scholarly journals A Fault Diagnosis Method of Train Wheelset Rolling Bearing Combined with Improved LMD and FK

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Yu Zhang ◽  
Zhuoyou Fan ◽  
Xiaorong Gao ◽  
Lin Luo
Keyword(s):  
Fault Location ◽  
Simulation Analysis ◽  
Spline Interpolation ◽  
Microphone Array ◽  
Computing Time ◽  
Rolling Bearing ◽  
Distortion Correction ◽  
Bearing Defects ◽  
Envelope Spectrum ◽  
The Impact

Trackside acoustic signals contain intense noise and nonstationary features even after Doppler distortion correction. Information on bearing defects in these signals is either weak or heavily attenuated. Thus, an improved compound interpolation envelope local mean decomposition (ICIE LMD) method combined with a fast kurtogram (FK) is proposed for wheelset bearings. In this methodology, cubic Hermite interpolation and cubic spline interpolation are employed to find the envelope of the extremal points in the ICIE LMD algorithm to improve accuracy and decrease the computing time of the decomposed signal component. An FK is sensitive to the impact signal and extracts the fault impact features efficiently. In the application, the proposed method uses ICIE LMD to decompose the multicomponent signal into several specific single product function (PF) components. The kurtosis index of the PF is calculated to select the component which contains the most fault information. Then, the selected component of PF is filtered by FK. Finally, the squared envelope spectrum is used to obtain the fault frequency and identify the fault location. The advantages of the ICIE LMD method are verified by simulation analysis. In the application, the results show that the proposed method efficiently extracts the fault features and enhances the target characteristics of the sound signals from a trackside microphone array. Furthermore, the influence of rotating frequency on locating the fault is reduced.

Median line-gram and its application in the fault diagnosis of rolling bearing

2022 ◽  
Author(s):  
Xinglong Wang ◽  
Jinde Zheng ◽  
Jun Zhang
Keyword(s):  
Frequency Band ◽  
Fault Location ◽  
Rolling Bearing ◽  
Suggested Method ◽  
Median Line ◽  
Frequency Bands ◽  
Unique Approach ◽  
Envelope Spectrum ◽  
Line Segmentation ◽  
Selection Of

Abstract The level selection of frequency band division structure relies on previous information in most gram approaches that capture the optimal demodulation frequency band (ODFB). When an improper level is specified in these approaches, the fault characteristic information contained in the produced ODFB may be insufficient. This research proposes a unique approach termed median line-gram (MELgram) to tackle the level selection problem. To divide the frequency domain signal into a series of demodulation frequency bands, a spectrum median line segmentation model based on Akima interpolation is first created. The level and boundary of the segmentation model can be adaptively identified by this means. Second, the acquired frequency bands are quantized using the negative entropy index, and the ODFB is defined as the frequency band with the largest value. Third, the envelope spectrum is used to determine the ODFB characteristic frequency to pinpoint the bearing fault location. Finally, both simulation and experimental signal analysis are used to demonstrate the efficiency of the suggested method. Furthermore, the suggested method extracts more defect feature information from the ODFB than existing methods.

scholarly journals Weak Fault Feature Extraction of Rolling Bearing Based on SVMD and Improved MOMEDA

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xinyu Wang ◽  
Jie Ma
Keyword(s):  
Fault Location ◽  
Harmonic Component ◽  
Energy Operator ◽  
Rolling Bearing ◽  
Teager Energy Operator ◽  
Mode Decomposition ◽  
Fault Features ◽  
Weak Fault ◽  
Envelope Spectrum ◽  
Teager Energy

In order to solve the problem that it is very difficult to extract fault features directly from the weak impact component of early fault signal of rolling bearing, a method combining continuous variational mode decomposition (SVMD) with modified MOMEDA based on Teager energy operator is proposed. Firstly, the low resonance impulse component in the fault signal is separated from the harmonic component and noise by SVMD, and then the Teager energy operator is used to enhance the impulse feature in the low resonance component to ensure that the accurate fault period is selected by the MOMOEDA algorithm. After further noise reduction by MOMEDA, the envelope spectrum of the signal is analyzed, and finally the fault location is determined. The results of simulation and experimental data show that this method can accurately and effectively extract the characteristic frequency of rolling bearing weak fault.

