scholarly journals Fault Diagnosis of Wheel Flat Using Empirical Mode Decomposition-Hilbert Envelope Spectrum

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Hua Jiang ◽  
Jianhui Lin
Keyword(s):  
Empirical Mode Decomposition ◽  
Degrees Of Freedom ◽  
Frequency Component ◽  
Dynamic Responses ◽  
Railway Vehicle ◽  
Vehicle Speed ◽  
Mode Decomposition ◽  
Hilbert Envelope ◽  
Wheel Flat ◽  
Envelope Spectrum

We establish the Injury Model of Wheel Flats with 10 degrees of freedom and calculate the dynamic responses of the railway vehicle system, which include different vehicle speeds and different length flats. The Hilbert envelope spectrum method based on Empirical Mode Decomposition (EMD) is proposed according to the nonstationary characteristics of axle box acceleration (ABA) signal. The vibration characteristics of the ABA are studied thoroughly. And then the effects concerning speed and flat length on the diagnosis results are analyzed. The simulation results show the amplitude corresponding to the frequency component of wheel flats raise with the increasing of the wheel flat length when the single or double wheel flats impact the track at the same vehicle speed. In other words, the longer the wheel flat is, the greater the magnitude of the decomposition result is. In the same vehicle speed, the amplitude corresponding to the frequency component of wheel flat is minimum when the two flats’ phase difference is 180°. With the same flat length (single or double wheel flats), the amplitude corresponding to the frequency components of wheel flats decreases with the increasing of the speed. This method could accurately and effectively identify the frequency of wheel flats.

A Fractional Derivative Model for Rubber Spring of Primary Suspension in Railway Vehicle Dynamics

2017 ◽  
Vol 3 (3) ◽  
Author(s):  
Dawei Zhang ◽  
Shengyang Zhu
Keyword(s):  
Fractional Derivative ◽  
Degrees Of Freedom ◽  
Excitation Frequency ◽  
Coupled System ◽  
Dynamic Responses ◽  
Viscous Force ◽  
Railway Vehicle ◽  
Spring Model ◽  
The Difference ◽  
Amplitude Dependency

This paper presents a nonlinear rubber spring model for the primary suspension of the railway vehicle, which can effectively describe the amplitude dependency and the frequency dependency of the rubber spring, by taking the elastic force, the fractional derivative viscous force, and nonlinear friction force into account. An improved two-dimensional vehicle–track coupled system is developed based on the nonlinear rubber spring model of the primary suspension. Nonlinear Hertz theory is used to couple the vehicle and track subsystems. The railway vehicle subsystem is regarded as a multibody system with ten degrees-of-freedom, and the track subsystem is treated as finite Euler–Bernoulli beams supported on a discrete–elastic foundation. Mechanical characteristic of the rubber spring due to harmonic excitations is analyzed to clarify the stiffness and damping dependencies on the excitation frequency and the displacement amplitude. Dynamic responses of the vehicle–track coupled dynamics system induced by the welded joint irregularity and random track irregularity have been performed to illustrate the difference between the Kelvin–Voigt model and the proposed model in the time and frequency domain.

scholarly journals Experimental Investigation of the Diagnosis of Angular Contact Ball Bearings Using Acoustic Emission Method and Empirical Mode Decomposition

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Ramtin Tabatabaei ◽  
Aref Aasi ◽  
Seyed Mohammad Jafari ◽  
Enrico Ciulli
Keyword(s):  
Acoustic Emission ◽  
Empirical Mode Decomposition ◽  
High Speed ◽  
Early Stage ◽  
Signal To Noise Ratio ◽  
Operating Conditions ◽  
Outer Race ◽  
Mode Decomposition ◽  
Speed Performance ◽  
Envelope Spectrum

