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Fault Detection of Roller-Bearings Using Signal Processing and Optimization Algorithms

Dae-Ho Kwak, Dong-Han Lee, Jong-Hyo Ahn, Bong-Hwan Koh
2013 Sensors  
This study presents a fault detection of roller bearings through signal processing and optimization techniques.  ...  The weights of each IMF can be optimized through the genetic algorithm, to enhance the sensitivity of kurtosis on damaged bearing signals.  ...  Acknowledgments This work was supported by the Basic Science Research Program, through the National Research Foundation of Korea (NRF), funded by the Ministry of Education, Science and Technology (2012R1A1A2003787  ... 
doi:10.3390/s140100283 pmid:24368701 pmcid:PMC3926558 fatcat:hydxzaonczdila422xemolnkrm

Detecting and Learning Unknown Fault States by Automatically Finding the Optimal Number of Clusters for Online Bearing Fault Diagnosis

Md Rashedul Islam, Young-Hun Kim, Jae-Young Kim, Jong-Myon Kim
2019 Applied Sciences  
Experimental results present that the MPDFCDF cluster evaluation method can detect the optimal number of fault clusters, and the proposed online diagnosis model can detect newly emerged faults and update  ...  The proposed online fault diagnosis system detects new fault modes from unknown signals using k-means clustering with the help of proposed MPDFCDF cluster evaluation method.  ...  Author Contributions: All authors contributed equally to the conception of the idea, as well as implementing and analyzing the experimental results, and writing the manuscript.  ... 
doi:10.3390/app9112326 fatcat:swtuxzc6ezfv3lunifpl6knjbq


2022 Journal of Science and Technology - IUH  
In this paper, the roller bearing vibration signals were used to evaluate the proposed method.  ...  The experimental results showed that the superior performance compared to other SVM parameter optimization techniques and successfully recognized different fault types of roller bearing during its operation  ...  In order to demonstrate the superior performance of AeDE-SVM, roller bearing vibration signals were used for detecting four different fault types.  ... 
doi:10.46242/jstiuh.v36i03.4077 fatcat:mgdjdcbkljglrk575uit3wuzqm

A Sparsity-Promoted Method Based on Majorization-Minimization for Weak Fault Feature Enhancement

Bangyue Ren, Yansong Hao, Huaqing Wang, Liuyang Song, Gang Tang, Hongfang Yuan
2018 Sensors  
Simulated and experimental signals including bearings and gearboxes are employed to validate the effectiveness of the proposed method.  ...  In addition, comparisons are made to prove that the proposed method outperforms the traditional MM algorithm in terms of detection results and efficiency.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s18041003 pmid:29597280 pmcid:PMC5948639 fatcat:6dwqxwv22jfttjr3ij3t23evcm

Complex Morlet wavelet design with global parameter optimization for diagnosis of industrial manufacturing faults of tapered roller bearing in noisycondition

Deák Krisztián Deák, Kocsis Imre Kocsis
2019 Diagnostyka  
Genetic algorithm is applied to optimize the center frequency and the bandwidth of the designed wavelet.  ...  In this article we are focusing on industrial grinding faults on the outer ring of tapered roller bearings.  ...  Wavelet transform was used for detecting the transients in the spectrum because it had the capability to detect the sharp edges caused by the roller and fault interaction during the rotation of the bearing  ... 
doi:10.29354/diag/109223 fatcat:u7226ulqwrfwzanfa32jqn4zvy

Discriminant Feature Distribution Analysis-Based Hybrid Feature Selection for Online Bearing Fault Diagnosis in Induction Motors

Rashedul Islam, Sheraz Ali Khan, Jong-myon Kim
2016 Journal of Sensors  
The effectiveness of the proposed feature selection scheme is verified through an online process that diagnoses faults in an unknown AE fault signal by extracting only the selected features and using thek-NN  ...  The feature selection determines the optimal features for different types and sizes of single and combined bearing faults under different speed conditions.  ...  Early detection and reliable diagnosis of bearing faults can help in avoiding unexpected machine failure and process shutdown.  ... 
doi:10.1155/2016/7145715 fatcat:b55ijziytvawvmwclab636zwru

Incipient Fault Diagnosis of Roller Bearing Using Optimized Wavelet Transform Based Multi-Speed Vibration Signatures

Zhiqiang Huo, Yu Zhang, Pierre Francq, Lei Shu, Jianfeng Huang
2017 IEEE Access  
Condition monitoring and incipient fault diagnosis of rolling bearing is of great importance to detect failures and ensure reliable operations in rotating machinery.  ...  In this paper, a new multi-speed fault diagnostic approach is presented by using self-adaptive wavelet transform components generated from bearing vibration signals.  ...  Optimized IMCWT is obtained by using PSO and quasi-Newton minimization techniques, and then used to decompose vibration signals obtained from roller bearings.  ... 
doi:10.1109/access.2017.2661967 fatcat:ectjmtcv4zd6fgyjawhgovg7aq

Fault Detection and Behavior Analysis of Wheelset Bearing Using Adaptive Convolutional Sparse Coding Technique Combined with Bandwidth Optimization

Liu He, Cai Yi, Jianhui Lin, Andy C.C. Tan, Dr Mahdi Mohammadpour
2020 Shock and Vibration  
Simulated signals and bench tests are used to verify the effectiveness of the proposed method.  ...  Union of convolutional dictionary learning algorithm (UC-DLA) is an efficient algorithm in CSCT-DLA. In this paper, UC-DLA is introduced and improved for wheelset bearing fault detection.  ...  Figure 24 shows the processing results of roller fault signal with AUC-DLA-OB. e optimized filter effectively avoids the interference signal and detects the resonance frequency band of the fault, as shown  ... 
doi:10.1155/2020/8879732 fatcat:mkncc67ykvhgnaamrnbn2sb74a

