scholarly journals Railway Wheel Flat Detection Based on Improved Empirical Mode Decomposition

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Yifan Li ◽  
Jianxin Liu ◽  
Yan Wang
Keyword(s):  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Railway Wheel ◽  
Mode Decomposition ◽  
Track Irregularity ◽  
Wheel Flat ◽  
Mixing Problem ◽  
Emd Method ◽  
Mode Mixing ◽  
Fault Characteristic

This study explores the capacity of the improved empirical mode decomposition (EMD) in railway wheel flat detection. Aiming at the mode mixing problem of EMD, an EMD energy conservation theory and an intrinsic mode function (IMF) superposition theory are presented and derived, respectively. Based on the above two theories, an improved EMD method is further proposed. The advantage of the improved EMD is evaluated by a simulated vibration signal. Then this method is applied to study the axle box vibration response caused by wheel flats, considering the influence of both track irregularity and vehicle running speed on diagnosis results. Finally, the effectiveness of the proposed method is verified by a test rig experiment. Research results demonstrate that the improved EMD can inhibit mode mixing phenomenon and extract the wheel fault characteristic effectively.

Ensemble Noise-Reconstructed Empirical Mode Decomposition for Mechanical Fault Detection

2013 ◽  
Vol 135 (2) ◽  
Author(s):  
Jing Yuan ◽  
Zhengjia He ◽  
Jun Ni ◽  
Adam John Brzezinski ◽  
Yanyang Zi
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Measured Signal ◽  
Accelerometer Data ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Mixing Problem ◽  
Emd Method ◽  
Mode Mixing

Various faults inevitably occur in mechanical systems and may result in unexpected failures. Hence, fault detection is critical to reduce unscheduled downtime and costly breakdowns. Empirical mode decomposition (EMD) is an adaptive time-frequency domain signal processing method, potentially suitable for nonstationary and/or nonlinear processes. However, the EMD method suffers from several problems such as mode mixing, defined as intrinsic mode functions (IMFs) with incorrect scales. In this paper, an ensemble noise-reconstructed EMD method is proposed to ameliorate the mode mixing problem and denoise IMFs for enhancing fault signatures. The proposed method defines the IMF components as an ensemble mean of EMD trials, where each trial is obtained by sifting signals that have been reconstructed using the estimated noise present in the measured signal. Unlike traditional denoising methods, the noise inherent in the input data is reconstructed and used to reduce the background noise. Furthermore, the reconstructed noise helps to project different scales of the signal onto their corresponding IMFs, instrumental in alleviating the mode mixing problem. Two critical issues concerned in the method, i.e., the noise estimation strategy and the number of EMD trials required for denoising are discussed. Furthermore, a comprehensive noise-assisted EMD method is proposed, which includes the proposed method and ensemble EMD (EEMD). Numerical simulations and experimental case studies on accelerometer data collected from an industrial shaving process are used to demonstrate and validate the proposed method. Results show that the proposed method can both detect impending faults and isolate multiple faults. Hence, the proposed method can act as a promising tool for mechanical fault detection.

An Optimal Ensemble Empirical Mode Decomposition Method for Vibration Signal Decomposition

2017 ◽  
Vol 139 (3) ◽  
Author(s):  
Shi-Chang Du ◽  
Tao Liu ◽  
De-Lin Huang ◽  
Gui-Long Li
Keyword(s):  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Signal Decomposition ◽  
Critical Parameters ◽  
Mode Decomposition ◽  
Eemd Method ◽  
Two Parameters ◽  
Mixing Problem ◽  
Mode Mixing

The vibration signal decomposition is a critical step in the assessment of machine health condition. Though ensemble empirical mode decomposition (EEMD) method outperforms fast Fourier transform (FFT), wavelet transform, and empirical mode decomposition (EMD) on nonstationary signal decomposition, there exists a mode mixing problem if the two critical parameters (i.e., the amplitude of added white noise and the number of ensemble trials) are not selected appropriately. A novel EEMD method with optimized two parameters is proposed to solve the mode mixing problem in vibration signal decomposition in this paper. In the proposed optimal EEMD, the initial values of the two critical parameters are selected based on an adaptive algorithm. Then, a multimode search algorithm is explored to optimize the critical two parameters by its good performance in global and local search. The performances of the proposed method are demonstrated by means of a simulated signal, two bearing vibration signals, and a vibration signal in a milling process. The results show that compared with the traditional EEMD method and other improved EEMD method, the proposed optimal EEMD method automatically obtains the appropriate parameters of EEMD and achieves higher decomposition accuracy and faster computational efficiency.

