A Two-Stage Compression Method for the Fault Detection of Roller Bearings

Data measurement of roller bearings condition monitoring is carried out based on the Shannon sampling theorem, resulting in massive amounts of redundant information, which will lead to a big-data problem increasing the difficulty of roller bearing fault diagnosis. To overcome the aforementioned shortcoming, a two-stage compressed fault detection strategy is proposed in this study. First, a sliding window is utilized to divide the original signals into several segments and a selected symptom parameter is employed to represent each segment, through which a symptom parameter wave can be obtained and the raw vibration signals are compressed to a certain level with the faulty information remaining. Second, a fault detection scheme based on the compressed sensing is applied to extract the fault features, which can compress the symptom parameter wave thoroughly with a random matrix called the measurement matrix. The experimental results validate the effectiveness of the proposed method and the comparison of the three selected symptom parameters is also presented in this paper.

[1]  Moncef Gabbouj,et al.  Adaptive sampling for compressed sensing based image compression , 2014, 2014 IEEE International Conference on Multimedia and Expo (ICME).

[2]  K. I. Ramachandran,et al.  Automatic rule learning using decision tree for fuzzy classifier in fault diagnosis of roller bearing , 2007 .

[3]  Jin Chen,et al.  Bearing fault recognition method based on neighbourhood component analysis and coupled hidden Markov model , 2016 .

[4]  Han Ding,et al.  New statistical moments for the detection of defects in rolling element bearings , 2005 .

[5]  V. Rai,et al.  Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform , 2007 .

[6]  Lili Jiang,et al.  Compressive sensing and sparse decomposition in precision machining process monitoring: From theory to applications , 2015 .

[7]  N. Tandon,et al.  A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings , 1999 .

[8]  Qin Yang,et al.  Sparse classification of rotating machinery faults based on compressive sensing strategy , 2015 .

[9]  Yi Cao,et al.  Combination of process and vibration data for improved condition monitoring of industrial systems working under variable operating conditions , 2016 .

[10]  Shuilong He,et al.  Compressed sparse time–frequency feature representation via compressive sensing and its applications in fault diagnosis , 2015 .

[11]  Geert Leus,et al.  Compressive sampling based differential detection for UWB impulse radio signals , 2012, Phys. Commun..

[12]  Yang Yang,et al.  An Improved Method Based on CEEMD for Fault Diagnosis of Rolling Bearing , 2014 .

[13]  Huaqing Wang,et al.  A Feature Extraction Method Based on Information Theory for Fault Diagnosis of Reciprocating Machinery , 2009, Sensors.

[14]  Jianwei Ma,et al.  Applications of Compressed Sensing for SAR Moving-Target Velocity Estimation and Image Compression , 2011, IEEE Transactions on Instrumentation and Measurement.

[15]  Tomasz Barszcz,et al.  The use of a fuzzy logic approach for integration of vibration-based diagnostic features of rolling element bearings , 2015 .

[16]  Hani Hamdan,et al.  Compression methods for mechanical vibration signals: Application to the plane engines , 2013 .

[17]  Sofie Van Hoecke,et al.  Thermal image based fault diagnosis for rotating machinery , 2015 .

[18]  Jing Na,et al.  Envelope extraction based dimension reduction for independent component analysis in fault diagnosis of rolling element bearing , 2014 .

[19]  Hong Fan,et al.  Rotating machine fault diagnosis using empirical mode decomposition , 2008 .

[20]  Mohd Jailani Mohd Nor,et al.  Statistical analysis of sound and vibration signals for monitoring rolling element bearing condition , 1998 .

[21]  Intaek Kim,et al.  Parallel Compressed Sensing method to accelerate MRI , 2012, 2012 12th International Conference on Control, Automation and Systems.

[22]  Gang Tang,et al.  A Compound Fault Diagnosis for Rolling Bearings Method Based on Blind Source Separation and Ensemble Empirical Mode Decomposition , 2014, PloS one.

[23]  Srdjan Stankovic,et al.  Missing samples analysis in signals for applications to L-estimation and compressive sensing , 2014, Signal Process..

[24]  Minqiang Xu,et al.  Rotating machine fault diagnosis based on intrinsic characteristic-scale decomposition , 2015 .

[25]  Han Zhang,et al.  Compressed sensing based on dictionary learning for extracting impulse components , 2014, Signal Process..

[27]  Aouni A. Lakis,et al.  Application of Cyclic Spectral Analysis in Diagnosis of Bearing Faults in Complex Machinery , 2015 .

[28]  Wei Hou,et al.  Fault Detection Enhancement in Rolling Element Bearings via Peak-Based Multiscale Decomposition and Envelope Demodulation , 2014 .

[29]  Bong-Hwan Koh,et al.  Fault Detection of a Roller-Bearing System through the EMD of a Wavelet Denoised Signal , 2014, Sensors.

[30]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[31]  Liang Ma,et al.  Fault diagnosis approach for rotating machinery based on dynamic model and computational intelligence , 2015 .

[32]  P. Tse,et al.  A comparison study of improved Hilbert–Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing , 2005 .

[33]  K. I. Ramachandran,et al.  Vibration-based fault diagnosis of spur bevel gear box using fuzzy technique , 2009, Expert Syst. Appl..

[34]  Hassan Ghassemian,et al.  Remote Sensing Image Fusion Using Ripplet Transform and Compressed Sensing , 2015, IEEE Geoscience and Remote Sensing Letters.

[35]  Wei Hou,et al.  Compressive Sensing of Roller Bearing Faults via Harmonic Detection from Under-Sampled Vibration Signals , 2015, Sensors.