The Feature Extraction and Diagnosis of Rolling Bearing Based on CEEMD and LDWPSO-PNN

The vibration signals of rolling bearing are often highly nonstationary and nonlinear, and consequently it is not accurate to extract and identify the characteristics of these signals by the traditional methods. In order to improve the performance on the feature extraction from bearing signals and the accuracy of the diagnosis, it requires effective signal processing and diagnose algorithms. In this paper, a new fault diagnosis algorithm which combines complementary ensemble empirical mode decomposition (CEEMD), probabilistic neural network (PNN) and particle swarm optimization (PSO) algorithm optimized by improved linear decreasing weight (LDW) algorithm is proposed. In this method, firstly the vibration signals are decomposed into a number of Intrinsic Mode Functions (IMFs) by the CEEMD algorithm since it has good adaptive ability to nonstable signals and can effectively extract fault features. Then the improved LDWPSO algorithm is introduced to solve the problem that the selection of smoothing factor in PNN model is arbitrary and uncertain. Finally, train and diagnose the fault types of rolling bearing using the LDWPSO-PNN model. The proposed method is verified by the experimental datasets. The results indicate that the method can extract the feature vectors of the vibration signals and distinguish them effectively.

[1]  Yanping Bai,et al.  A Novel Hybrid Model Based on TVIW-PSO-GSA Algorithm and Support Vector Machine for Classification Problems , 2019, IEEE Access.

[2]  Yong Guan,et al.  Rolling Element Bearing Fault Diagnosis Using Improved Manifold Learning , 2017, IEEE Access.

[3]  Kun Jiang,et al.  A deep capsule neural network with stochastic delta rule for bearing fault diagnosis on raw vibration signals , 2019 .

[4]  Michael R. Lyu,et al.  A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training , 2007, Appl. Math. Comput..

[5]  N. Huang,et al.  A study of the characteristics of white noise using the empirical mode decomposition method , 2004, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[6]  Zhenjun Tang,et al.  Robust image hashing with multidimensional scaling , 2017, Signal Process..

[7]  Zhipeng Wang,et al.  Bearing Fault Diagnosis Using Multiclass Self-Adaptive Support Vector Classifiers Based on CEEMD–SVD , 2018, Wirel. Pers. Commun..

[8]  Yang Hu,et al.  Fault diagnosis approach for photovoltaic arrays based on unsupervised sample clustering and probabilistic neural network model , 2018, Solar Energy.

[9]  Yan He,et al.  The Parameters Selection of PSO Algorithm influencing On performance of Fault Diagnosis , 2016 .

[10]  Haisheng Yu,et al.  Decentralized state estimation for a large-scale spatially interconnected system. , 2018, ISA transactions.

[11]  Huanyu Dong,et al.  BA-PNN-based methods for power transformer fault diagnosis , 2019, Adv. Eng. Informatics.

[12]  Qing Zhang,et al.  WPD and DE/BBO-RBFNN for solution of rolling bearing fault diagnosis , 2018, Neurocomputing.

[13]  Jin Yue Liu,et al.  The Application of Particle Swarm Optimization Algorithm in the Extremum Optimization of Nonlinear Function , 2012, CIT.

[14]  Hamid Reza Karimi,et al.  An EEMD Aided Comparison of Time Histories and Its Application in Vehicle Safety , 2017, IEEE Access.

[15]  Minqiang Xu,et al.  A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery , 2019, Entropy.

[16]  Li Zhang,et al.  Fault feature extraction of low speed roller bearing based on Teager energy operator and CEEMD , 2019, Measurement.

[17]  Cheng-Hong Yang,et al.  Linearly Decreasing Weight Particle Swarm Optimization with Accelerated Strategy for Data Clustering , 2022 .

[18]  Qingsong Xu,et al.  Improved shuffled frog leaping algorithm-based BP neural network and its application in bearing early fault diagnosis , 2015, Neural Computing and Applications.

[19]  Ming Hong,et al.  An investigation of rolling bearing early diagnosis based on high-frequency characteristics and self-adaptive wavelet de-noising , 2016, Neurocomputing.

[20]  Brigitte Chebel-Morello,et al.  Linear feature selection and classification using PNN and SFAM neural networks for a nearly online diagnosis of bearing naturally progressing degradations , 2015, Eng. Appl. Artif. Intell..

[21]  Yanli Yang,et al.  Bearing Fault Automatic Classification Based on Deep Learning , 2018, IEEE Access.

[22]  Xiaohui Wang,et al.  Fault diagnosis of rotating machines for rail vehicles based on local mean decomposition—energy moment—directed acyclic graph support vector machine , 2016 .

[23]  Ponnuthurai N. Suganthan,et al.  Recent advances in differential evolution - An updated survey , 2016, Swarm Evol. Comput..

[24]  Norden E. Huang,et al.  Complementary Ensemble Empirical Mode Decomposition: a Novel Noise Enhanced Data Analysis Method , 2010, Adv. Data Sci. Adapt. Anal..

[25]  Jing Na,et al.  Experimental investigation on double-impulse phenomenon of hybrid ceramic ball bearing with outer race spall , 2016, Mechanical Systems and Signal Processing.

[26]  Ujjwal Maulik,et al.  Genetic algorithm-based clustering technique , 2000, Pattern Recognit..