rolling bearing fault diagnosis based on improved

2020-5-15the fault features from low SNR signals McDonald [17] proposed a maximum correlated Kurtosis deconvolution method e experimental results indicate that with their improved performance deconvolution of separate fault periods is possible which allows for concurrent fault de-tection e majorization-minimization-based total varia- network for rolling bearing fault diagnosis Wang Fuan Jiang Hongkai Shao Haidong et al -Intelligent fault diagnosis of rolling bearings using an improved deep recurrent neural network Hongkai Jiang Xingqiu Li Haidong Shao et al -An integrated approach to planetary gearbox fault diagnosis using deep belief networks Haizhou Chen Jiaxu Wang

Compound fault identification of rolling element

A compound bearing fault diagnosis method based on SES sparsogram is presented l The IRSGWPT is presented to decompose original signal of the bearing l The SES sparsity criteria is designed to quantify the bearing fault characteristics l The numerical and experimental results confirm effectiveness of the proposed method

Bearing fault diagnosis is important in condition monitoring of any rotating machine moving elements This initial check is best on rotating machinery in particular high-speed machines Davis et al (1993) explained about comparison of methods and analysis of vibration measurement

2016-1-1The contributions of the work reported in this letter in the field of rolling bearing fault diagnosis are summarized as follows: 1 Rolling bearing feature extraction from noise-contaminated sensor signals based on LMD and MPE 2 Design of a new hierarchical structures in the SVM-BT which leads to the significant performance enhancement 3

In this article a low-cost computer system for the monitoring and diagnosis of the condition of the induction motor (IM) rolling bearings is demonstrated and tested The system allows the on-line monitoring of the IM bearings and subsequent fault diagnostics based on analysis of the vibration measurement data The evaluation of the bearing condition is made by a suitably trained neural

A compound bearing fault diagnosis method based on SES sparsogram is presented l The IRSGWPT is presented to decompose original signal of the bearing l The SES sparsity criteria is designed to quantify the bearing fault characteristics l The numerical and experimental results confirm effectiveness of the proposed method

Weak Fault Feature Extraction of Rolling Bearings

1 Introduction Rolling bearings are one of the most common but the most vulnerable parts in mechanical systems In order to ensure uninterrupted operation and avoid unnecessary losses caused by sudden failure extraction of weak fault failures of rolling bearings has become a key factor to condition monitoring and fault diagnosis concerning mechanical systems [1 2]

2017-10-20Fault Diagnosis Method Based on a New Supervised Locally Linear Embedding Algorithm for Rolling Bearing HONGFANG YUAN 1 XUE ZHANG 2 YANGYANG3 HUAQING WANG 3* 1 College of Information Science and Technology Beijing University of Chemical Technology Chao Yang District Beijing 100029 P R CHINA

In this article a low-cost computer system for the monitoring and diagnosis of the condition of the induction motor (IM) rolling bearings is demonstrated and tested The system allows the on-line monitoring of the IM bearings and subsequent fault diagnostics based on analysis of the vibration measurement data The evaluation of the bearing condition is made by a suitably trained neural

Weak Fault Diagnosis of Rolling Bearing Based on Improved 575 1) Population parameter initialization Set the population size G the chromosome length L and the evolution number K and use the binary coding method to map the range of values for variables aϵ [ ] aa min max and bϵ [ ] bb min max 2) Calculation of fitness of parents

1 Wan Shuting Lou Yuanyuan Zhao Xiufang The Design of Fault Diagnosis System for Rolling Bearing based on Virtual Instrument and Shock Pulse Method[J] Journal of Mechanical Transmission 2009-02 2 Zhu Jiangmiao Hu Peng(College of Electronic Information

The collected bearing signals are easily interfered by strong ambient noise due to complex operating conditions It's a challenge to identify faults accurately and to reduce dependence on model hyper-parameters for intelligent diagnostic methods This paper proposes an improved ensemble method based on deep belief network (DBN) for fault diagnosis of rolling bearings

