Impulsive Noise Cancellation Method for Copper Ore Crusher Vibration Signals Enhancement

In this paper, we deal with a problem of local damage detection in bearings in the presence of a high-energy impulsive noise. Such a problem was identified during diagnostics of bearings in raw materials crusher. Unfortunately, classical approaches cannot be applied due to the impulsive character of the noise. In this paper we propose, a procedure that cancels out impulsive noise rather than extracts signal of interest. The methodology is based on the regime switching model with two regimes: first corresponding to high-energy noncyclic impulses and second to the rest of the signal. We apply the proposed technique to a simulated signal as well as to the real one. Effectiveness of the method is presented graphically using time series, time-frequency spectrogram, and classical envelope analysis. The obtained results indicate efficiency of the method in impulsive noise cancellation and improve the ability to detect a damage.

[1]  Fakher Chaari,et al.  Gearbox Vibration Signal Amplitude and Frequency Modulation , 2012 .

[2]  Huijun Gao,et al.  Data-Based Techniques Focused on Modern Industry: An Overview , 2015, IEEE Transactions on Industrial Electronics.

[3]  Robert Granat.  Detecting Regional Events via Statistical Analysis of Geodetic Networks , 2006 .

[4]  Ryszard A. Makowski,et al.  Automatic speech signal segmentation based on the innovation adaptive filter , 2014, Int. J. Appl. Math. Comput. Sci..

[5]  Jérôme Antoni,et al.  The infogram: Entropic evidence of the signature of repetitive transients , 2016 .

[6]  Michael G. Lipsett,et al.  Fault detection using transient machine signals , 2008 .

[7]  Tomasz Barszcz,et al.  A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram , 2011 .

[8]  Marco Cocconcelli,et al.  Overview on condition monitoring for bearings in variable speedconditions: the example of a packaging machine , 2011 .

[9]  Rasmus Bro,et al.  Pre‐whitening of data by covariance‐weighted pre‐processing , 2003 .

[10]  Tomasz Barszcz,et al.  Bearings Fault Detection in Gas Compressor in Presence of High Level of Non-Gaussian Impulsive Noise , 2013 .

[11]  Radoslaw Zimroz,et al.  Identification of cyclic components in presence of non-Gaussian noise – application to crusher bearings damage detection , 2015 .

[12]  Alberto Bellini,et al.  Detection of Generalized-Roughness Bearing Fault by Spectral-Kurtosis Energy of Vibration or Current Signals , 2009, IEEE Transactions on Industrial Electronics.

[13]  Robert B. Randall,et al.  The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines , 2006 .

[14]  Agnieszka Wyłomańska,et al.  Recognition of stable distribution with Lévy index α close to 2. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  Ming Liang,et al.  Detection and diagnosis of bearing and cutting tool faults using hidden Markov models , 2011 .

[16]  David He,et al.  A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology , 2007 .

[17]  Robert B. Randall,et al.  A Stochastic Model for Simulation and Diagnostics of Rolling Element Bearings With Localized Faults , 2003 .

[18]  Jont B. Allen,et al.  Short term spectral analysis, synthesis, and modification by discrete Fourier transform , 1977 .

[19]  Chang‐Jin Kim,et al.  Dynamic linear models with Markov-switching , 1994 .

[20]  Michèle Basseville,et al.  Detection of abrupt changes: theory and application , 1993 .

[21]  Grzegorz Sikora,et al.  REGIME VARIANCE TESTING — A QUANTILE APPROACH , 2012, 1203.1144.

[22]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[23]  Rafał Weron,et al.  Efficient estimation of Markov regime-switching models: An application to electricity spot prices , 2012 .

[24]  Robert B. Randall,et al.  Vibration-based Condition Monitoring: Industrial, Aerospace and Automotive Applications , 2011 .

[25]  Robert B. Randall,et al.  Rolling element bearing diagnostics—A tutorial , 2011 .

[26]  Dankmar Böhning,et al.  Asymptotic properties of the EM algorithm estimate for normal mixture models with component specific variances , 2003, Comput. Stat. Data Anal..

[27]  Pl . Teatralny.  Rolling bearing diagnosing method based on Empirical Mode Decomposition of machine vibration signal , 2014 .

[28]  M. Hagberg.  Editorial , 2004 .

[29]  Radoslaw Zimroz,et al.  Signal segmentation for operational regimes detection of heavy duty mining mobile machines – a statistical approach , 2014 .

[30]  N. D. Freitas.  Rao-Blackwellised particle filtering for fault diagnosis , 2002 .

[31]  Okyay Kaynak,et al.  Improved PLS Focused on Key-Performance-Indicator-Related Fault Diagnosis , 2015, IEEE Transactions on Industrial Electronics.

[32]  Radoslaw Zimroz,et al.  A new feature for monitoring the condition of gearboxes in non-stationary operating conditions , 2009 .

[33]  Radoslaw Zimroz,et al.  Procedures for Decision Thresholds Finding in Maintenance Management of Belt Conveyor System – Statistical Modeling of Diagnostic Data , 2015 .

[34]  Radoslaw Zimroz,et al.  A procedure for weighted summation of the derivatives of reflection coefficients in adaptive Schur filter with application to fault detection in rolling element bearings , 2013 .

[35]  Radoslaw Zimroz,et al.  Adaptive Bearings Vibration Modelling for Diagnosis , 2011, ICAIS.

[36]  Shen Yin,et al.  Performance Monitoring for Vehicle Suspension System via Fuzzy Positivistic C-Means Clustering Based on Accelerometer Measurements , 2015, IEEE/ASME Transactions on Mechatronics.