Enhancing Fault Classification Accuracy of Ball Bearing Using Central Tendency Based Time Domain Features

Time-domain (TD) statistical features are frequently utilized in vibration-based pattern recognition (PR) models to identify faults in rotating machinery. Presence of possible fluctuations or spikes in random vibration signals can considerably affect the statistical values of the extracted features consequently. This paper discusses the sensitivity of TD features against the fluctuations occurred in vibration signals while classifying localized faults in ball bearing. Based on the sensitivity level, the features are statistically processed prior to employing a classifier in PR model. A central tendency-based feature pre-processing technique is proposed that enhances the diagnostic capability of classifiers by providing appropriate values. The feature processing reduces undesired impact of fluctuations on the diagnostic model. Several classifiers are utilized to evaluate the performance of the proposed method, and the results are evident of its effectiveness. The associated advantage of the feature pre-processing over the conventional pre-processing of raw data is its computational efficiency. It is worth mentioning that only few values in feature distributions are required to be processed rather than dealing with big TD vibration data set.

[1]  Fen Chen,et al.  Fault Diagnosis of Rolling Bearing Based on Wavelet Package Transform and Ensemble Empirical Mode Decomposition , 2013 .

[2]  Fabrizio Angiulli,et al.  Outlier Detection Techniques for Data Mining , 2009, Encyclopedia of Data Warehousing and Mining.

[3]  Anoushiravan Farshidianfar,et al.  Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine , 2007 .

[4]  Hyungdae Lee,et al.  PHM system enhancement through noise reduction and feature normalization , 2010, 2010 IEEE Aerospace Conference.

[5]  Cajetan M. Akujuobi,et al.  An approach to vibration analysis using wavelets in an application of aircraft health monitoring , 2007 .

[6]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

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

[8]  Asoke K. Nandi,et al.  Fault detection using genetic programming , 2005 .

[9]  Satish C. Sharma,et al.  Fault diagnosis of ball bearings using machine learning methods , 2011, Expert Syst. Appl..

[10]  Victoria J. Hodge,et al.  A Survey of Outlier Detection Methodologies , 2004, Artificial Intelligence Review.

[11]  Asoke K. Nandi,et al.  Practical scheme for fast detection and classification of rolling-element bearing faults using support vector machines , 2006 .

[12]  Victor Wowk,et al.  Machinery Vibration: Measurement and Analysis , 1991 .

[13]  Sameer Singh,et al.  Novelty detection: a review - part 1: statistical approaches , 2003, Signal Process..

[14]  V. Sugumaran,et al.  Effect of number of features on classification of roller bearing faults using SVM and PSVM , 2011, Expert Syst. Appl..

[15]  K. R. Al-Balushi,et al.  Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection , 2003 .

[16]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[17]  John W. Tukey,et al.  Exploratory Data Analysis. , 1979 .

[18]  Robert B. Randall,et al.  The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis , 2007 .

[19]  V. Purushotham,et al.  Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition , 2005 .

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

[21]  David L. Woodruff,et al.  Identification of Outliers in Multivariate Data , 1996 .

[22]  Reda Alhajj,et al.  A comprehensive survey of numeric and symbolic outlier mining techniques , 2006, Intell. Data Anal..

[23]  Ron Kohavi,et al.  The Power of Decision Tables , 1995, ECML.

[24]  Ji Zhang,et al.  Advancements of Outlier Detection: A Survey , 2013, EAI Endorsed Trans. Scalable Inf. Syst..

[25]  Matthew J. Watson,et al.  A Comprehensive High Frequency Vibration Monitoring System for Incipient Fault Detection and Isolation of Gears, Bearings and Shafts/Couplings in Turbine Engines and Accessories , 2007 .

[26]  Stefan Ericsson,et al.  Towards automatic detection of local bearing defects in rotating machines , 2005 .

[27]  Pramodita Sharma.  2012 , 2013, Les 25 ans de l’OMC: Une rétrospective en photos.

[28]  Joseph Mathew,et al.  Multiple Band-Pass Autoregressive Demodulation for Rolling-Element Bearing Fault Diagnosis , 2001 .

[29]  Eric Bechhoefer,et al.  Bearing Envelope Analysis Window Selection , 2009 .

[30]  Raymond T. Ng,et al.  Algorithms for Mining Distance-Based Outliers in Large Datasets , 1998, VLDB.

[31]  Wahyu Caesarendra,et al.  Condition monitoring of naturally damaged slow speed slewing bearing based on ensemble empirical mode decomposition , 2013 .

[32]  K. Kroschel,et al.  Applying Bayesian networks to fault diagnosis , 1994, 1994 Proceedings of IEEE International Conference on Control and Applications.

[33]  Shuchita Upadhyaya,et al.  Outlier Detection: Applications And Techniques , 2012 .

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

[35]  Tao Han,et al.  ART–KOHONEN neural network for fault diagnosis of rotating machinery , 2004 .

[36]  K. Loparo,et al.  Bearing fault diagnosis based on wavelet transform and fuzzy inference , 2004 .

[37]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[38]  Sameer Singh,et al.  Novelty detection: a review - part 2: : neural network based approaches , 2003, Signal Process..

[39]  Wilson Wang,et al.  An energy kurtosis demodulation technique for signal denoising and bearing fault detection , 2013 .

[40]  Asoke K. Nandi,et al.  FAULT DETECTION USING SUPPORT VECTOR MACHINES AND ARTIFICIAL NEURAL NETWORKS, AUGMENTED BY GENETIC ALGORITHMS , 2002 .

[41]  N. R. Sakthivel,et al.  Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine , 2011, Expert Syst. Appl..

[42]  B. Samanta,et al.  ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROLLING ELEMENT BEARINGS USING TIME-DOMAIN FEATURES , 2003 .

[43]  J. Antoni.  Fast computation of the kurtogram for the detection of transient faults , 2007 .