scholarly journals A Spectrum Detection Approach for Bearing Fault Signal Based on Spectral Kurtosis

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yunfeng Li ◽  
Liqin Wang ◽  
Jian Guan
Keyword(s):  
Wavelet Spectrum ◽  
Rolling Element Bearing ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Time Frequency ◽  
Bearing Fault ◽  
Spectral Kurtosis ◽  
Spectrum Detection ◽  
Detection Approach ◽  
The Impact

According to the similarity between Morlet wavelet and fault signal and the sensitive characteristics of spectral kurtosis for the impact signal, a new wavelet spectrum detection approach based on spectral kurtosis for bearing fault signal is proposed. This method decreased the band-pass filter range and reduced the wavelet window width significantly. As a consequence, the bearing fault signal was detected adaptively, and time-frequency characteristics of the fault signal can be extracted accurately. The validity of this method was verified by the identifications of simulated shock signal and test bearing fault signal. The method provides a new understanding of wavelet spectrum detection based on spectral kurtosis for rolling element bearing fault signal.

scholarly journals Fault Diagnosis of Oil Pumping Machine Retarder Based on Sound Texture-Vibration Entropy Characteristics and Gray Wolf Optimization-Support Vector Machine

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Shutao Zhao ◽  
Ke Chang ◽  
Erxu Wang ◽  
Bo Li ◽  
Kedeng Wang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Contrast Ratio ◽  
Permutation Entropy ◽  
Support Vector ◽  
Box Dimension ◽  
Gray Wolf ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Time Frequency ◽  
The Impact

In order to diagnose the retarder faults of oil pumping machine accurately in complex environments and improve the generalization of the algorithm, a GWO-SVM fault diagnosis algorithm based on the combination of sound texture and vibration entropy characteristics was proposed. Firstly, the acquired sound signal was purified by band-pass filter, then generalized S-transform was developed to extract the box dimension, directivity, and contrast ratio, which reflect the characteristics of time-frequency spectrum, to construct three-dimensional texture eigenvectors. Secondly, the K parameter of variational mode decomposition (VMD) was reasonably selected by the energy method, and then the vibration signal was decomposed to get modal components, and the permutation entropy was obtained from modal components. Finally, joint eigenvectors were constructed and fed into SVM for learning. The gray wolf optimization (GWO) algorithm was used to optimize the parameters of the SVM model based on mixed kernel function, which reduces the impact of sensor frequency response, environmental noise, and load fluctuation disturbance on the accuracy of retarder fault diagnosis. The results showed that the GWO-SVM fault diagnosis method, which is based on the combination of sound texture and vibration entropy characteristics, makes full use of the complementary advantages of signal frequency band. And the overall diagnostic accuracy for the experimental samples reaches 100%, which has good generalization ability.

Rolling Element Bearing Condition Monitoring and Diagnosis

2010 ◽  
Vol 34-35 ◽  
pp. 332-337
Author(s):  
Hui Bin Lin ◽  
Kang Ding
Keyword(s):  
Condition Monitoring ◽  
Rolling Element Bearing ◽  
Bearing Fault ◽  
Monitoring And Diagnosis ◽  
Bearing Condition Monitoring ◽  
Envelope Detection ◽  
Rolling Element ◽  
Machine Speed ◽  
Bearing Characteristic ◽  
The Impact

Bearing failure is one of the foremost causes of breakdown in rotating machinery. To date, Envelope detection is always used to identify faults occurring at the Bearing Characteristic Frequencies (BCF). However, because the impact vibration generated by a bearing fault has relatively low energy, it is often overwhelmed by background noise and difficult to identify. Combined the results of extensive experiments performed in a series of bearings with artificial damage, this research investigates the effect of many influencing factors, such as demodulation methods, sampling frequency, variable machine speed and the signals collected in different directions, on the effectiveness of demodulation and the implications for bearing fault detection. By understanding these effects, a more skillful application of the envelope detection in condition monitoring and diagnosis is achieved.

