Pattern Recognition Method Based on Embedded SRVPMCD

Abstract

Abstract:

Targeting the inherent defects of the variable predictive model based class discriminate(VPMCD) and the problems of how to choose the features of those mechanical faults, which was the effects on timeliness due to the excessive characteristic dimensions and feature selection needed o introduce the subjective factors, a new embedded SRVPMCD method was put forward herein. Introducing variable and calculating the significant level by stepwise regression, establishing a prediction model that only contained significant characteristic values, achieving the function of embedded feature selection and modeling simultaneously, and then using the established prediction model to predict the characteristic values of those unclassified signals samples, the prediction results would be recognized by the model as accordance to classify. The analysis results for roller bearing fault signals show that compared to the original VPMCD and combined methods, the pattern recognition method based on embedded SRVPMCD can realize the double functions of feature selection and classification, and identify the working states and fault types of roller bearing quickly.

Key words: stepwise regression, stepwise regression-variable predictive mode-based class discriminate（SRVPMCD）, roller bearing, fault diagnosis

摘要：

针对多变量预测模型（VPMCD）模式识别方法的固有缺陷和机械故障特征难以选择的难题，即特征维数较多时对时效性的影响和特征选择需要引入主观因素的现状,提出了一种基于嵌入式的逐步回归多变量预测模型（SRVPMCD）模式识别方法。该方法首先通过逐步回归引入变量并计算其显著水平,建立只包含显著特征值的预测模型，同时实现嵌入式特征选择和建模分类的功能，然后用所建立的预测模型来预测待分类样本的特征值，最后把预测结果作为分类依据进行模式识别。对滚动轴承故障信号的分析结果表明，基于嵌入式SRVPMCD的模式识别方法可以实现特征选择和分类的双重功能，在保证识别精度的前提下，比原VPMCD方法及其组合方法可以更快地识别滚动轴承的工作状态和故障类型。

关键词: 逐步回归, 逐步回归多变量预测模型, 滚动轴承, 故障诊断

CLC Number:

TH113

Pan Haiyang, Yang Yu, Ma Li, Cheng Junsheng. Pattern Recognition Method Based on Embedded SRVPMCD[J]. China Mechanical Engineering, 2014, 25(24): 3308-3313.

潘海洋, 杨宇, 马利, 程军圣. 基于嵌入式SRVPMCD的模式识别方法[J]. 中国机械工程, 2014, 25(24): 3308-3313.

References

[1]Raghuraj R,Lakshminarayanan S.Variable Predictive Models-A New Multivariate Classification Approach for Pattern Recognition Applications[J].Pattern Recognition,2009,42(1):7-16.
[2]Xu Zengbing,Xuan Jianping,Shi Tielin,et al. Application of a Modified Fuzzy ARTMAP with Feature-weight Learning for the Fault Diagnosis of Bearing [J]. Expert Systems with Applications.2009, 36: 9961-9968.
[3]Sun Zehang,Bebis G,Miller R.Object Detection Using Feature Subset Selection [J]. Pattern Recognition, 2004, 37:2165-2176.
[4]Wu J D,Liu C H.An Expert System for Fault Diagnosis in Internal Combustion Engines Using Wavelet Packet Transform and Neural Network[J].Expert Systems with Applications,2009,36(3):4278-4286.
[5]Huang N E,Shen Z,Long S R.A New View of Nonlinear Water Waves:the Hilbert Spectrum[J].Annu. Rev. Fluid Mech., 1999, 31: 417-457.
[6]Smith J S.The Local Mean Decomposition and Its Application to EEG Perception Data[J].Journal of the Royal Society Interface, 2005,2(5):443-454.
[7]Frei M G,Osorio I.Intrinsic Time-scale Decomposition:Time-frequency-energy Analysis and Real-time Filtering of Non-stationary Signals[J].Proceedings of the Royal Society:A,2007,463:321-342.
[8]Cheng J S,Yu D J,Yang Y.Energy Operator Demodulating Approach Based on EMD and Its Application in Mechanical Fault Diagnosis[J].Chinese Journal of Mechanical Engineering,2004,40(8):115-118．
[9]Lei Yaguo,He Zhengjia,Zi Yanyang.Application of the EEMD Method to Rotor Fault Diagnosis of Rotating Machinery[J]. Mechanical Systems and Signal Processing,2009,23:1327-1338.
[10]程军圣,郑近德,杨宇.一种新的非平稳信号分析方法——局部特征尺度分解[J].振动工程学报,2012,25(2):215-220.
Cheng Junsheng,Zheng Jinde,Yang Yu.A Nonstationary Signal Analysis Approach:the Local Characteristic-scale Decomposition Method[J].Journal of Vibration Engineering,2012,25(2):215-220.
[11]Case Western Reserve University Bearing Data Center.Bearing Data Center Fault Test Data.[2009-10-01]. http://www.eecs.case.edu/laboratory/bearing.

