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

Sensors ◽  
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.

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

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
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.

scholarly journals A Fault Diagnosis Method of Rolling Bearing Integrated with Cooperative Energy Feature Extraction and Improved Least-Squares Support Vector Machine

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zhang Xu ◽  
Darong Huang ◽  
Tang Min ◽  
Yunhui Ou
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Working Conditions ◽  
Rolling Bearing ◽  
Support Vector ◽  
Swarm Optimization ◽  
Energy Characteristics ◽  
Wavelet Energy ◽  
Energy Feature

To solve the problem that the bearing fault of variable working conditions is challenging to identify and classify in the industrial field, this paper proposes a new method based on optimization of multidimension fault energy characteristics and integrates with an improved least-squares support vector machine (LSSVM). First, because the traditional wavelet energy feature is difficult to effectively reflect the characteristics of rolling bearing under different working conditions, based on analyzing the wavelet energy feature extraction in detail, a collaborative method of multidimension fault energy feature extraction combined with the method of Transfer Component Analysis (TCA) is constructed, which improves the discrimination between the different features and the compactness between the same features of rolling bearing faults. Then, for solving the problem of the local optimal of particle swarm optimization (PSO) in fault diagnosis and recognition of rolling bearing, an improved LSSVM based on particle swarm optimization and wavelet mutation optimization is established to realize the collaborative optimization and adjustment of LSSVM dynamic parameters. Based on the improved LSSVM and optimization of multidimensional energy characteristics, a new method for fault diagnosis of rolling bearing is designed. Finally, the simulation and analysis of the proposed algorithm are verified by the experimental data of different working conditions. The experimental results show that this method can effectively extract the multidimensional fault characteristics under variable working conditions and has a high fault recognition rate.

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

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.

scholarly journals A Novel Cuckoo Search Optimized Deep Auto-Encoder Network-Based Fault Diagnosis Method for Rolling Bearing

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jinyu Tong ◽  
Jin Luo ◽  
Haiyang Pan ◽  
Jinde Zheng ◽  
Qing Zhang
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Search Algorithm ◽  
Feature Learning ◽  
Cuckoo Search ◽  
Rolling Bearing ◽  
Cuckoo Search Algorithm ◽  
Learning Ability ◽  
Penalty Term ◽  
Experimental Data Analysis

To enhance the performance of deep auto-encoder (AE) under complex working conditions, a novel deep auto-encoder network method for rolling bearing fault diagnosis is proposed in this paper. First, multiscale analysis is adopted to extract the multiscale features from the raw vibration signals of rolling bearing. Second, the sparse penalty term and contractive penalty term are used simultaneously to regularize the loss function of auto-encoder to enhance the feature learning ability of networks. Finally, the cuckoo search algorithm (CS) is used to find the optimal hyperparameters automatically. The proposed method is applied to the experimental data analysis. The results indicate that the proposed method could more effectively distinguish fault categories and severities of rolling bearings under different working conditions than other methods.

A Rolling Bear Fault Diagnosis Method Based on Improved LMD and Order Analysis

2014 ◽  
Vol 530-531 ◽  
pp. 256-260
Author(s):  
Hui Juan Yuan ◽  
Jia Qi ◽  
Hong Mei Li ◽  
Jun Zhong Li ◽  
Xue Jiang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Amplitude Modulation ◽  
Frequency Modulation ◽  
Rolling Bearing ◽  
Analysis Method ◽  
Vibration Signals ◽  
Order Analysis ◽  
Diagnosis Method ◽  
Bearing Vibration

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.

Research on the Gearbox Fault Signal Relation to the Sensitivity of Variable Working Conditions

2014 ◽  
Vol 494-495 ◽  
pp. 921-924 ◽  
Author(s):  
Guo Quan Ren ◽  
Wen Chen Zhang ◽  
Ben Li
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Fault Model ◽  
Diagnostic Purpose ◽  
Variable Speed ◽  
Optimal Test ◽  
Vibration Signals ◽  
Design Load ◽  
System Vibration ◽  
Load Conditions

Established the gearbox fault model and simulated it at different load conditions view of the problem with gearbox experimental technique research under the variable speed and load conditions. The law between the changes of rotational speed and load with the system vibration signals was investigated and the corresponding optimal test fault condition was given. Based on this, validated the feasibility on fault diagnosis by increasing (decreasing) speed signal and found that the different load will have a different influence on the different fault signal. The different diagnostic purpose should be considered to design load conditions.

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

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.

GEAR FAULT DIAGNOSIS IN TIME DOMAINS VIA BAYESIAN NETWORKS

2013 ◽  
Vol 37 (3) ◽  
pp. 665-672 ◽  
Author(s):  
Chun-Chieh Wang ◽  
Yuan Kang ◽  
Chin-Chi Liao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bayesian Networks ◽  
Rolling Bearing ◽  
Vibration Signals ◽  
The Neural Network ◽  
Training Samples ◽  
Time Domains ◽  
Gear Fault ◽  
The Time Domain

In gear or rolling bearing systems, it is difficult to extract symptoms from vibration signals where shock vibration signals are present. However, the neural network method cannot provide satisfactory diagnosis results without adequate training samples. Bayesian networks provide an effective approach for fault diagnosis in cases given uncertain and incomplete information. In this study, the statistical factors of vibration signals in the time-domain were used and the diagnosis results by using Bayesian networks were superior to other neural network methods.

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