scholarly journals Rolling Bearing Fault Diagnosis Based on Component Screening Vector Local Characteristic-Scale Decomposition

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Tengfei Guan ◽  
Shijun Liu ◽  
Wenbo Xu ◽  
Zhisheng Li ◽  
Hongtao Huang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Single Channel ◽  
Simulation Analysis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Local Characteristic ◽  
Characteristic Scale ◽  
Dual Channel ◽  
Component Selection ◽  
Envelope Spectrum

The fault vibration signal of a bearing has nonstationary and nonlinear characteristics and can be regarded as the combination of multiple amplitude- and frequency-modulation components. The envelope of a single component contains the fault characteristics of a bearing. Local characteristic-scale decomposition (LCD) can decompose the vibration signal into a series of multiple intrinsic scale components. Some components can clearly reflect the running state of a bearing, and fault diagnosis is conducted according to the envelope spectrum. However, the conventional LCD takes a single-channel signal as the research object, which cannot fully reflect the characteristic information of the rotor, and the analysis results based on different channel signals of the same section will be inconsistent. To solve this problem, based on full vector spectrum technology, the homologous dual-channel information is fused. A vector LCD method based on cross-correlation coefficient component selection is given, and a simulation analysis is completed. The effectiveness of the proposed method is verified by simulated signals and experimental signals of a bearing, which provides a method for bearing feature extraction and fault diagnosis.

scholarly journals Improving the Accuracy of Fault Frequency by Means of Local Mean Decomposition and Ratio Correction Method for Rolling Bearing Failure

2019 ◽  
Vol 9 (9) ◽  
pp. 1888 ◽  
Author(s):  
Yongqiang Duan ◽  
Chengdong Wang ◽  
Yong Chen ◽  
Peisen Liu
Keyword(s):  
Fault Location ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Correction Method ◽  
Bearing Failure ◽  
Local Mean Decomposition ◽  
Local Mean ◽  
Hilbert Envelope ◽  
Ratio Correction ◽  
Envelope Spectrum

The fault frequencies are as they are and cannot be improved. One can only improve its estimation quality. This paper proposes a fault diagnosis method by combining local mean decomposition (LMD) and the ratio correction method to process the short-time signals. Firstly, the vibration signal of rolling bearing is decomposed into a series of product functions (PFs) by LMD. The PF, which contains the richest fault information, is selected to perform envelope spectrum analysis by the Hilbert transform (HT). Secondly, the Hilbert envelope spectrum of the selected PF is corrected with the ratio correction method. Finally, higher precision fault frequencies are extracted from the corrected Hilbert envelope spectrum, and then the fault location is accurately determined. The proposed method of this paper can be used in online real-time monitoring technology of rolling bearing failure.

scholarly journals Kurtosis Based Empirical Mode Decomposition for Rolling Bearing Fault Detection

2020 ◽  
Vol 327 ◽  
pp. 03003
Author(s):  
Hui Li ◽  
Xuhan Liu
Keyword(s):  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Intrinsic Mode Functions ◽  
Bearing Fault ◽  
Spectral Kurtosis ◽  
Bearing Defects ◽  
Mode Decomposition ◽  
Kurtosis Parameter ◽  
Envelope Spectrum

A bearing fault diagnosis approach based on spectral kurtosis and empirical mode decomposition (EMD) is proposed. EMD is a signal decomposition technique, which can adaptively separate a number of intrinsic mode functions (IMFs) from the vibration signal according to the architectural characteristics of the data. The spectral kurtosis parameter takes as signal impulsive indicator. Firstly, EMD is utilized to process the sampling vibration signal. And then spectral kurtosis is calculated to select the optimal intrinsic mode functions, so as to suppress the noise and highlight the transient impact feature. Finally, the envelope spectrum is computed and the fault characteristic is recognized. The experimental results show that the proposed approach can identify bearing defects effectively and provide a reliable method for gearbox fault monitoring and diagnosis.