[1]	LUO Ji-Sai, XU De-Ge, SHI Mei-Li. Gearbox Fault Diagnosis with Rotating Speed Fluctuation Based on SVD and Chirplet Path Pursuit Algorithm [J]. J4, 201016, 21(16): 1947-1951.
[2]	ZHENG Jinde, WANG Xinglong, PAN Haiyang, TONG Jinyu, LIU Qingyun. Rolling Bearing Fault Diagnosis Method Based on Adaptive Autogram [J]. China Mechanical Engineering, 2021, 32(07): 778-785，792.
[3]	CHEN Huanguo, LIU Peijun, YU Hang, XIAO Xue. Identification of Fault Degree of Oil Filter Blockage in Electro-hydraulic Actuator Based on an Improved PCA-SOM [J]. China Mechanical Engineering, 2021, 32(07): 799-805.
[4]	DING Jiaxin ;WANG Zhenya ;YAO Ligang ;CAI Yongwu. Rolling Bearing Fault Diagnosis Based on GCMWPE and Parameter Optimization SVM [J]. China Mechanical Engineering, 2021, 32(02): 147-155.
[5]	YANG Luhang1;LI Baoqing1;WANG Ping2,3;WANG Jian2,3;YANG Yu1. Fault Diagnosis Method of Rolling Bearings Based on Probability Output Flexible Convex Hull [J]. China Mechanical Engineering, 2021, 32(01): 40-46.
[6]	WAN Shuting;PENG Bo;WANG Xiaolong. Early Fault Diagnosis Method of Rolling Bearings Based on DTD Transform-SCS Method [J]. China Mechanical Engineering, 2020, 31(23): 2829-2836.
[7]	MENG Zong;LIU Zihan;LYU Meng. Fault Diagnosis for Rolling Bearings Based on Improved Singular Value Decomposition and Spectral Kurtosis [J]. China Mechanical Engineering, 2020, 31(20): 2420-2428.
[8]	WANG Zhenya;YAO Ligang. Rolling Bearing Fault Diagnosis Method Based on Generalized Refined Composite Multiscale Sample Entropy and Manifold Learning [J]. China Mechanical Engineering, 2020, 31(20): 2463-2471.
[9]	CAI Yanping;FAN Yu;CHEN Wan;ZHANG Jinming. Applications of Improved Time-frequency Analysis and Feature Fusion in Fault Diagnosis of IC Engines [J]. China Mechanical Engineering, 2020, 31(16): 1901-1911.
[10]	JIANG Shaofei;WU Tianji;PENG Xiang;LI Jiquan;LI Zhi;SUN Tao. Data Driven Fault Diagnosis Method Based on XGBoost Feature Extraction [J]. China Mechanical Engineering, 2020, 31(10): 1232-1239.
[11]	LIU Guanghui1;HONG Jun1;SUN Yanhui1;WU Wenwu2. Nonlinear Stiffness Characteristic Analysis of Cylindrical Roller Bearings Considering Variation of Roller Positions [J]. China Mechanical Engineering, 2020, 31(05): 505-512.
[12]	CHEN Xuefeng, GUO Yanjie, XU Caibin, SHANG Hongbing. Review of Fault Diagnosis and Health Monitoring for Wind Power Equipment [J]. China Mechanical Engineering, 2020, 31(02): 175-189.
[13]	LIU Yang;LIU Xiaobo;LIANG Shan. Aeroengine Rotor Fault Diagnosis Based on Fourier Decomposition Method [J]. China Mechanical Engineering, 2019, 30(18): 2156-2163.
[14]	LI Congzhi;ZHENG Jinde;PAN Haiyang;LIU Qingyun. Fault Diagnosis Method of Rolling Bearings Based on Refined Composite Multiscale Dispersion Entropy and Support Vector Machine [J]. China Mechanical Engineering, 2019, 30(14): 1713-1719,1726.
[15]	LIU Zhaolun1,2;ZHANG Chunlan2;WU You2;WANG Haiyu2;LIU Bin1,2. An Incremental Bayesian Algorithm on Fault Diagnosis of Grate Coolers [J]. China Mechanical Engineering, 2019, 30(10): 1163-1171.