scholarly journals The Hybrid KICA-GDA-LSSVM Method Research on Rolling Bearing Fault Feature Extraction and Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Jiyong Li ◽  
Shunming Li ◽  
Xiaohong Chen ◽  
Lili Wang
Keyword(s):  
Feature Extraction ◽  
High Speed ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Least Square ◽  
Support Vector ◽  
Rolling Element Bearings ◽  
Kernel Independent Component Analysis ◽  
Rolling Element ◽  
Independent Features

Rolling element bearings are widely used in high-speed rotating machinery; thus proper monitoring and fault diagnosis procedure to avoid major machine failures is necessary. As feature extraction and classification based on vibration signals are important in condition monitoring technique, and superfluous features may degrade the classification performance, it is needed to extract independent features, so LSSVM (least square support vector machine) based on hybrid KICA-GDA (kernel independent component analysis-generalized discriminate analysis) is presented in this study. A new method named sensitive subband feature set design (SSFD) based on wavelet packet is also presented; using proposed variance differential spectrum method, the sensitive subbands are selected. Firstly, independent features are obtained by KICA; the feature redundancy is reduced. Secondly, feature dimension is reduced by GDA. Finally, the projected feature is classified by LSSVM. The whole paper aims to classify the feature vectors extracted from the time series and magnitude of spectral analysis and to discriminate the state of the rolling element bearings by virtue of multiclass LSSVM. Experimental results from two different fault-seeded bearing tests show good performance of the proposed method.

scholarly journals Fault Feature Extraction and Enhancement of Rolling Element Bearings Based on Maximum Correlated Kurtosis Deconvolution and Improved Empirical Wavelet Transform

2019 ◽  
Vol 9 (9) ◽  
pp. 1876 ◽  
Author(s):  
Zheng Li ◽  
Anbo Ming ◽  
Wei Zhang ◽  
Tao Liu ◽  
Fulei Chu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Wavelet Packet ◽  
Amplitude Spectrum ◽  
Energy Operator ◽  
Segmentation Result ◽  
Rolling Element Bearings ◽  
Empirical Wavelet Transform ◽  
Rolling Element ◽  
Fault Feature Extraction

In order to extract and enhance the weak fault feature of rolling element bearings in strong noise conditions, the Empirical Wavelet Transform (EWT) is improved and a novel fault feature extraction and enhancement method is proposed by combining the Maximum Correlated Kurtosis Deconvolution (MCKD) and improved EWT method. At first, the MCKD method is conducted to de-noise the signal by eliminating the non-impact components. Then, the Fourier spectrum is segmented by local maxima or minima in the envelope of the amplitude spectrum with a pre-set threshold based on the noise level. By building up the wavelet filter banks based on the spectrum segmentation result, the signal is adaptively decomposed into several sub-signals. Finally, by choosing the most meaningful sub-signal with the maximum kurtosis, the fault feature can be extracted in the squared envelope spectrum and teager energy operator spectrum of the chosen component. Both simulations and experiments are performed to validate the effectiveness of the proposed method. It is shown that the spectrum segmentation result of improved EWT is more reasonable than the traditional EWT in strong noise conditions. Furthermore, compared with commonly used methods, such as the Fast Kurtogram (FK) and the Optimal Wavelet Packet Transform (OWPT) method, the proposed method is more effective in the fault feature extraction and enhancement of rolling element bearings.

The Error Caused by Order Analysis for Rolling Bearing Fault Signal

2013 ◽  
Vol 819 ◽  
pp. 254-258 ◽  
Author(s):  
Wei Dong Cheng ◽  
Robert X. Gao ◽  
Jin Jiang Wang ◽  
Tian Yang Wang ◽  
Wei Gang Wen ◽  
...  
Keyword(s):  
High Speed ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rate Of Change ◽  
Rolling Element Bearings ◽  
Factors Affecting ◽  
Order Analysis ◽  
Rolling Element ◽  
Angular Interval ◽  
The Time Domain

