phenomenon. The improved multivariate variational mode decomposition (IMVMD) combined with multivariate composite multiscale weighted permutation entropy (MCMWPE) are applied to process the vibration signals in order to provide better maintenance strategy about the FTRBs. The analysis results indicate that the axial fretting regime and slip region of contact surface are affected by axial thrust, friction coefficient, and roller position angle. The axial thrust under different cross angles varies nonlinearly and the radial load of the contact surface varies with the row number and position angle of the roller. The MCMWPE of fretting bearing is significantly lower than that of healthy bearing, and there is a regular impact phenomenon.

Rolling mill bearings fault diagnosis based on improved multivariate variational mode decomposition and multivariate composite multiscale weighted permutation entropy

The multi-row bearings of rolling mills are subject to axial and radial loads. Due to the complex working conditions, it is difficult to achieve better results in fault diagnosis by analyzing signal directional vibration signals. In order to realize the fault diagnosis of bearings subjected to multiple directional loads, this paper introduces the idea of cooperative processing of multi-sensing signals and proposes a fault feature extraction method of improved multivariate variational mode decomposition (IMVMD) combined with multivariate composite multiscale weighted permutation entropy (MCMWPE). First, reconstruct the signal by variational modal decomposition (VMD). Upgrade VMD to multi-channel decomposition mode by considering the correlation of the multi-channel signal and optimizing its parameters. Secondly, propose the method to calculate multi-channel signal entropy. Represent bearing fault features by calculating the MCMWPE of multi-channel reconstructed signals. The method proposed in this paper is validated on experiment rolling mill datasets and actual rolling mill datasets from a factory. Entropy curves and PSO-SVM classification results show that IMVMD-MCMWPE can extract fault features better than other methods.

Fault diagnosis of rolling bearing using a refined composite multiscale weighted permutation entropy

The health status information of rolling bearings is often contained in vibration signals, but it is difficult to detect bearing defects directly through vibration signals. To effectively extract the key feature information hidden in the original signal, this paper proposes the refined composite multiscale weighted permutation entropy (RCMWPE) method to efficiently characterize the operating state of the bearing. The proposed method focuses on two aspects: the improved version reduces the dependence of entropy on the length of the original time series, and the error caused by considering the amplitude information is suppressed. The performance of the proposed method is evaluated by synthetic signals and real bearing data, and compared with other traditional methods. By analyzing bearing signals of different fault types and different degrees of damage, it is verified that the proposed method can obtain more stable and reliable results and achieve higher fault diagnosis accuracy.

Intelligent fault diagnosis of rolling bearings based on refined composite multi-scale dispersion q-complexity and adaptive whale algorithm-extreme learning machine

In order to extract the non-linear fault characteristics of rolling bearings more accurately, a novel nonlinear dynamical analysis method, referred to as the refined composite multi-scale dispersion q-complexity (RCMSDQC), is proposed for fault feature extraction of rolling bearings. To improve further the overall performance of the extreme learning machine (ELM) algorithm, the adaptive whale optimization algorithm (AWOA) is used to determine the input weights and hidden layer biases of the ELM. The RCMSDQC has the advantages of strong feature extraction ability and stability compared to the composite multi-scale weighted permutation entropy (CMSWPE) and composite multi-scale permutation entropy (CMSPE) methods. Furthermore, compared to the whale optimization algorithm, particle swarm optimization, and genetic algorithm, the AWOA shows a superior performance in the benchmark function comparison experiment. Based on the experimental rolling bearing data from the Paderborn University, the performance of the proposed method is further evaluated. The experimental results indicate that the proposed fault diagnosis method can identify the type and severity of rolling bearing faults with an accuracy of 99.1%.

Modified multiscale weighted permutation entropy and optimized support vector machine method for rolling bearing fault diagnosis with complex signals

The rolling bearing vibration signals are complex, non-linear, and non-stationary, it is difficult to extract the sensitive features and diagnose faults by conventional signal processing methods. This paper focuses on the sensitive features extraction and pattern recognition for rolling bearing fault diagnosis and proposes a novel intelligent fault-diagnosis method based on generalized composite multiscale weighted permutation entropy (GCMWPE), supervised Isomap (S-Iso), and marine predators algorithm-based support vector machine (MPA-SVM). Firstly, a novel non-linear technology named GCMWPE was presented, allowing the extraction of bearing features from multiple scales and enabling the construction of a high-dimensional feature set. The GCMWPE uses the generalized composite coarse-grained structure to overcome the shortcomings of the original structure in multiscale weighted permutation entropy and obtain more stable entropy values. Subsequently, the S-Iso algorithm was introduced to obtain the main features and reduce the GCMWPE set dimensionality. Finally, a combination of GCMWPE and S-Iso set was input to the MPA-SVM for diagnosis and identification. The marine predators algorithm (MPA) was used to obtain the optimal SVM parameters. The effectiveness of the proposed fault diagnosis method was confirmed through two bearing fault diagnosis experiments. The results have shown that the proposed method can be used to correctly diagnose bearing states with high diagnostic accuracy.

