Application of Pattern Filter Method on Fault Diagnosis of Rolling Bearing Vibration Signals

This document explains and demonstrates how to predict the fault point of rolling bear. Rolling bearing vibration signals are decomposed by the LMD method to get several single components including amplitude modulation and frequency modulation signals. Combing the order analysis method can get the fault point of rolling bear.

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Improved VMD‐KFCM algorithm for the fault diagnosis of rolling bearing vibration signals

IET Signal Processing ◽

10.1049/sil2.12026 ◽

2021 ◽

Author(s):

Yong Chang ◽

Guangqing Bao ◽

Sikai Cheng ◽

Ting He ◽

Qiaoling Yang

Keyword(s):

Fault Diagnosis ◽

Rolling Bearing ◽

Vibration Signals ◽

Bearing Vibration

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Fault Diagnosis of Rolling Bearing Based on a Novel Adaptive High-Order Local Projection Denoising Method

Complexity ◽

10.1155/2018/3049318 ◽

2018 ◽

Vol 2018 ◽

pp. 1-15 ◽

Cited By ~ 5

Author(s):

Rui Yuan ◽

Yong Lv ◽

Gangbing Song

Keyword(s):

Fault Diagnosis ◽

Numerical Simulations ◽

Rolling Bearing ◽

High Order ◽

Rolling Bearings ◽

Theoretical Derivation ◽

Denoising Method ◽

Vibration Signals ◽

Local Projection ◽

Ring Fault

Rolling bearings are vital components in rotary machinery, and their operating condition affects the entire mechanical systems. As one of the most important denoising methods for nonlinear systems, local projection (LP) denoising method can be used to reduce noise effectively. Afterwards, high-order polynomials are utilized to estimate the centroid of the neighborhood to better preserve complete geometry of attractors; thus, high-order local projection (HLP) can improve noise reduction performance. This paper proposed an adaptive high-order local projection (AHLP) denoising method in the field of fault diagnosis of rolling bearings to deal with different kinds of vibration signals of faulty rolling bearings. Optimal orders can be selected corresponding to vibration signals of outer ring fault (ORF) and inner ring fault (IRF) rolling bearings, because they have different nonlinear geometric structures. The vibration signal model of faulty rolling bearing is adopted in numerical simulations, and the characteristic frequencies of simulated signals can be well extracted by the proposed method. Furthermore, two kinds of experimental data have been processed in application researches, and fault frequencies of ORF and IRF rolling bearings can be both clearly extracted by the proposed method. The theoretical derivation, numerical simulations, and application research can indicate that the proposed novel approach is promising in the field of fault diagnosis of rolling bearing.

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Refinement Envelope Analysis for Small Failures of Rolling Bearing

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.383-390.2622 ◽

2011 ◽

Vol 383-390 ◽

pp. 2622-2627

Author(s):

Shu Shang Zhao ◽

Juan Juan Pan

Keyword(s):

Fault Diagnosis ◽

Hilbert Transform ◽

Vibration Spectrum ◽

Rolling Bearing ◽

Bearing Failure ◽

Wavelet Filter ◽

Bearing Defect ◽

Small Fault ◽

Bearing Vibration ◽

Envelope Spectrum

In the rotating machinery, rolling bearing is used widespread in many places. Due to various reasons, there is great dispersion in the life of bearing. Therefore, it is very important to have fault diagnosis of rolling bearing, especially the small fault diagnosis of rolling bearing. According to the characteristics of rolling bearing defect signals and the features integrated with wavelet transform, Hilbert transform and envelope spectrum detailed analysis, this text proposed a method to judge the bearing failure. At first, bearing vibration signals are reconstructed from wavelet filter and envelope signals are obtained by Hilbert transform and then vibration spectrum is obtained from the refining envelope spectrum. Bearing failure is judged from the refining frequency spectrum. Bearing failure is also estimated by experiment to verify the correctness of theoretical analysis.