Early detection of angular contact bearings, one of the important subsets of rolling element bearings (REBs), is critical for applications of high accuracy and high speed performance. In this study, acoustic emission (AE) method was applied to an experimental case with defects on angular contact bearing. AE signals were collected by AE sensors in different operating conditions. Signal to noise ratio (SNR) was calculated by kurtosis to entropy ratio (KER), then acquired signals were denoised by empirical mode decomposition (EMD) method, and optimal intrinsic mode function (IMF) was selected by the proposed method. Finally, envelope spectrum was applied to the denoised signals, and frequencies of defects were obtained in different rotating speeds, loadings, and defect sizes. For the first time, a small defect with width of 0.3 mm and loading of 475 N was detected in early stage of 0.04 KHz. Moreover, a comparison between theoretical and extracted defect frequencies suggested that our method successfully detected localized defects in both inner and outer race. Our results show promise in detecting small size defects in REBs.

Hunting stability analysis of a railway vehicle system using a novel non-linear creep model

2012 ◽  
Vol 226 (6) ◽  
pp. 612-629 ◽  
Author(s):  
Yung-Chang Cheng
Keyword(s):  
Degrees Of Freedom ◽  
Lateral Displacement ◽  
Creep Model ◽  
Railway Vehicle ◽  
Vehicle Speed ◽  
System Parameters ◽  
Proposed Model ◽  
Linear Creep ◽  
Non Linear ◽  
Six Dof

A non-linear creep model that considers non-constant creep coefficients that vary as a function of vehicle speed is derived using Hertz contact theory, Kalker’s linear theory and a heuristic non-linear creep model. The proposed model is created by modifying the heuristic non-linear creep model by adding a linear creep moment and the semi-axis lengths in the non-linearity of the saturation constant. In this paper, the vehicle is modeled by a system with 28 degrees of freedom, taking into consideration the lateral displacement, vertical displacement, roll angle and yaw angle of each wheelset, the truck frames and car body. To analyze the respective effects of the major system parameters on the vehicle dynamics, the 28 degree-of-freedom (DOF) system is reduced to a 25-DOF model, by excluding designated subsets of the system parameters. The accuracy of the present analysis is verified by comparing a six-DOF system and the current numerical results with results in the literature. The effects of suspension parameters of a vehicle on the critical hunting speeds evaluated by the currently proposed model, the traditional non-linear creep model and the linear creep model are illustrated. In most cases, the obtained results show that the critical hunting speed evaluated using the new non-linear creep model is greater than that derived using the traditional non-linear creep model. Additionally, the critical hunting speed evaluated using the linear creep model is higher than that evaluated using the currently proposed non-linear creep model.

EMD and Envelope Spectrum Based Bearing Fault Detection

2012 ◽  
Vol 459 ◽  
pp. 233-237 ◽  
Author(s):  
Zhen Tao Li ◽  
Hui Li
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Fault Detection And Diagnosis ◽  
Mode Decomposition ◽  
Analysis Technique ◽  
Oscillation Modes ◽  
Intrinsic Oscillation ◽  
Detection And Diagnosis ◽  
Envelope Spectrum

A novel method to fault diagnosis of bearing based on empirical mode decomposition (EMD) and envelope spectrum is presented. EMD method is self-adaptive to non-stationary and non-linear signal. The methodology developed in this paper decomposes the original vibration signal in intrinsic oscillation modes, using the empirical mode decomposition. Then the envelope spectrum is applied to the selected intrinsic mode function that stands for the bearing faults. The basic principle is firstly introduced in detail. Then the EMD is applied in the research of the fault detection and diagnosis of the bearing. The experimental results show that the proposed method based on EMD and envelope spectrum analysis technique can effectively diagnose the faults of bearing.

scholarly journals Fault Diagnosis of Rolling Bearing based on an Improved Denoising Technique using Complete Ensemble Empirical Mode Decomposition and Adaptive Thresholding Method

2021 ◽  
Author(s):  
Prashant Kumar Sahu ◽  
Rajiv Nandan Rai
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal Reconstruction ◽  
Low Frequency ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Adaptive Thresholding ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Envelope Spectrum