Defect diagnosis of rolling element bearings operating under time-varying rotational speeds entails order tracking and analysis techniques that convert a vibration signal from the time domain to the angle domain to eliminate the effect of speed variations. When a signal is resampled at a constant angular increment, the amount of data padded into each data segment will vary, depending on the rate of change in the rotational speeds. This leads to changes in the distance between the adjacent impulse peaks, and consequently, the result of order analysis. This paper presents a quantitative analysis of key factors affecting the accuracy of order analysis on rolling element bearings under variable speeds. An analytical model is established and simulated. The effects of speed variation, instantaneous speed, angular interval between impulses, and the rising time of impulse are specified. It is concluded that the results of order analysis will be smaller as the rotational speed increases, and becomes larger when the speed decreases. Furthermore, the error is larger under low speeds than high speed.

scholarly journals Wavelet Packet Transform-Assisted Least Squares Support Vector Machine for Gear Wear Degree Diagnosis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hongmin Wang ◽  
Liang Chan
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
High Speed ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Wavelet Packet Transform ◽  
Least Square ◽  
Signal Spectrum ◽  
Support Vector ◽  
Particle Swarm Algorithm

Wear degree detection of gears is an effective way to prevent faults. However, due to the interference of high-speed meshing vibration and environmental noise, the weak vibration signal generated by the gear is easily covered by the noise, which makes it difficult to detect the degree of wear. To address this issue, this paper proposes a novel gear wear degree diagnosis method based on local weighted scatter smoothing method (LOWESS), wavelet packet transform (WPT), and least square support vector machine (APSO-LSSVM) optimized by adaptive particle swarm algorithm. According to the low signal-to-noise ratio characteristic of gear vibration signal, LOWESS is first used to preprocess the signal spectrum. Then, the characteristic parameters used to characterize gear wear are extracted from different decomposition depths by WPT and, finally, combined with APSO-SVM to diagnose the degree of gear wear. Compared with the basic least squares support vector machine, the improved method has better performance in sample classification. The experimental results show that the method in this paper can effectively reduce the diagnosis error caused by background noise, and the diagnosis accuracy reaches 98.33%, which can provide a solution for the health status monitoring of gears.

A Combination of WKNN to Fault Diagnosis of Rolling Element Bearings

2009 ◽  
Vol 131 (6) ◽  
Author(s):  
Yaguo Lei ◽  
Zhengjia He ◽  
Yanyang Zi
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Nearest Neighbor ◽  
Wavelet Packet ◽  
Rolling Element Bearings ◽  
K Nearest Neighbor ◽  
Vibration Signals ◽  
Multiple Classifiers ◽  
Operation Conditions ◽  
Rolling Element

This paper presents a new method for fault diagnosis of rolling element bearings, which is developed based on a combination of weighted K nearest neighbor (WKNN) classifiers. This method uses wavelet packet transform based on the lifting scheme to preprocess the vibration signals before feature extraction. Time- and frequency-domain features are all extracted to represent the operation conditions of the bearings totally. Sensitive features are selected after feature extraction. And then, multiple classifiers based on WKNN are combined to overcome the two disadvantages of KNN and therefore it may enhance the classification accuracy. The experimental results of the proposed method to fault diagnosis of the rolling element bearings show that this method enables the detection of abnormalities in bearings and at the same time identification of fault categories and levels.

Hot Strip Tail Flick Recognition Based on Morphological Pattern Spectrum

2012 ◽  
Vol 572 ◽  
pp. 25-30
Author(s):  
Li Jing Han ◽  
Jian Hong Yang ◽  
Min Lin ◽  
Jin Wu Xu
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Steel Strip ◽  
Wavelet Packet ◽  
Support Vector ◽  
Tail Flick ◽  
Morphological Pattern ◽  
Feature Extraction Method ◽  
Pattern Spectrum ◽  
Hot Strip

Hot strip tail flick is an abnormal production phenomenon, which brings many damages. To recognize the tail flick signals from all throwing steel strip signals, a feature extraction method based on morphological pattern spectrum is proposed in this paper. The area between signal curves after multiscale opening operation and the horizontal axis is computed as the pattern spectrum value and it reflects the geometric information differences. Then, support vector machine is used as the classifier. Experimental results show that the total correct rate based on pattern spectrum feature reached 96.5%. Compared with wavelet packet energy feature, the total correct rate is 92.1%. So, the feasibility and availability of this new feature extraction method are verified.

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