Research of Adaptive Neighborhood Incremental Principal Component Analysis and Locality Preserving Projection Manifold Learning Algorithm

doi:10.1007/s12204-018-1936-7

Abstract

Abstract: In view of the incremental learning problem of manifold learning algorithm, an adaptive neighborhood incremental principal component analysis (PCA) and locality preserving projection (LPP) manifold learning algorithm is presented, and the incremental learning principle of algorithm is introduced. For incremental sample data, the adjacency and covariance matrices are incrementally updated by the existing samples; then the dimensionality reduction results of the incremental samples are estimated by the dimensionality reduction results of the existing samples; finally, the dimensionality reduction results of the incremental and existing samples are updated by subspace iteration method. The adaptive neighborhood incremental PCA-LPP manifold learning algorithm is applied to processing of gearbox fault signals. The dimensionality reduction results by incremental learning have very small error, compared with those by batch learning. Spatial aggregation of the incremental samples is basically stable, and fault identification rate is increased.

Key words: incremental learning| adaptive, manifold learning| fault diagnosis

摘要： In view of the incremental learning problem of manifold learning algorithm, an adaptive neighborhood incremental principal component analysis (PCA) and locality preserving projection (LPP) manifold learning algorithm is presented, and the incremental learning principle of algorithm is introduced. For incremental sample data, the adjacency and covariance matrices are incrementally updated by the existing samples; then the dimensionality reduction results of the incremental samples are estimated by the dimensionality reduction results of the existing samples; finally, the dimensionality reduction results of the incremental and existing samples are updated by subspace iteration method. The adaptive neighborhood incremental PCA-LPP manifold learning algorithm is applied to processing of gearbox fault signals. The dimensionality reduction results by incremental learning have very small error, compared with those by batch learning. Spatial aggregation of the incremental samples is basically stable, and fault identification rate is increased.

关键词: incremental learning| adaptive, manifold learning| fault diagnosis

CLC Number:

DENG Shijie (邓士杰), TANG Liwei (唐力伟), ZHANG Xiaotao (张晓涛). Research of Adaptive Neighborhood Incremental Principal Component Analysis and Locality Preserving Projection Manifold Learning Algorithm[J]. Journal of Shanghai Jiao Tong University (Science), 2018, 23(2): 269-275.

References 11

[1]	ZHAO Z H, HAO X H. Linear locality preserving anddiscriminating projection for face recognition [J]. Journalof Electronics & Information Technology, 2013,35(2): 463-466 (in Chinese).
[2]	HE X F, NIYOGI P. Locality preserving projections[C]//Neural Information Processing Systems 16. Vancouver:MIT Press, 2004: 153-160.
[3]	ZHANG Z H, ZHU X Z, ZHAO J M, et al. Imageretrieval based on PCA-LPP [C]//2011 10th InternationalSymposium on Distributed Computing and Applicationsto Business, Engineering and Science. Wuxi:IEEE, 2011: 230-233.
[4]	WANG J, FENG J, HAN Z Y. Locally preserving PCAmethod based on manifold learning and its applicationin fault detection [J]. Control and Decision, 2013,28(5): 683-687 (in Chinese).
[5]	JIA P, YIN J S, HUANG X S, et al. IncrementalLaplacian eigenmaps by preserving adjacent informationbetween data points [J]. Pattern Recognition Letters,2009, 30(8): 1457-1463.
[6]	CHIN T J, SUTER D. Out-of-sample extrapolation oflearned manifolds [J]. IEEE Transactions on PatternAnalysis and Machine Intelligence, 2008, 30(9): 1547-1556.
[7]	YANG Q, CHEN G M, TONG X M, et al. Applicationof incremental local tangent space alignment algorithmto rolling bearings fault diagnosis [J]. Journal of MechanicalEigineering, 2012, 48(5): 81-86 (in Chinese).
[8]	TAN C, GUAN J H, ZHOU S G. Multi-sample incrementalmanifold learning algorithm based on isogonalmapping [J]. Pattern Recognition and Artificial Intelligence,2014, 27(2): 127-133 (in Chinese).
[9]	SUN J F. New method for moving object detectionbased on Gaussian kernel density estimation [J]. ComputerTechnology and Development, 2010, 20(8): 45-48(in Chinese).
[10]	YAN S C, XU D, ZHANG B Y, et al. Graph embeddingand extensions: A general framework for dimensionalityreduction [J]. IEEE Transactions on PatternAnalysis and Machine Intelligence, 2007, 29(1): 40-50.
[11]	LI C L, WANG Z S, JIANG H K, et al. AdaptiveHessian LLE in mechanical fault feature extraction [J].Journal of Vibration Engineering, 2013, 26(5): 758-763 (in Chinese).