Abstract The vibration signals for rotating machines are generally polluted by excessive noise and can lose the fault information at the early development phase. In this paper, an improved denoising technique is proposed for early faults diagnosis of rolling bearing based on the complete ensemble empirical mode decomposition (CEEMD) and adaptive thresholding (ATD) method. Firstly, the bearing vibration signals are decomposed into a set of various intrinsic mode functions (IMFs) using CEEMD algorithm. The IMFs grouping and selection are formed based upon the correlation coefficient value. The noise-predominant IMFs are subjected to adaptive thresholding for denoising and then added to the low-frequency IMFs for signal reconstruction. The effectiveness of the proposed method denoised signals are measured based on kurtosis value and the envelope spectrum analysis. The presented method results on experimental datasets illustrate that the proposed approach is an effective denoising technique for early fault detection in the rolling bearing.

scholarly journals Predicting the Carbon Price Sequence in the Shenzhen Emissions Exchange Using a Multiscale Ensemble Forecasting Model Based on Ensemble Empirical Mode Decomposition

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1907 ◽  
Author(s):  
Jianguo Zhou ◽  
Xuechao Yu ◽  
Xiaolei Yuan
Keyword(s):  
Empirical Mode Decomposition ◽  
Low Frequency ◽  
Frequency Component ◽  
Carbon Price ◽  
Ensemble Empirical Mode Decomposition ◽  
Ensemble Forecasting ◽  
High Frequency Component ◽  
Forecasting Model ◽  
Mode Decomposition ◽  
Learning Machine

Accurately predicting the carbon price sequence is important and necessary for promoting the development of China’s national carbon trading market. In this paper, a multiscale ensemble forecasting model that is based on ensemble empirical mode decomposition (EEMD-ADD) is proposed to predict the carbon price sequence. First, the ensemble empirical mode decomposition (EEMD) is applied to decompose a carbon price sequence, SZA2013, into several intrinsic mode functions (IMFs) and one residual. Second, the IMFs and the residual are restructured via a fine-to-coarse reconstruction algorithm to generate three stationary and regular frequency components that high frequency component, low frequency component, and trend component. The fluctuation of each component can effectively reveal the factors that influence market operation. Third, extreme learning machine (ELM) is applied to forecast the trend component, support vector machine (SVM) is applied to forecast the low frequency component and the high frequency component is predicted via PSO-ELM, which means extreme learning machine whose input weights and bias threshold were optimized by particle swarm optimization. Then, the predicted values are combined to form a final predicted value. Finally, using the relevant error-type and trend-type performance indexes, the proposed multiscale ensemble forecasting model is shown to be more robust and accurate than the single format models. Three additional emission allowances from the Shenzhen Emissions Exchange are used to validate the model. The empirical results indicate that the established model is effective, efficient, and practical in terms of its statistical measures and prediction performance.

scholarly journals Railway Vehicle Wheel Flat Detection with Multiple Records Using Spectral Kurtosis Analysis

2021 ◽  
Vol 11 (9) ◽  
pp. 4002
Author(s):  
Araliya Mosleh ◽  
Pedro Aires Montenegro ◽  
Pedro Alves Costa ◽  
Rui Calçada
Keyword(s):  
Detection System ◽  
Railway Vehicle ◽  
Optimal Position ◽  
Maintenance Costs ◽  
Spectral Kurtosis ◽  
Railway Systems ◽  
Railway Infrastructure ◽  
Wheel Flat ◽  
Infrastructure Maintenance ◽  
Envelope Spectrum

The gradual deterioration of train wheels can increase the risk of failure and lead to a higher rate of track deterioration, resulting in less reliable railway systems with higher maintenance costs. Early detection of potential wheel damages allows railway infrastructure managers to control railway operators, leading to lower infrastructure maintenance costs. This study focuses on identifying the type of sensors that can be adopted in a wayside monitoring system for wheel flat detection, as well as their optimal position. The study relies on a 3D numerical simulation of the train-track dynamic response to the presence of wheel flats. The shear and acceleration measurement points were defined in order to examine the sensitivity of the layout schemes not only to the type of sensors (strain gauge and accelerometer) but also to the position where they are installed. By considering the shear and accelerations evaluated in 19 positions of the track as inputs, the wheel flat was identified by the envelope spectrum approach using spectral kurtosis analysis. The influence of the type of sensors and their location on the accuracy of the wheel flat detection system is analyzed. Two types of trains were considered, namely the Alfa Pendular passenger vehicle and a freight wagon.

