The morphological undecimated wavelet decomposition – Discrete cosine transform composite spectrum fusion algorithm and its application on hydraulic pumps

Abstract Degradation feature extraction of hydraulic pumps is a key step of the condition-based maintenance. In this paper, a novel method based on MUWD-DCS fusion algorithm is proposed. In order to decrease noises and disturbances, the method for obtaining detail components by the Morphological Undecimated Wavelet Decomposition (MUWD) with the selected parameters is presented firstly. Multi-channel vibration signals are proposed by the MUWD and the detail components containing sensitive information are achieved. Furthermore, on the basement of the earlier Composite Spectrum (CS), the DCS fusion algorithm is proposed to make fusion of the obtained detail components for improving the feature performance. The DCS entropy is extracted to be the degradation feature. Analysis of application on the hydraulic pump degradation experiment demonstrates that the proposed algorithm is feasible and it is effective to reveal the performance degradation of the hydraulic pump.

[1]  Wei He,et al.  A joint adaptive wavelet filter and morphological signal processing method for weak mechanical impulse extraction , 2010 .

[2]  Liyi Xiao,et al.  CORDIC-Based Unified Architectures for Computation of DCT/IDCT/DST/IDST , 2014, Circuits Syst. Signal Process..

[3]  Ming J. Zuo,et al.  Multivariate EMD and full spectrum based condition monitoring for rotating machinery , 2012 .

[4]  Jyoti K. Sinha,et al.  An improved data fusion technique for faults diagnosis in rotating machines , 2014 .

[5]  Jyoti K. Sinha,et al.  Vibration-based condition monitoring of rotating machines using a machine composite spectrum , 2013 .

[6]  孙健,et al.  Morphological Undecimated Wavelet Decomposition Fusion Algorithm and Its Application on Fault Feature Extraction of Hydraulic Pump , 2015 .

[7]  P. Srinivasa Pai,et al.  Multiscale Slope Feature Extraction for Gear and Bearing Fault Diagnosis Using Wavelet Transform , 2014 .

[8]  Fulei Chu,et al.  Morphological undecimated wavelet decomposition for fault diagnostics of rolling element bearings , 2009 .

[9]  Lin Liang,et al.  Quantitative diagnosis of a spall-like fault of a rolling element bearing by empirical mode decomposition and the approximate entropy method , 2013 .

[10]  Bo-Suk Yang,et al.  Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine , 2012, WCE 2010.

[11]  José-Luis Sánchez-Romero,et al.  Mathematical morphology for design and manufacturing , 2011, Math. Comput. Model..

[12]  Junsheng Cheng.  Local Characteristic-scale Decomposition Method and Its Application to Gear Fault Diagnosis , 2012 .

[13]  Bing Li,et al.  Gear fault detection using multi-scale morphological filters , 2011 .

[14]  Yujing Wang,et al.  Classification of fault location and the degree of performance degradation of a rolling bearing based on an improved hyper-sphere-structured multi-class support vector machine , 2012 .

[15]  James A. Rodger,et al.  Toward reducing failure risk in an integrated vehicle health maintenance system: A fuzzy multi-sensor data fusion Kalman filter approach for IVHMS , 2012, Expert Syst. Appl..

[16]  Shufang Li,et al.  Feature extraction and recognition of ictal EEG using EMD and SVM , 2013, Comput. Biol. Medicine.

[17]  Yongbai Sha.  Kinematic Analysis and Simulation Research on Pipe Automatic Transferring System , 2012 .

[18]  Rick S. Blum,et al.  Theoretical analysis of correlation-based quality measures for weighted averaging image fusion , 2010, Inf. Fusion.

[19]  Yang Debin.  MORPHOLOGICAL UNDECIMATED WAVELET AND ITS APPLICATION TO FEATURE EXTRACTION OF IMPULSIVE SIGNAL , 2007 .

[20]  Shaoyuan Li,et al.  A switch-mode information fusion filter based on ISRUKF for autonomous navigation of spacecraft , 2014, Inf. Fusion.

[21]  Jean Cousty,et al.  Morphological filtering on graphs , 2013, Comput. Vis. Image Underst..

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

[23]  Jeremy S. Smith,et al.  Morphological undecimated wavelet decomposition for fault location on power transmission lines , 2006, IEEE Transactions on Circuits and Systems I: Regular Papers.

[24]  B. Tang,et al.  Bearing running state recognition based on non-extensive wavelet feature scale entropy and support vector machine , 2013 .

[25]  Jinde Zheng,et al.  A rolling bearing fault diagnosis approach based on LCD and fuzzy entropy , 2013 .

[26]  En-Hui Yang,et al.  Transparent Composite Model for DCT Coefficients: Design and Analysis , 2014, IEEE Transactions on Image Processing.

[27]  Ke Xi-zheng.  Multi-wavelet-basis multi-scale multi-sensor data fusion , 2010 .

[28]  S. N. Panigrahi,et al.  Gear fault diagnosis using active noise cancellation and adaptive wavelet transform , 2014 .

[29]  Plamen P. Angelov,et al.  Density-based averaging - A new operator for data fusion , 2013, Inf. Sci..

[30]  Yang Yu.  Local Characteristic-scale Decomposition Method and Its Application to Gear Fault Diagnosis , 2012 .