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Multiscale convolutional neural network and decision fusion for rolling bearing fault diagnosis

Industrial Lubrication and Tribology ◽

10.1108/ilt-09-2020-0335 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Defeng Lv ◽

Huawei Wang ◽

Changchang Che

Keyword(s):

Neural Network ◽

Time Series ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Rolling Bearing ◽

Decision Fusion ◽

Fusion Model ◽

Vibration Signals ◽

Content Type ◽

Proposed Model

Purpose The purpose of this study is to achieve an accurate intelligent fault diagnosis of rolling bearing. Design/methodology/approach To extract deep features of the original vibration signal and improve the generalization ability and robustness of the fault diagnosis model, this paper proposes a fault diagnosis method of rolling bearing based on multiscale convolutional neural network (MCNN) and decision fusion. The original vibration signals are normalized and matrixed to form grayscale image samples. In addition, multiscale samples can be achieved by convoluting these samples with different convolution kernels. Subsequently, MCNN is constructed for fault diagnosis. The results of MCNN are put into a data fusion model to obtain comprehensive fault diagnosis results. Findings The bearing data sets with multiple multivariate time series are used to testify the effectiveness of the proposed method. The proposed model can achieve 99.8% accuracy of fault diagnosis. Based on MCNN and decision fusion, the accuracy can be improved by 0.7%–3.4% compared with other models. Originality/value The proposed model can extract deep general features of vibration signals by MCNN and obtained robust fault diagnosis results based on the decision fusion model. For a long time series of vibration signals with noise, the proposed model can still achieve accurate fault diagnosis.

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A Fault Diagnostic Scheme Based on Capsule Network for Rolling Bearing under Different Rotational Speeds

Sensors ◽

10.3390/s20071841 ◽

2020 ◽

Vol 20 (7) ◽

pp. 1841

Author(s):

Linjie Li ◽

Mian Zhang ◽

Kesheng Wang

Keyword(s):

Fault Diagnosis ◽

Working Conditions ◽

Rolling Bearing ◽

Extraction Ability ◽

Position Information ◽

Vibration Signals ◽

Test Dataset ◽

Horizontal Vibration ◽

Automatic Feature Extraction ◽

Real Practice

Deep learning-based intelligent fault diagnosis methods have attracted increasing attention for their automatic feature extraction ability. However, existing works are usually under the assumption that the training and test dataset share similar distributions, which unfortunately always violates real practice due to the variety of working conditions. In this paper, an end-to-end scheme of joint use of two-direction signals and capsule network (CN) is proposed for fault diagnosis of rolling bearing. With the help of the superior ability of CN in capturing the spatial position information between features, more valuable information can be mined. Aiming to eliminate the influence of different rotational speeds, vertical and horizontal vibration signals are fused as the input to CN, so that invariant features can be extracted automatically from the raw signals. The effectiveness of the proposed method is verified by experimental data of rolling bearing under different rotational speeds and compared with a deep convolutional neural network (DCNN). The results demonstrate that the proposed scheme is able to recognize the fault types of rolling bearing under scenarios of different rotational speeds.

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The Bearing Vibration Signal Collecting System Based on STM32F103C8T6

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.971-973.1376 ◽

2014 ◽

Vol 971-973 ◽

pp. 1376-1379

Author(s):

Zhong Hu Yuan ◽

Man Yang Xu ◽

Xiao Xuan Qi

Keyword(s):

Fault Diagnosis ◽

Vibration Signal ◽

Rolling Bearing ◽

Bearing Fault Diagnosis ◽

Communication Module ◽

Bearing Vibration ◽

Signal Conditioning Circuit ◽

Collecting System ◽

Conditioning Circuit ◽

New System

Vibration signal collecting is an important step in rolling bearing fault diagnosis process. The collected signal can exhibit effectiveness of the fault depend on the signal collecting system. Combine the attribute of the rolling bearing, a new signal collecting system base on the STM32F103C8T6 is designed in this paper. The new system is made of supply circuit, signal conditioning circuit, AD conversion circuit and communication module.