Computer simulation study about the dependence of amorphous silicon photonic waveguides efficiency on the material quality

2020 ◽  
Vol 90 (3) ◽  
pp. 30502
Author(s):  
Alessandro Fantoni ◽  
João Costa ◽  
Paulo Lourenço ◽  
Manuela Vieira
Keyword(s):  
Amorphous Silicon ◽  
High Throughput Screening ◽  
Simulation Analysis ◽  
Low Cost ◽  
Deposition Process ◽  
Large Area ◽  
Sidewall Roughness ◽  
Sensing Applications ◽  
Fabrication Defects ◽  
The Impact

Amorphous silicon PECVD photonic integrated devices are promising candidates for low cost sensing applications. This manuscript reports a simulation analysis about the impact on the overall efficiency caused by the lithography imperfections in the deposition process. The tolerance to the fabrication defects of a photonic sensor based on surface plasmonic resonance is analysed. The simulations are performed with FDTD and BPM algorithms. The device is a plasmonic interferometer composed by an a-Si:H waveguide covered by a thin gold layer. The sensing analysis is performed by equally splitting the input light into two arms, allowing the sensor to be calibrated by its reference arm. Two different 1 × 2 power splitter configurations are presented: a directional coupler and a multimode interference splitter. The waveguide sidewall roughness is considered as the major negative effect caused by deposition imperfections. The simulation results show that plasmonic effects can be excited in the interferometric waveguide structure, allowing a sensing device with enough sensitivity to support the functioning of a bio sensor for high throughput screening. In addition, the good tolerance to the waveguide wall roughness, points out the PECVD deposition technique as reliable method for the overall sensor system to be produced in a low-cost system. The large area deposition of photonics structures, allowed by the PECVD method, can be explored to design a multiplexed system for analysis of multiple biomarkers to further increase the tolerance to fabrication defects.

Separating Multiple Moving Sources by Microphone Array Signals for Wayside Acoustic Fault Diagnosis

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Wei Xiong ◽  
Qingbo He ◽  
Zhike Peng
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Feature Matching ◽  
Matching Pursuit ◽  
Microphone Array ◽  
Time Frequency ◽  
Moving Sources ◽  
The Time Domain ◽  
Envelope Spectrum ◽  
Threshold Setting

Wayside acoustic defective bearing detector (ADBD) system is a potential technique in ensuring the safety of traveling vehicles. However, Doppler distortion and multiple moving sources aliasing in the acquired acoustic signals decrease the accuracy of defective bearing fault diagnosis. Currently, the method of constructing time-frequency (TF) masks for source separation was limited by an empirical threshold setting. To overcome this limitation, this study proposed a dynamic Doppler multisource separation model and constructed a time domain-separating matrix (TDSM) to realize multiple moving sources separation in the time domain. The TDSM was designed with two steps of (1) constructing separating curves and time domain remapping matrix (TDRM) and (2) remapping each element of separating curves to its corresponding time according to the TDRM. Both TDSM and TDRM were driven by geometrical and motion parameters, which would be estimated by Doppler feature matching pursuit (DFMP) algorithm. After gaining the source components from the observed signals, correlation operation was carried out to estimate source signals. Moreover, fault diagnosis could be carried out by envelope spectrum analysis. Compared with the method of constructing TF masks, the proposed strategy could avoid setting thresholds empirically. Finally, the effectiveness of the proposed technique was validated by simulation and experimental cases. Results indicated the potential of this method for improving the performance of the ADBD system.

scholarly journals USMED: broadening the impact of simulation analysis methodology

2005 ◽  
Author(s):  
R.R. Barton ◽  
L.W. Schruben ◽  
J.C. Ford ◽  
D. Hopkins ◽  
D. Goldsman ◽  
...  
Keyword(s):  
Simulation Analysis ◽  
Analysis Methodology ◽  
The Impact

Wind Farm Grid Integration Simulation Analysis of the Impact of Power System Transient Stability

2013 ◽  
Vol 805-806 ◽  
pp. 334-337
Author(s):  
Shi Wei Su ◽  
You Wei Zhou ◽  
Wei Xiong
Keyword(s):  
Power System ◽  
Wind Farm ◽  
Transient Stability ◽  
Simulation Analysis ◽  
Direct Access ◽  
Grid Integration ◽  
Access System ◽  
Wind Dispersion ◽  
The Impact ◽  
Single Set

Analysis compares the direct access to a single set of wind power systemTwo groups of wind farm access system directlyMultiple sets of wind farm access system directly And Multiple sets of wind dispersion access system's impact on power system transient stability. And compare the simulation results, Concluded that wind farm access capacity and its topology structure's influence on system transient stability.

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