Fault severity recognition of aviation piston pump based on feature extraction of EEMD paving and optimized support vector regression model

Abstract Recognizing an early fault for aviation hydraulic pump and evaluating its size is essential in this industrial application. This paper proposes a new method which combines ensemble empirical mode decomposition (EEMD) paving and optimized support vector regression (SVR) model to detect faults and estimate the fault sizes of a piston pump. Different from other feature extraction methods in which the information of intrinsic mode functions (IMFs) is not being fully utilized, the collected pressure signals are first decomposed by EEMD, and then some useful IMFs are selected by calculating the correlation coefficients between the signals reconstructed by the chosen IMFs and the original signals. These selected IMFs are referred to as EEMD paving. Subsequently, some new fault features considering time domain, frequency domain, and time–frequency domain are extracted from the paving of EEMD. To acquire the most sensitive fault features, principal component analysis (PCA) is then employed to reduce the dimensionality of the original feature vectors. Finally, SVR model is constructed to identify different fault sizes of aviation pump. To achieve higher recognition accuracy, a new method combining genetic algorithm (GA) with grid search is adopted to optimize the parameters of the SVR model. The effectiveness of the proposed method is verified by two datasets collected from a test rig under different conditions. The results demonstrate that the fault features based on the proposed method can be used to characterize the pump fault severity more accurately, and the constructed SVR model has higher recognition accuracy and better prediction ability when compared with previously published methods. The proposed method can also be readily used in other industrial applications.

[1]  Yaguo Lei,et al.  A review on empirical mode decomposition in fault diagnosis of rotating machinery , 2013 .

[2]  Zhen Zhao,et al.  Intermittent chaos and sliding window symbol sequence statistics-based early fault diagnosis for hydraulic pump on hydraulic tube tester , 2009 .

[3]  Qin Zhang,et al.  A Wavelet Packet and Residual Analysis Based Method for Hydraulic Pump Health Diagnosis , 2006 .

[4]  Noureddine Zerhouni,et al.  Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression , 2015, IEEE Transactions on Instrumentation and Measurement.

[5]  Shaoping Wang,et al.  Fault diagnosis of hydraulic piston pumps based on a two-step EMD method and fuzzy C-means clustering , 2016 .

[6]  Yitao Liang,et al.  A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM , 2015 .

[7]  Brigitte Chebel-Morello,et al.  Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals , 2015 .

[8]  Peter W. Tse,et al.  A critical study of different dimensionality reduction methods for gear crack degradation assessment under different operating conditions , 2016 .

[9]  Chen Lu,et al.  Performance degradation prediction for a hydraulic servo system based on Elman network observer and GMM–SVR , 2015 .

[10]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[11]  Fanrang Kong,et al.  Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier , 2013 .

[12]  Shaojiang Dong,et al.  Bearing degradation process prediction based on the PCA and optimized LS-SVM model , 2013 .

[13]  Pierre Bect,et al.  Diagnostic and decision support systems by identification of abnormal events: Application to helicopters , 2015 .

[14]  Changqing Shen,et al.  Recognition of rolling bearing fault patterns and sizes based on two-layer support vector regression machines , 2014 .

[15]  Lei Xu,et al.  ISHM-based intelligent fusion prognostics for space avionics , 2013 .

[16]  Yi Shen,et al.  Optimized Ensemble EMD-Based Spectral Features for Hyperspectral Image Classification , 2014, IEEE Transactions on Instrumentation and Measurement.

[17]  Enrico Zio,et al.  Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method , 2014, Digit. Signal Process..

[18]  Bo-Suk Yang,et al.  Support vector machine in machine condition monitoring and fault diagnosis , 2007 .

[19]  Ting Yang,et al.  Feature Knowledge Based Fault Detection of Induction Motors Through the Analysis of Stator Current Data , 2016, IEEE Transactions on Instrumentation and Measurement.

[20]  Hassani Messaoud,et al.  Kernel principal component analysis with reduced complexity for nonlinear dynamic process monitoring , 2017 .

[21]  Yaguo Lei,et al.  Gear crack level identification based on weighted K nearest neighbor classification algorithm , 2009 .

[22]  Chen Lu,et al.  Chaotic information-geometric support vector machine and its application to fault diagnosis of hydraulic pumps , 2014 .

[23]  Dong Yunfeng,et al.  The GA-ANN expert system for mass-model classification of TSTO surrogates , 2016 .

[24]  Shaoping Wang,et al.  Fault severity recognition of hydraulic piston pumps based on EMD and feature energy entropy , 2015, 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA).

[25]  Shaoping Wang,et al.  Remaining useful life prediction based on the Wiener process for an aviation axial piston pump , 2016 .

[26]  Paolo Pennacchi,et al.  Empirical mode decomposition of pressure signal for health condition monitoring in waterjet cutting , 2014 .

[27]  Jun Du,et al.  Layered clustering multi-fault diagnosis for hydraulic piston pump , 2013 .

[28]  Qing Huang,et al.  A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network , 2015, PloS one.

[29]  M. Benbouzid,et al.  EEMD-based wind turbine bearing failure detection using the generator stator current homopolar component , 2013 .

[30]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[31]  Shaoping Wang,et al.  A partial lubrication model between valve plate and cylinder block in axial piston pumps , 2015 .

[32]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[33]  Huaqing Wang,et al.  Quantitative diagnosis of fault severity trend of rolling element bearings , 2015 .

[34]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.