The health status feature vectors extracted from rolling bearing normal operating condition and different fault conditions with 7‰ fault diameter based on entropy characteristics Holder coefficient characteristics and improved generalized box-counting dimension characteristics are

2020-7-17Rolling bearing fault detection approach based on improved dispersion entropy and AFSA optimized SVM Wuqiang Liu Jinxing Shen and Xiaoqiang Yang The International Journal of Electrical Engineering Education 0 10 1177/0020720920940584

Rolling Bearing Fault Diagnosis Based on Wavelet

Fault diagnosis of a rolling bearing using wavelet paceet de-noising and LMD JOURNAL OF VIBRATION AND SHOCK 2012(18): 153-156 [2] YANG Jianwei CAI Guoqiang YAO Dechen etc Fault diagnosis method for the rolling bearing of railway vehicle based on

2020-8-18Since the rolling elements in thrust tapered roller bearings are tapered rollers the rolling generatrix and the raceway generatrix of the washer are structurally converged at a certain point on the shaft center line of the bearing so the rolling surface can form pure rolling and high limit speed For thrust cylindrical roller bearings

For the rotating machinery system it is a challenge to explore fault detection and diagnosis for multiple-faults condition which simultaneously contains faulty bearing components and faulty gear components In the study a fault feature separation and extraction approach is proposed for the bearing-gear fault condition through combining empirical mode decomposition (EMD) Hilbert transform

A compound bearing fault diagnosis method based on SES sparsogram is presented l The IRSGWPT is presented to decompose original signal of the bearing l The SES sparsity criteria is designed to quantify the bearing fault characteristics l The numerical and experimental results confirm effectiveness of the proposed method

The flow of rolling element bearing fault diagnosis method based on IVMD and DCNN is shown in Fig 6 and specific steps are shown as follows: Steps 1: Process the original vibration data of rolling element bearings by sliding window and the vibration data samples in different states are obtained

A novel bearing fault diagnosis method based on improved locality-constrained linear coding (LLC) and adaptive PSO-optimized support vector machine (SVM) is proposed In traditional LLC each feature is encoded by using a fixed number of bases without considering the distribution of the features and the weight of the bases To address these problems an improved LLC algorithm based on adaptive

2016-9-1Feng F Si A Rao G (2012) Early fault diagnosis technology for bearing based on wavelet correlation permutation entropy Journal of mechanical engineering (48) 73-79 Doi: 10 3901/JME 2012 13 073 Feng Y Ai Y Zhou H (2015) Study on Improved Morphological Filter and LMD Fault Diagnosis of Roller Bearing

The flow of rolling element bearing fault diagnosis method based on IVMD and DCNN is shown in Fig 6 and specific steps are shown as follows: Steps 1: Process the original vibration data of rolling element bearings by sliding window and the vibration data samples in different states are obtained

The vibration signals resulting from rolling bearings are nonlinear and nonstationary and an approach for the fault diagnosis of rolling bearings using the quantile permutation entropy and EMD (empirical mode decomposition) is proposed Firstly the EMD is used to decompose the rolling bearings vibration signal and several IMFs (intrinsic mode functions) spanning different scales are obtained

A compound bearing fault diagnosis method based on SES sparsogram is presented l The IRSGWPT is presented to decompose original signal of the bearing l The SES sparsity criteria is designed to quantify the bearing fault characteristics l The numerical and experimental results confirm effectiveness of the proposed method

2018-6-14materials Article Health Degradation Monitoring and Early Fault Diagnosis of a Rolling Bearing Based on CEEMDAN and Improved MMSE Yong Lv 1 2 Rui Yuan 1 2 * ID Tao Wang 1 2 Hewenxuan Li 3 and Gangbing Song 4 1 Key Laboratory of Metallurgical Equipment and Control Technology Wuhan University of Science and Technology Ministry of Education Wuhan 430081

2019-7-30Improving Rolling Bearing Fault Diagnosis by DS Evidence Theory Based Fusion Model method which effectively improved the detection results Their findings indicate that the optimal features extracted Rolling bearing Signal processing unit USB Figure2:Schematicofthesetup