Ensemble Empirical Mode Decomposition and Sparsity Measurement as Tools Enhancing the Gear Diagnostic Capabilities of Time Synchronous Averaging

2017 ◽  
Vol 09 (02) ◽  
pp. 1750004 ◽  
Author(s):  
Pawel Rzeszucinski ◽  
Michal Juraszek ◽  
James R. Ottewill
Keyword(s):  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Diagnostic Information ◽  
Optimal Parameters ◽  
Vibration Data ◽  
Mode Decomposition ◽  
Ultrasound Signal ◽  
Diagnostic Capabilities ◽  
Mixing Problem ◽  
Mode Mixing

The paper introduces the concept of exploring the potential of Ensemble Empirical Mode Decomposition (EEMD) and Sparsity Measurement (SM) in enhancing the diagnostic information contained in the Time Synchronous Averaging (TSA) method used in the field of gearbox diagnostics. EEMD was created as a natural improvement of the Empirical Mode Decomposition which suffered from a so-called mode mixing problem. SM is heavily used in the field of ultrasound signal processing as a tool for assessing the degree of sparsity of a signal. A novel process of automatically finding the optimal parameters of EEMD is proposed by incorporating a Form Factor parameter, known from the field of electrical engineering. All these elements are combined and applied on a set of vibration data generated on a 2-stage gearbox under healthy and faulty conditions. The results suggest that combining these methods may increase the robustness of the condition monitoring routine, when compared to the standard TSA used alone.

Two Solutions of EMD Mode Mixing

2012 ◽  
Vol 433-440 ◽  
pp. 5364-5367
Author(s):  
Xue Ming Zhai ◽  
Li Na Zhao ◽  
Pan Zhang
Keyword(s):  
Signal Analysis ◽  
High Amplitude ◽  
Sampling Frequency ◽  
Mode Function ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Frequency Components ◽  
Mixing Problem ◽  
Emd Method ◽  
Mode Mixing

Based on the mode mixing problem which exists in EMD(Empirical Mode Decomposition)method, this paper provides two ways to solve this problem.First is to increase the sampling frequency, add a characteristic wave with high amplitude and frequency in the process of signal analysis,then put the FFT transformation on the IMF(intrinsic mode function) which derives from EMD method,while in the process of transformation,binary scaling to the sampling frequency to adapt to the changes of each IMF component, making the spectrum more clear and the analysis of the data with the relevant frequency components more careful.The second is EEMD ,which is proposed in recent years.We use this method to deal with the signal ,after that,then use EMD method to analyse it,which will get a good result.While a comparison with the two methods and point out the different ranges of applications.

scholarly journals A New Islanding Detection Technique using Ensemble Empirical Mode Decomposition

2020 ◽  
Vol 9 (3) ◽  
pp. 461-466
Keyword(s):  
Empirical Mode Decomposition ◽  
Distributed Generation ◽  
Gaussian White Noise ◽  
Ensemble Empirical Mode Decomposition ◽  
Islanding Detection ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Signal Processing Methods ◽  
Mixing Problem ◽  
Mode Mixing

Penetration of distributed generation (DG) is rapidly increasing but their main issue is islanding. Advanced signal processing methods needs a renewed focus in detecting islanding. The proposed scheme is based on Ensemble Empirical Mode Decomposition (EEMD) in which Gaussian white noise is added to original signal which solves the mode mixing problem of Empirical mode decomposition (EMD) and Hilbert transform is applied to obtained Intrinsic mode functions(IMF). The proposed method reliably and accurately detects disturbances at different events

A fast online bandwidth empirical mode decomposition scheme for avoidance of the mode mixing problem

2017 ◽  
Vol 232 (20) ◽  
pp. 3652-3674 ◽  
Author(s):  
SH Momeni Massouleh ◽  
Seyed Ali Hosseini Kordkheili ◽  
H Mohammad Navazi
Keyword(s):  
Empirical Mode Decomposition ◽  
Low Frequency ◽  
Dominant Frequency ◽  
Auxiliary Function ◽  
Intrinsic Mode Functions ◽  
Online Data ◽  
Decomposition Scheme ◽  
Mode Decomposition ◽  
Mixing Problem ◽  
Mode Mixing