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Rolling element bearing fault diagnosis based on multi-scale global fuzzy entropy, multiple class feature selection and support vector machine

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331219844555 ◽

2019 ◽

Vol 41 (14) ◽

pp. 4013-4022 ◽

Cited By ~ 2

Author(s):

Keheng Zhu ◽

Liang Chen ◽

Xiong Hu

Keyword(s):

Fault Diagnosis ◽

Fuzzy Entropy ◽

Rolling Element Bearing ◽

Support Vector ◽

Vibration Signals ◽

Bearing Fault Diagnosis ◽

Multi Scale ◽

Rolling Element ◽

Element Bearing ◽

Bearing Vibration

Multi-scale fuzzy entropy (MFE) is a recently developed non-linear dynamic parameter for measuring the complexity of vibration signals of rolling element bearing over different scales. However, the calculation of fuzzy entropy (FuzzyEn) in each scale ignores the sequence’s global characteristics while the bearing vibration signals’ global fluctuation may vary as the bearing runs under different states. Therefore, in this paper, the multi-scale global fuzzy entropy (MGFE) method is put forward for extracting the fault features from the bearing vibration signals. After the feature extraction, multiple class feature selection (MCFS) method is introduced to select the most informative features from the high-dimensional feature vector. Then, a new rolling element bearing fault diagnosis approach is proposed based on MGFE, MCFS and support vector machine (SVM). The experimental results indicate that the proposed approach can effectively fulfill the fault diagnosis of rolling element bearing and has good classification performance.

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A NewFault Detection and Diagnosis Method Based on Wigner-Ville Spectrum Entropy for the Rolling Bearing

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.197.346 ◽

2012 ◽

Vol 197 ◽

pp. 346-350 ◽

Cited By ~ 3

Author(s):

Ping Xie ◽

Yu Xin Yang ◽

Guo Qian Jiang ◽

Yi Hao Du ◽

Xiao Li Li

Keyword(s):

Fault Detection ◽

Rolling Bearing ◽

Work Conditions ◽

Fault Detection And Diagnosis ◽

Vibration Signals ◽

Time Frequency ◽

Diagnosis Method ◽

Bearing Vibration ◽

Detection And Diagnosis ◽

Critical Components

The rolling bearings are one of the most critical components in rotary machinery. To prevent unexpected bearing failure, it is crucial to develop the effective fault detection and diagnosis techniques to realize equipment’s near-zero downtime and maximum productivity. In this paper, a new fault detection and diagnosis method based on Wigner-Ville spectrum entropy (WVSE) is proposed. First, the local mean decomposition (LMD) and the Wigner-Ville distribution (WVD) are combined to develop a new feature extraction approach to extract the fault features in time-frequency domain of the bearing vibration signals. Second, the concept of the Shannon entropy is integrated into the WVD to define the Wigner-Ville spectrum entropy to quantify the energy variation in time-frequency distribution under different work conditions. The research results from the bearing vibration signals demonstrate that the proposed method based on WVSE can identify different fault patterns more accurately and effectively comparing with other methods based on singular spectrum entropy (SSE) or power spectrum entropy (PSE).

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A New Method of Fault Feature Extraction Based on Hierarchical Dispersion Entropy

Shock and Vibration ◽

10.1155/2021/8824901 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Peng Chen ◽

Xiaoqiang Zhao ◽

HongMei Jiang

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

White Noise ◽

Gaussian White Noise ◽

Rolling Bearing ◽

K Nearest Neighbor ◽

Vibration Signals ◽

Diagnosis Method ◽

Feature Information ◽

Fault Feature Extraction

In the process of fault feature extraction of rolling bearing, the feature information is difficult to be extracted fully. A novel method of fault feature extraction called hierarchical dispersion entropy is proposed in this paper. In this method, the vibration signals firstly are decomposed hierarchically. Secondly, dispersion entropies of different nodes are calculated. Hierarchical dispersion entropy can realize the comprehensive feature extraction of the high- and low-frequency band information of vibration signals and overcome the problems that dispersion entropy and multiscale dispersion entropy are insufficient to extract the fault feature information of vibration signals. The feasibility of hierarchical dispersion entropy is obtained by analyzing the hierarchical dispersion entropy of Gaussian white noise and compared with the multiscale dispersion entropy of Gaussian white noise. Meanwhile, a fault diagnosis method for rolling bearings based on hierarchical dispersion entropy and k-nearest neighbor (KNN) classifier is developed. Finally, the superiority of the proposed fault diagnosis method is verified in the realization of fault diagnosis of the rolling bearing in different positions and different degrees of damage.

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