Rule-based Identification of Bearing Faults using Central Tendency of Time Domain Features

Muhammad Masood Tahir, Ayyaz Hussain, Saeed Badshah, Qaisar Javaid

Abstract


Vibration-based time domain features (TDFs) are commonly used to recognize patterns of machinery faults. This study exploits central tendency (CT) of TDFs to develop a Rule-based Diagnostic Scheme (RDS), which identifies localized faults in ball bearing. The RDS offers an accurate and efficient diagnostic procedure, and purges the requirement of expensive training of conventional classifier. A test rig is used to acquire vibration data from bearings having localized faults, and various TDFs are extracted. It is worth mentioning that fluctuations in random vibration signals may alter the feature values. Therefore, each of the TDFs is processed statistically to approximate its reliable central values (CVs) against the respective faults. In this way, every feature provides a set of CVs, which are equal in number to that of faults. Separating distances among normalized CVs (NCVs) in a set provide the criteria to select or discard that particular feature before further processing. The selected sets of NCVs are finally used as references to generate rule-set for testing the unknown vibration samples. The results are evident that the proposed RDS may be an effective alternative to the existing classifier-based fault diagnosis, even if the vibration signals are contaminated with considerable background noise.

Keywords


Rule-based diagnostics; Feature processing; Fault diagnosis; Central tendency; Time domain features

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References


Randall, R. B., 2011. "Vibration-based condition monitoring : industrial, aerospace and automotive applications". Wiley.

Wowk, V., 1991. "Machinery Vibration: Measurement and Analysis". McGraw-Hill Education.

Sawalhi, N., Randall, R. and Endo, H., 2007. "The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis". Mechanical Systems and Signal Processing 21: 2616-2633.

Wang, W. and Lee, H., 2013. "An energy kurtosis demodulation technique for signal denoising and bearing fault detection". Measurement Science and Technology 24: 025601.

Lou, X. and Loparo, K. A., 2004. "Bearing fault diagnosis based on wavelet transform and fuzzy inference". Mechanical Systems and Signal Processing 18: 1077-1095.

Smith, C., Akujuobi, C. M., Hamory, P. and Kloesel, K., 2007. "An approach to vibration analysis using wavelets in an application of aircraft health monitoring". Mechanical Systems and Signal Processing 21: 1255-1272.

Purushotham, V., Narayanan, S. and Prasad, S. A., 2005. "Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden markov model based fault recognition". NDT and E International 38: 654-664.

Altmann, J. and Mathew, J., 2001. "Multiple bandpass autoregressive demodulation for rolling-element bearing fault diagnosis". Mechanical Systems and Signal Processing 15: 963- 977.

Abbasion, S., Rafsanjani, A., Farshidianfar, A. and Irani N., 2007. "Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine". Mechanical Systems and Signal Processing 21: 2933- 2945.

Randall, R., Antoni, J. and Sawalhi, N., 2004. "Application of spectral kurtosis to bearing fault detection in rolling element bearings". 11th International Congress Sound and Vibration(ICSV11).

Young-Chul, C. and Yang-Hann, K., 2007. "Fault detection in a ball bearing system using minimum variance cepstrum". Measurement Science and Technology 18: 1433-1440.

Trendafilova, I., 2010. "An automated procedure for detection and identification of ball bearing damage using multivariate statistics and pattern recognition". Mechanical Systems and Signal Processing 24: 1858-1869.

Rauber, T. W., do Nascimento, E. M., Wandekokem, E. D. and Varejo, F. M., 2010. "Pattern Recognition based Fault Diagnosis in Industrial Processes: Review and Application". InTech.

Ericsson, S., Grip, N., Johansson, E., Persson, L.-E., Sjoberg, R. and Stromberg, J.-O., 2005. "Towards automatic detection of local bearing defects in rotating machines". Mechanical systems and signal processing 19: 509-535.

Lazzerini, B. and Volpi, S., 2013. "Classifier ensembles to improve the robustness to noise of bearing fault diagnosis". Pattern Analysis and Applications 16: 235-251.

Samanta, B., Al-Balushi, K. and Al-Araimi, S., 2003. "Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection". Engineering Applications of Artificial Intelligence 16: 657- 665.

Jack, L. and Nandi, A., 2002. "Fault detection using suppoet vector machines and artificial neural networks, augmented by genetic algorithms". Mechanical Systems and Signal Processing 16: 373-390.

Rojas, A. and Nandi, A. K., 2006. "Practical scheme for fast detection and classification of rolling-element bearing faults using support vector machines". Mechanical Systems and Signal Processing 20: 1523-1536.

Samanta, B. and Al-Balushi, K., 2003. "Artificial neural network based fault diagnostics of rolling element bearings using time-domain features". Mechanical Systems and Signal Processing 17: 317-328.

Yang, B. S., Han, T. and An, J. L.,2004. "ARTKOHONEN neural network for fault diagnosis of rotating machinery". Mechanical Systems and Signal Processing 18: 645-657.

Zhang, L., Jack, L. B. and Nandi, A. K., 2005. "Fault detection using genetic programming". Mechanical Systems and Signal Processing 19: 271-289.

Sugumaran, V. and Ramachandran, K. I., 2007. "Automatic rule learning using decision tree for fuzzy classifier in fault diagnosis of roller bearing". Mechanical Systems and Signal Processing 21: 2237-2247.

Kankar, P., Sharma, S. C. and Harsha, S., 2011. "Fault diagnosis of ball bearings using machine learning methods". Expert Systems with Applications 38: 1876-1886.

Sugumaran, V. and Ramachandran, K. I., 2011. "Effect of number of features on classification of roller bearing faults using SVM and PSVM". Expert Systems with Applications 38: 4088-4096.

Saimurugan, M., Ramachandran, K. I., Sugumaran, V. and Sakthivel, N. R., 2011. "Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine". Expert Systems with Applications 38: 3819-3826.

Antoni, J., 2005. "Matlab code to compute signal’s fast kurtogram". Accessed online on 30th June, 2014. https://github.com/amaggi/seismokurt/blob/master/originals/matlab/Fast Kurtogram.m

Patil, M., Mathew, J. and RajendraKumar, P., 2008. "Bearing signature analysis as a medium for fault detection: a review". Journal of Tribology 130: 014001.

Vigya and Ghose, Tirthadip, 2016. "Artificial Intelligence and Evolutionary Computations in Engineering Systems: Chapter- Filtration of Noise in Images Using Median Filter". Springer.

Li, B., Chow, M.-Y., Tipsuwan, Y. and Hung J., 2000. "Neural-network-based motor rolling bearing fault diagnosis". IEEE Transactions on industrial electronics 47: 1060–1069


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ISSN: 1023-862X  & eISSN:2518-4571