The main objective of this work is to propose a scheme to extract intrinsic mode functions of online data with an acceptable speed as well as accuracy. For this purpose, an individual block framework method is firstly employed to extract the intrinsic mode functions. In this method, buffers are selected such that they overlap with their neighbors to prevent the end effect errors with no need for the averaging process. And in order to avoid the mode mixing problem, a bandwidth empirical mode decomposition scheme is developed to effectively improve the results. Through this scheme, an auxiliary function made of both high- and low-frequency components corresponding to noise and dominant frequency is added to data for the strengthening of the components for the better extraction of intrinsic mode functions during sifting process. An index criterion as well as a threshold limit is also introduced to separate high- and low-frequency parts of data at desired frequency range. Advantages of the proposed scheme are assessed and comparisons with the available methods are presented. Solution of different types of examples and experimentally generated data for two faulty ball bearings reveals that the present easily implemented scheme achieves results with lower computational efforts and accuracy.

A Method of Fault Diagnosis for Reciprocating Compressor Based on Phase Space Reconstruction (PSR) and Empirical Mode Decomposition (EMD)

2012 ◽  
Vol 151 ◽  
pp. 83-86
Author(s):  
Yan Liu ◽  
Wen Yuan ◽  
Shi Gang Wang
Keyword(s):  
Phase Space ◽  
Empirical Mode Decomposition ◽  
Correlation Dimension ◽  
Lorenz System ◽  
Simulation Analysis ◽  
Vibration Signal ◽  
Reciprocating Compressor ◽  
Embedding Dimension ◽  
Mode Decomposition ◽  
Emd Method

Extract correlation dimension of vibration signal of reciprocating compressor based on G-P algorithm and empirical mode decomposition（EMD）technology, with found mode of EMD de-noise, with the incorporation of method of self-interrelated function and method of pseudo-phase portrait ensure delay-time and calculate embedding dimension by EMD method. Through simulation analysis of Lorenz system, Apply the method to identify valve faults of reciprocating compressor, the result indicate that the modeling method is effective.

scholarly journals Quantification of Dynamic Properties of Pile Using Ensemble Empirical Mode Decomposition

2018 ◽  
Vol 2018 ◽  
pp. 1-6
Author(s):  
Feng Xiao ◽  
Gang S. Chen ◽  
Wael Zatar ◽  
J. Leroy Hulsey
Keyword(s):  
Empirical Mode Decomposition ◽  
Dynamic Properties ◽  
Ensemble Empirical Mode Decomposition ◽  
Frozen Soil ◽  
Frozen Ground ◽  
Mode Decomposition ◽  
Eemd Method ◽  
Pile Response ◽  
Emd Method ◽  
Mode Mixing

This paper investigated dynamical interactions between pile and frozen ground by using the ensemble empirical mode decomposition (EEMD) method. Unlike the conventional empirical mode decomposition (EMD) method, EEMD is found to be able to separate the mode patterns of pile response signals of different scales without causing mode mixing. The identified dynamic properties using the EEMD method are more accurate than those obtained from conventional methods. EEMD-based results can be used to reliably and accurately characterize pile-frozen soil interactions and help designing infrastructure foundations under permafrost condition.

Bandpass Empirical Mode Decomposition Using a Rolling Ball Algorithm

2015 ◽  
Vol 07 (01n02) ◽  
pp. 1550003 ◽  
Author(s):  
Adam Huang ◽  
Min-Yin Liu ◽  
Wei-Te Yu
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Rolling Ball ◽  
Mode Decomposition ◽  
Local Extrema ◽  
Mixing Problem ◽  
Mode Mixing

We propose using a rolling ball algorithm, which moves a ball along a time series signal, to sort the local extrema within a signal according to a geometric tangibility criterion. Letting the ball always roll above or below the signal, we are able to classify the signal’s extrema according to their tangibility: touched or not touched by the ball. Applying this ball-tangibility information to select an extremum in the empirical mode decomposition (EMD) algorithm, we are able to prevent the mode mixing problem in analyzing intermittent signals and decompose mode functions satisfying bandpass filtering properties.

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