[1]	XU Yong, CAI Yunze, SONG Lin. Review of Research on Condition Assessment of Nuclear Power Plant Equipment Based on Data-Driven [J]. Journal of Shanghai Jiao Tong University, 2022, 56(3): 267-278.
[2]	NIE Rui, WANG Hongru. Fault Diagnosis of UAV Formation Actuator Based on Neural Network Observer [J]. Air & Space Defense, 2022, 5(2): 32-41.
[3]	LIU Xiuli, XU Xiaoli. A Fault Diagnosis Method Based on Feature Pyramid CRNN Network [J]. Journal of Shanghai Jiao Tong University, 2022, 56(2): 182-190.
[4]	MA Hangyu, ZHOU Di, WEI Yujie, WU Wei, PAN Ershun. Intelligent Bearing Fault Diagnosis Based on Adaptive Deep Belief Network Under Variable Working Conditions [J]. Journal of Shanghai Jiao Tong University, 2022, 56(10): 1368-1377.
[5]	HU Xiaoqiang,ZHONG Xunyu,ZHANG Xiaoli,PENG Xiafu,HE Ying. A Two-Level Fault Diagnosis Method for Gyro-Quadruplet Assisted by Support Vector Machine [J]. Journal of Shanghai Jiaotong University, 2020, 54(11): 1151-1156.
[6]	LU Cheng,XU Tingxue,WANG Hong. Fault Diagnosis of Terminal Guidance Radar Based on Attribute Granulation Clustering and Echo State Network [J]. Journal of Shanghai Jiaotong University, 2018, 52(9): 1112-1119.
[7]	YU Kun (俞昆), TAN Jiwen (谭继文), LIN Tianran (林天然). Fault Diagnosis of Rolling Element Bearing Using Multi-Scale Lempel-Ziv Complexity and Mahalanobis Distance Criterion [J]. Journal of Shanghai Jiao Tong University (Science), 2018, 23(5): 696-701.
[8]	JIA Lei,DONG Wei，SUN Xinya，JI Yindong，CHEN Hua. Soft Faults Diagnosis of Track Circuit with Tolerance Based on NodeVoltage Increments [J]. Journal of Shanghai Jiaotong University, 2017, 51(6): 679-685.
[9]	WU Bin1* (吴斌), XI Lifeng2 (奚立峰), FAN Sixia1 (范思遐), ZHAN Jian1 (占健). Fault Diagnosis for Wind Turbine Based on Improved Extreme Learning Machine [J]. Journal of shanghai Jiaotong University (Science), 2017, 22(4): 466-473.
[10]	LIU Yinhua1* (刘银华), YE Xialiang1 (叶夏亮), JIN Sun2 (金隼). A Bayesian Based Process Monitoring and Fixture Fault Diagnosis Approach in the Auto Body Assembly Process [J]. Journal of shanghai Jiaotong University (Science), 2016, 21(2): 164-172.
[11]	ZHANG Wei1* (张伟), HOU Yue-min1,2 (侯悦民). Systematic Safety Analysis Method for Power Generating Equipment [J]. Journal of shanghai Jiaotong University (Science), 2015, 20(4): 508-512.
[12]	SHANG Qun-li1 (尚群立), ZHANG Zhen2 (张镇), XU Xiao-bin2* (徐晓滨). Dynamic Fault Diagnosis Using the Improved Linear Evidence Updating Strategy [J]. Journal of shanghai Jiaotong University (Science), 2015, 20(4): 427-436.
[13]	REN Fang-yu (任方宇), SI Shu-bin* (司书宾), CAI Zhi-qiang (蔡志强), ZHANG Shuai (张帅). Transformer Fault Analysis Based on Bayesian Networks and Importance Measures [J]. Journal of shanghai Jiaotong University (Science), 2015, 20(3): 353-357.
[14]	BAO Yong-lin (鲍泳林). Primary Research on Real-Time Fault Diagnosis Platform for Fuel Tank System of an Aircraft [J]. Journal of shanghai Jiaotong University (Science), 2015, 20(3): 358-362.
[15]	BAI Lu* (白璐), DU Cheng-lie (杜承烈), GUO Yang-ming (郭阳明). A Fuzzy Fault Diagnosis Method for Large Radar Based on Directed Graph Model [J]. Journal of shanghai Jiaotong University (Science), 2015, 20(3): 363-369.