Wigner–Ville distribution based on cyclic spectral density and the application in rolling element bearings diagnosis

2011 ◽  
Vol 225 (12) ◽  
pp. 2831-2847 ◽  
Author(s):  
Y Zhou ◽  
J Chen ◽  
G M Dong ◽  
W B Xiao ◽  
Z Y Wang
Keyword(s):  
Fault Diagnosis ◽  
Spectral Density ◽  
Wigner Distribution ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Accurate Analysis ◽  
Time Frequency ◽  
Bearing Fault ◽  
Rolling Element ◽  
Cross Terms

The vibration signals of rolling element bearings are random cyclostationary when they have faults. Also, statistical properties of the signals change periodically with time. The accurate analysis of time-varying signals is an essential pre-requisite for the fault diagnosis and hence safe operation of rolling element bearings. The Wigner distribution is probably most widely used among the Cohen’s class in order to describe how the spectral content of a signal changes over time. However, the basic nature of such signals causes significant interfering cross-terms, which do not permit a straightforward interpretation of the energy distribution. To overcome this difficulty, the Wigner–Ville distribution (WVD) based on the cyclic spectral density (CSD) is discussed in this article. It is shown that the improved WVD, based on CSD of a long time series, can render the time–frequency distribution less susceptible to noise, and restrain the cross-terms in the time–frequency domain. Simulation and experiment of the rolling element-bearing fault diagnosis are performed, and the results indicate the validity of WVD based on CSD in time–frequency analysis for bearing fault detection.

scholarly journals Fault Diagnosis of Rolling Bearings Based on Improved Kurtogram in Varying Speed Conditions

2019 ◽  
Vol 9 (6) ◽  
pp. 1157 ◽  
Author(s):  
Yong Ren ◽  
Wei Li ◽  
Bo Zhang ◽  
Zhencai Zhu ◽  
Fang Jiang
Keyword(s):  
Frequency Band ◽  
Band Pass Filter ◽  
Angular Domain ◽  
Optimal Frequency ◽  
Rolling Bearings ◽  
Pass Filter ◽  
Bearing Fault ◽  
Order Spectrum ◽  
Resonance Band ◽  
Speed Fluctuation

Envelope analysis is a widely used method in fault diagnoses of rolling bearings. An optimal narrowband chosen for the envelope demodulation is critical to obtain high detection accuracy. To select the narrowband, the fast kurtogram (FK), which computes the kurtosis of a set of filtered signals, is introduced to detect cyclic transients in a signal, and the zone with the maximum kurtosis is the optimal frequency band. However, the kurtosis value is affected by rotating frequencies and is sensitive to large random impulses which normally occur in industrial applications. These factors weaken the performance of the FK for extracting weak fault features. To overcome these limitations, a novel feature named Order Spectrum Correlated Kurtosis (OSCK) is proposed, replacing the kurtosis index in the FK, to construct an improved kurtogram called Fast Order Spectrum Correlated Kurtogram (FOSCK). A band-pass filter is used to extract the optimal frequency band signal corresponding to the maximum OSCK. The envelope of the filtered signal is calculated using the Hilbert transform, and a low-pass filter is employed to eliminate the trend terms of the envelope. Then, the non-stationary filtered envelope is converted in the time domain into the stationary envelope in the angular domain via Computed Order Tracking (COT) to remove the effects of the speed fluctuation. The order structure of the angular domain envelope signal can then be used to determine the type of fault by identifying its characteristic order. This method offers several merits, such as fine order spectrum resolution and robustness to both random shock and heavy noise. Additionally, it can accurately locate the bearing fault resonance band within a relatively large speed fluctuation. The effectiveness of the proposed method is verified by a number of simulations and experimental bearing fault signals. The results are compared with several existing methods; the proposed method outperforms others in accurate bearing fault feature extraction under varying speed conditions.

scholarly journals Optimum IMFs Selection Based Envelope Analysis of Bearing Fault Diagnosis in Plunger Pump

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Wenliao Du ◽  
Zhiyang Wang ◽  
Xiaoyun Gong ◽  
Liangwen Wang ◽  
Guofu Luo
Keyword(s):  
Fault Diagnosis ◽  
Promising Result ◽  
Rolling Element Bearing ◽  
Band Pass Filter ◽  
Intrinsic Mode Functions ◽  
Pass Filter ◽  
Plunger Pump ◽  
Filter Bandwidth ◽  
Envelope Analysis ◽  
Inner Race

As the plunger pump always works in a complicated environment and the hydraulic cycle has an intrinsic fluid-structure interaction character, the fault information is submerged in the noise and the disturbance impact signals. For the fault diagnosis of the bearings in plunger pump, an optimum intrinsic mode functions (IMFs) selection based envelope analysis was proposed. Firstly, the Wigner-Ville distribution was calculated for the acquired vibration signals, and the resonance frequency brought on by fault was obtained. Secondly, the empirical mode decomposition (EMD) was employed for the vibration signal, and the optimum IMFs and the filter bandwidth were selected according to the Wigner-Ville distribution. Finally, the envelope analysis was utilized for the selected IMFs filtered by the band pass filter, and the fault type was recognized by compared with the bearing character frequencies. For the two modes, inner race fault and compound fault in the inner race and roller of rolling element bearing in plunger pump, the experiments show that a promising result is achieved.

