Sensitivity analysis of higher order coherent spectra in machine faults diagnosis

In an earlier study, the poly-coherent composite higher order spectra (i.e. poly-coherent composite bispectrum and trispectrum) frequency domain data fusion technique was proposed to detect different rotor-related faults. All earlier vibration-based faults detection involving the application of poly-coherent composite bispectrum and trispectrum have been solely based on the notion that the measured vibration data from all measurement locations on a rotating machine are always available and intact. In reality, industrial scenarios sometimes deviate from this notion, due to faults and/or damages associated with vibration sensors or their accessories (e.g. connecting cables). Sensitivity analysis of the method to various scenarios of measured vibration data availability (i.e. complete data from all measurement locations and missing/erroneous data from certain measurement locations) is also examined through experimental and industrial cases, so as to bring out the robustness of the method.

[1]  F. Ma,et al.  Bearing system health condition monitoring using a wavelet cross-spectrum analysis technique , 2012 .

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

[3]  Guillaume Bouleux Oblique projection pre-processing and TLS application for diagnosing rotor bar defects by improving power spectrum estimation , 2013 .

[4]  Jyoti K. Sinha,et al.  A Comparison of Signal Processing Tools: Higher Order Spectra Versus Higher Order Coherences , 2015 .

[5]  Robert X. Gao,et al.  Wavelets for fault diagnosis of rotary machines: A review with applications , 2014, Signal Process..

[6]  Fanrang Kong,et al.  Multiscale slope feature extraction for rotating machinery fault diagnosis using wavelet analysis , 2013 .

[7]  Akilu Yunusa-Kaltungo,et al.  Combined bispectrum and trispectrum for faults diagnosis in rotating machines , 2014 .

[8]  Xue Jun Li,et al.  Using bispectral distribution as a feature for rotating machinery fault diagnosis , 2011 .

[9]  Jyoti K. Sinha,et al.  Experimental Observations of Rotor Orbit Analysis in Rotating Machines , 2015 .

[10]  D. Osypiw,et al.  On-Line Vibration Analysis with Fast Continuous Wavelet Algorithm for Condition Monitoring of Bearing , 2003 .

[11]  Jyoti K. Sinha,et al.  A novel fault diagnosis technique for enhancing maintenance and reliability of rotating machines , 2015 .

[12]  Keith Worden,et al.  Fault detection in rolling element bearings using wavelet-based variance analysis and novelty detection , 2016 .

[13]  Linfeng Deng,et al.  A vibration analysis method based on hybrid techniques and its application to rotating machinery , 2013 .

[14]  Jin Chen,et al.  Performance degradation assessment of rolling bearing based on bispectrum and support vector data description , 2014 .

[15]  Jyoti K. Sinha,et al.  Higher Order Spectra for Crack and Misalignment Identification in the Shaft of a Rotating Machine , 2007 .

[16]  Hongkai Jiang,et al.  An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis , 2013 .

[17]  Paul R. White,et al.  The interpretation of the bispectra of vibration signals—: II. Experimental results and applications , 1995 .

[18]  Qiong Chen,et al.  Fault diagnosis of rolling bearing based on wavelet transform and envelope spectrum correlation , 2013 .

[19]  Jyoti K. Sinha,et al.  Use of composite higher order spectra for faults diagnosis of rotating machines with different foundation flexibilities , 2015 .

[20]  Jyoti K. Sinha,et al.  First Aid Treatment for Machine Vibration Problems , 2014 .

[21]  Jyoti K. Sinha,et al.  Coherence Measurement for Early Contact Detection between Two Components , 2006 .

[22]  P. White,et al.  HIGHER-ORDER SPECTRA: THE BISPECTRUM AND TRISPECTRUM , 1998 .

[23]  V. Sugumaran,et al.  Feature extraction using wavelets and classification through decision tree algorithm for fault diagnosis of mono-block centrifugal pump , 2013 .

[24]  S.A.V. Satya Murty,et al.  Roller element bearing fault diagnosis using singular spectrum analysis , 2013 .

[25]  Guoyu Meng,et al.  Compound rub malfunctions feature extraction based on full-spectrum cascade analysis and SVM , 2006 .