A novel method of composite multiscale weighted permutation entropy and machine learning for fault complex system fault diagnosis

A novel fault diagnosis method is proposed for rolling bearing by combining extreme-point symmetric mode decomposition (ESMD) composite multiscale weighted permutation entropy (CMWPE) and gravitational search algorithm based on multiple adaptive constraint strategy (MACGSA) optimized least squares support vector machine (LSSVM). In order to solve the problem of intrinsic mode function (IMF) modal aliasing and small differences in fault features, ESMD and CMWPE are used to obtain a more sensitive high-dimensional feature vector set. Aiming at the low accuracy of LSSVM fault diagnosis, MACGSA was used to optimize LSSVM to improve the accuracy of fault diagnosis. ESMD is used to process the rolling bearing data to obtain a series of IMFs; Then, extracting the CMWPE values of IMFs to form a high-dimensional feature vector set; Finally, the MACGSA-LSSVM model is adopted to achieve fault classification. Compared with other diagnostic methods, this method has higher diagnostic accuracy.

Identification of Denatured Biological Tissues Based on Time-Frequency Entropy and Refined Composite Multi-Scale Weighted Permutation Entropy during HIFU Treatment.

Identification of denatured biological tissue is crucial to high intensity focused ultrasound (HIFU) treatment. It is not easy for intercepting ultrasonic scattered echo signals from HIFU treatment region. Therefore, this paper employed time-frequency entropy based on generalized S-transform (GST) to intercept ultrasonic echo signals. First, the time-frequency spectra of ultrasonic echo signal is obtained by GST, which is concentrated around the real instantaneous frequency of the signal. Then the time-frequency entropy is calculated based on time-frequency spectra. The experimental results indicate that the time-frequency entropy of ultrasonic echo signal will be abnormally high when ultrasonic signal travels across the boundary between normal region and treatment region in tissues. Ultrasonic scattered echo signals from treatment region can be intercepted by time-frequency entropy. In addition, the refined composite multi-scale weighted permutation entropy (RCMWPE) is proposed to evaluate the complexity of nonlinear time series. Comparing with multi-scale permutation entropy (MPE) and multi-scale weighted permutation entropy (MWPE), RCMWPE not only measures complexity of signal including amplitude information, but also improves the stability and reliability of multi-scale entropy. The RCMWPE and MPE are applied to 300 cases of actual ultrasonic scattered echo signals (including 150 cases in normal status and 150 cases in denatured status). It is found that the RCMWPE and MPE values of denatured tissues are higher than those of the normal tissues. Both RCMWPE and MPE can be used to distinguish normal tissues and denatured tissues. However, there are fewer feature points in the overlap region between RCMWPE of denatured tissues and normal tissues compared with MPE. The intra-class distance and the inter-class distance of RCMWPE are less and greater respectively than MPE. The difference between denatured tissues and normal tissues is more obvious when RCMWPE is used as the characteristic parameter. The results of this study will be helpful to guide doctors to obtain more accurate assessment of treatment effect during HIFU treatment.

Composite multi-scale weighted permutation entropy and extreme learning machine based intelligent fault diagnosis for rolling bearing

Multi-scale permutation entropy (MPE) has been proven to be an effective nonlinear dynamic analysis tool for complexity and irregularity evaluation of rolling bearing. Nevertheless, MPE still has some issues that need to be addressed. First, the coarse grained process used in MPE will shorten the length of time series and result in mode information loss, especially for short time series. Second, different patterns of a symbol cannot be distinguished by permutation entropy and MPE. Inspired by the thought of composite coarse graining and weighted permutation entropy, the composite multi-scale weighted permutation entropy (CMWPE) methodology is proposed in this paper. Compared with MPE, CMWPE preserves much more useful information by adding the weighted factor and using composite coarse graining to optimize the process of coarse-gained time series, where multiple time series information is considered for the same scale factor. The simulation synthetic signals are used to demonstrate the effectiveness of CMWPE and the results show that CMWPE has less dependence on data length and the estimated entropy values are much more stable than the other existing methods. Based on CMWPE, a new intelligent fault diagnosis scheme for rolling bearing is proposed with combination of extreme learning machine. Finally, the proposed fault diagnosis method is applied to two diagnostic cases of rolling bearing and the results verified the effectiveness and superiority of the proposed approach to MPE and MWPE.

Rolling Bearing Diagnosis Based on Composite Multiscale Weighted Permutation Entropy.

In this paper, composite multiscale weighted permutation entropy (CMWPE) is proposed to evaluate the complexity of nonlinear time series, and the advantage of the CMWPE method is verified through analyzing the simulated signal. Meanwhile, considering the complex nonlinear dynamic characteristics of fault rolling bearing signal, a rolling bearing fault diagnosis approach based on CMWPE, joint mutual information (JMI) feature selection, and k-nearest-neighbor (KNN) classifier (CMWPE-JMI-KNN) is proposed. For CMWPE-JMI-KNN, CMWPE is utilized to extract the fault rolling bearing features, JMI is applied for sensitive features selection, and KNN classifier is employed for identifying different rolling bearing conditions. Finally, the proposed CMWPE-JMI-KNN approach is used to analyze the experimental dataset, the analysis results indicate the proposed approach could effectively identify different fault rolling bearing conditions.