Feature Extraction Method for Rolling Bearing’s Week Fault Based on Kalman Filter and FSK

2014 ◽  
Vol 574 ◽  
pp. 684-689
Author(s):  
Zhi Chuan Liu ◽  
Li Wei Tang ◽  
Li Jun Cao
Keyword(s):  
Feature Extraction ◽  
Kalman Filter ◽  
Extraction Method ◽  
Model Parameters ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Feature Extraction Method ◽  
Spectral Kurtosis ◽  
Band Pass ◽  
Fault Feature Extraction

Aiming at the problem that traditional demodulated resonance technology has the deficiency of difficulty to choose the parameters of band-pass filter, Kalman filter technology and fast spectral kurtosis were combined for fault feature extraction of rolling bearing. AR model was firstly built with gearbox original vibration signals, and then model order was ascertained with AIC formula, and finally model parameters were calculated with least-squares method. The original signals were pretreated by Kalman filter. Fast spectral kurtosis (FSK) was used to choose parameters of the best band-pass filter, and finally fault diagnosis was achieved by the energy operator demodulation spectrum analysis of band-pass filtered signal. The analysis result of engineering signals indicated that fault feature extraction method based on Kalman filter and fast spectral kurtosis can primely provide a new feature extraction method for rolling bearing’s week fault.

Computational Parametric Analysis of the Mechanical Response of Structurally Varying Pacinian Corpuscles

2017 ◽  
Vol 139 (7) ◽  
Author(s):  
Julia C. Quindlen ◽  
Burak Güçlü ◽  
Eric A. Schepis ◽  
Victor H. Barocas
Keyword(s):  
Mechanical Response ◽  
Structural Parameters ◽  
Peak Frequency ◽  
Lamellar Thickness ◽  
Fluid Viscosity ◽  
Band Pass Filter ◽  
Peak Strain ◽  
Pass Filter ◽  
The Impact ◽  
The Relationship

The Pacinian corpuscle (PC) is a cutaneous mechanoreceptor that senses low-amplitude, high-frequency vibrations. The PC contains a nerve fiber surrounded by alternating layers of solid lamellae and interlamellar fluid, and this structure is hypothesized to contribute to the PC's role as a band-pass filter for vibrations. In this study, we sought to evaluate the relationship between the PC's material and geometric parameters and its response to vibration. We used a spherical finite element mechanical model based on shell theory and lubrication theory to model the PC's outer core. Specifically, we analyzed the effect of the following structural properties on the PC's frequency sensitivity: lamellar modulus (E), lamellar thickness (h), fluid viscosity (μ), PC outer radius (Ro), and number of lamellae (N). The frequency of peak strain amplification (henceforth “peak frequency”) and frequency range over which strain amplification occurred (henceforth “bandwidth”) increased with lamellar modulus or lamellar thickness and decreased with an increase in fluid viscosity or radius. All five structural parameters were combined into expressions for the relationship between the parameters and peak frequency, ωpeak=1.605×10−6N3.475(Eh/μRo), or bandwidth, B=1.747×10−6N3.951(Eh/μRo). Although further work is needed to understand how mechanical variability contributes to functional variability in PCs and how factors such as PC eccentricity also affect PC behavior, this study provides two simple expressions that can be used to predict the impact of structural or material changes with aging or disease on the frequency response of the PC.

scholarly journals Band pass filter design against interrupted‐sampling repeater jamming based on time‐frequency analysis

2019 ◽  
Vol 13 (10) ◽  
pp. 1646-1654 ◽  
Author(s):  
Jian Chen ◽  
Wenzhen Wu ◽  
Shiyou Xu ◽  
Zengping Chen ◽  
Jiangwei Zou
Keyword(s):  
Frequency Analysis ◽  
Filter Design ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Band Pass
Sign in / Sign up

Export Citation Format

Share Document