A Novel Fault Diagnosis Method for Rolling Element Bearings Using Kernel Independent Component Analysis and Genetic Algorithm Optimized RBF Network

Hang Dai, Jingshi He
2013 Research Journal of Applied Sciences Engineering and Technology  
This study focuses on the defect detection of rolling element bearings using a novel method.  ...  Then a RBF network was used to classify the fault patterns. To improve the fault identification, the Genetic Algorithm (GA) was adopted to optimize the parameters of the RBF network.  ...  Hence, it is reasonable to analyze the bearing vibration using multiply sensors and process the signals at one time.  ... 
doi:10.19026/rjaset.6.4138 fatcat:chni4p23rjeg7lbtpaxhzub2lu

Application of LCD-SVD Technique and CRO-SVM Method to Fault Diagnosis for Roller Bearing

Songrong Luo, Junsheng Cheng, HungLinh Ao
2015 Shock and Vibration  
Targeting the nonlinear and nonstationary characteristics of vibration signal from fault roller bearing and scarcity of fault samples, a novel method is presented and applied to roller bearing fault diagnosis  ...  Firstly, the nonlinear and nonstationary vibration signal produced by local faults of roller bearing is decomposed into intrinsic scale components (ISCs) by using local characteristic-scale decomposition  ...  Therefore, using CRO algorithm to optimize the parameters of SVM classifier, CRO-SVM method is presented to fulfil pattern recognition in roller bearing fault diagnosis.  ... 
doi:10.1155/2015/847802 fatcat:thixptmq6ngktkxwdvy6xfnnli

Improved LMD, Permutation Entropy and Optimized K-Means to Fault Diagnosis for Roller Bearings

Zongli Shi, Wanqing Song, Saied Taheri
2016 Entropy  
A novel bearing vibration signal fault feature extraction and recognition method based on the improved local mean decomposition (LMD), permutation entropy (PE) and the optimized K-means clustering algorithm  ...  For roller bearing, vibration signals of different fault locations and different degrees of failures will show varying complexity, so they will have various PFs through LMD.  ...  Acknowledgments: This project is supported by Shanghai Nature Science Foundation of China (Grant No. 14ZR1418500 and 14ZR1418400).  ... 
doi:10.3390/e18030070 fatcat:ein2aohpubbyfofliazbmoss5q

A Rolling Bearing Fault Diagnosis Method Based on Variational Mode Decomposition and an Improved Kernel Extreme Learning Machine

Ke Li, Lei Su, Jingjing Wu, Huaqing Wang, Peng Chen
2017 Applied Sciences  
A bearing diagnosis model is created via a KELM; the KELM parameters are optimized using the particle swarm optimization (PSO) algorithm to obtain a KELM diagnosis model with optimal parameters.  ...  A fault signal is decomposed via VMD to obtain the intrinsic mode function (IMF) components, and the approximate entropy (ApEn) of the IMF component containing the main fault information is calculated.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app7101004 fatcat:d65inpky3zh6bdesyts524jco4

Comparison between Artificial Neural Network and Support Vector Method for a Fault Diagnostics in Rolling Element Bearings

J.P. Patel, S.H. Upadhyay
2016 Procedia Engineering  
Therefore condition monitoring of bearing is very important. In this paper, artificial intelligence techniques are used to predict and analyses the bearing faults.  ...  Experiments were carried out on rolling bearing having localized defects on the various bearing components for wide range of speed and vibration signals were stored.  ...  There are two important stages to implement the fault diagnosis process: the first is signal processing, for feature extraction and noise diminishing, and the second one consists of signal classification  ... 
doi:10.1016/j.proeng.2016.05.148 fatcat:fvb3hpshrvcerbquv7gkcg5beq

Fault Diagnosis for Rolling Bearings Using Optimized Variational Mode Decomposition and Resonance Demodulation

Chunguang Zhang, Wang Yao, Wu Deng
2020 Entropy  
It is difficult to extract the fault signal features of locomotive rolling bearings and the accuracy of fault diagnosis is low.  ...  The resonance demodulation technology is used to decompose the reconstructed vibration signal in order to obtain the envelope spectrum, and the fault frequency of locomotive rolling bearings is effectively  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/e22070739 pmid:33286510 pmcid:PMC7517282 fatcat:filwkflsevat7k2qp5mtkev3ly

Underdetermined Source Separation of Bearing Faults Based on Optimized Intrinsic Characteristic-Scale Decomposition and Local Non-Negative Matrix Factorization

Yansong Hao, Liuyang Song, Mengyang Wang, Lingli Cui, Huaqing Wang
2019 IEEE Access  
Since roller bearing is one of the most vulnerable components, bearing faults usually occur in an unprepared situation with multiple faults, and the quantity of sensors is limited in the real-time working  ...  Ultimately, envelope analysis is utilized to detect the source signal feature. Both simulated and experimental vibration signals are used to verify the effectiveness of the proposed approach.  ...  The principle of NMF algorithm is shown in Fig. 2 . Since the NMF algorithm has been proposed, a variety of cost function to optimize the NMF algorithm is used.  ... 
doi:10.1109/access.2019.2892559 fatcat:z72kysvguvalrhw2yquewvfv2u
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