Gearbox Fault Detection Using Empirical Mode Decomposition

2004 ◽  
Author(s):  
Xianfeng Fan ◽  
Ming J. Zuo
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Original Data ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Stationary Vibration ◽  
Non Stationary Signal ◽  
Envelope Spectrum

Local faults in a gearbox cause impacts and the collected vibration signal is often non-stationary. Identification of impulses within the non-stationary vibration signal is key to fault detection. Recently, the technique of Empirical Mode Decomposition (EMD) was proposed as a new tool for analysis of non-stationary signal. EMD is a time series analysis method that extracts a custom set of bases that reflects the characteristic response of a system. The Intrinsic Mode Functions (IMFs) within the original data can be obtained through EMD. We expect that the change in the amplitude of the special IMF’s envelope spectrum will become larger when fault impulses are present. Based on this idea, we propose a new fault detection method that combines EMD with Hilbert transform. The proposed method is compared with both the Hilbert-Huang transform and the wavelet transform using simulated signal and real signal collected from a gearbox. The results obtained show that the proposed method is effective in capturing the hidden fault impulses.

A pseudo-modal structural damage index based on orthogonal empirical mode decomposition

2019 ◽  
Vol 233 (23-24) ◽  
pp. 7545-7564 ◽  
Author(s):  
Egidio Lofrano ◽  
Francesco Romeo ◽  
Achille Paolone
Keyword(s):  
Empirical Mode Decomposition ◽  
Structural Damage ◽  
Degrees Of Freedom ◽  
Damage Identification ◽  
Damage Index ◽  
Experimental Tests ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Identification Technique ◽  
Output Only

A structural damage identification technique hinged on the combination of orthogonal empirical mode decomposition and modal analysis is proposed. The output-only technique is based on the comparison between pre- and post-damage free structural vibrations signals. The latter are either kinematic (displacements, velocities or accelerations) or deformation measures (strains or curvatures). The response data are decomposed by means of the orthogonal empirical mode decomposition to derive a finite set of orthogonal intrinsic mode functions; the latter are used as a multi-frequency and data-driven basis to build pseudo-modal shapes. A new damage index, the so-called pseudo-mode index, is introduced to compare the response obtained for the two states of the structural system and detect potential damages. The performance of the devised index in detecting a localised damage is shown through numerical and experimental tests on two structural models, namely a 4-degrees-of-freedom system and a two-hinged parabolic arch.

Identification of Linear Time-Varying Dynamical Systems Using Hilbert Transform and Empirical Mode Decomposition Method

2006 ◽  
Vol 74 (2) ◽  
pp. 223-230 ◽  
Author(s):  
Z. Y. Shi ◽  
S. S. Law
Keyword(s):  
Dynamical Systems ◽  
Empirical Mode Decomposition ◽  
Hilbert Transform ◽  
Decomposition Method ◽  
Degrees Of Freedom ◽  
Linear Time ◽  
Time Varying ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
The Hilbert Transform

This paper addresses the identification of linear time-varying multi-degrees-of-freedom systems. The identification approach is based on the Hilbert transform and the empirical mode decomposition method with free vibration response signals. Three-different types of time-varying systems, i.e., smoothly varying, periodically varying, and abruptly varying stiffness and damping of a linear time-varying system, are studied. Numerical simulations demonstrate the effectiveness and accuracy of the proposed method with single- and multi-degrees-of-freedom dynamical systems.

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