Non-Stationary Vibratory Signatures Bearing Fault Detection Using Alternative Novel Kurtosis-based Statistical Analysis

  • Nur Adilla Kasim Department of Mechanical Engineering, Politeknik Mukah, Mukah, Malaysia
  • Mohd Ghafran Mohamed Department of Mechanical Engineering, Politeknik Mukah, Mukah, Malaysia
  • Mohd Zaki Nuawi Department of Mechanical and Material Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Malaysia
Keywords: Rotating machinery, Vibration signal, Bearing fault, statistical analysis, Z-rotation method


Vibration signature-based analysis to detect and diagnose is the commonly used technique in the monitoring of rotating machinery. Reliable features will determine the efficacy of diagnosis and prognosis results in the field of machine condition monitoring. This study intends to produce a reliable set of signal features through an alternative statistical characteristic before available relevant prediction methods. Given the above advantage of Kurtosis, a newly formed feature extraction analysis is adapted to extract a single coefficient out of EMD-based pre-processing vibration signal data for bearing fault detection monitoring. Each set of IMFs data is analyzed using the Z-rotation method to extract the data coefficient. Afterwards, the Z-rot coefficients, RZ are presented on the base of the specification of the defect vibratory signal to observe which IMF data set has the highest correlation over the specification given. Throughout the analysis studies, the RZ shows some significant non-linearity in the measured impact. For that reason, the Z-rotation method has effectively determined the strong correlation that existed in some of the IMFs components of the bearing fault. It corresponds to the first IMF for the inner race and the rolling ball specified a strong RZ coefficient with the highest correlation coefficient of R2 = 0.9653 (1750 rpm) and R2 = 0.9518 (1772 rpm), respectively. Whereas, the 4th IMF decomposition for the outer race bearing fault scored is R2 = 0.8865 (1772 rpm). Meanwhile, the average R-squared score in the correlation between RZ coefficient and bearing fault throughout the study is R2 = 0.8915. Thus, it can be utilized to be the alternative feature extraction findings for monitoring bearing conditions.


Fenineche, H., Felkaoui, A., & Rezig, A. (2019). Effect of input data on the neural networks performance applied in bearing fault diagnosis. Rotating Machinery and Signal Processing (pp. 34–43). Springer International Publishing.

Patil, M. S., Mathew, J., & RajendraKumar, P. K. (2007). Bearing signature analysis as a medium for fault detection: A review. Journal of Tribology, 130(1).

Orhan, S., Aktürk, N., & Çelik, V. (2006). Vibration monitoring for defect diagnosis of rolling element bearings as a predictive maintenance tool: Comprehensive case studies. NDT & E International, 39(4), 293–298.

Williams, t., Ribadeneira, x., Billington, s., & Kurfess, t. (2001). Rolling element bearing diagnostics in run-to-failure lifetime testing. Mechanical Systems and Signal Processing, 15(5), 979–993.

Pham, D. T., & Oztemel, E. (1996). Condition monitoring and fault diagnosis. Intelligent Quality Systems (pp. 167–191). Springer London.

Dyer, D., & Stewart, R. M. (1978). Detection of rolling element bearing damage by statistical vibration analysis. Journal of Mechanical Design, 100(2), 229–235.

El-Thalji, I., & Jantunen, E. (2014). A descriptive model of wear evolution in rolling bearings. Engineering Failure Analysis, 45, 204–224.

Zhou, S., Xiao, M., Bartos, P., Filip, M., & Geng, G. (2020). Remaining useful life prediction and fault diagnosis of rolling bearings based on short-time fourier transform and convolutional neural network. Shock and Vibration, 2020, 8857307.

Su, Y.-T., & Lin, S.-J. (1992). On initial fault detection of a tapered roller bearing: Frequency domain analysis. Journal of Sound and Vibration, 155(1), 75–84.

Kuo, R. ., & Cohen, P. . (1999). Multi-sensor integration for on-line tool wear estimation through radial basis function networks and fuzzy neural network. Neural Networks, 12(2), 355–370.

Liu, J., Teng, G., & Hong, F. (2020). Human activity sensing with wireless signals: A survey. Sensors, 20(4).

Jia, F., Lei, Y., Lin, J., Zhou, X., & Lu, N. (2016). Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mechanical Systems and Signal Processing, 72–73, 303–315.

Zhang, X., Feng, N., Wang, Y., & Shen, Y. (2015). Acoustic emission detection of rail defect based on wavelet transform and Shannon entropy. Journal of Sound and Vibration, 339, 419–432.

Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert Spectrum for nonlinear and non-stationary Time Series analysis. Proceedings: Mathematical, Physical and Engineering Sciences, 454(1971), 903–995.

Krishnakumar, P., Rameshkumar, K., & Ramachandran, K. I. (2015). Tool Wear Condition prediction using vibration signals in high speed machining (HSM) of titanium (Ti-6Al-4V) alloy. Procedia Computer Science, 50, 270–275.

Kasim, N. A., Nuawi, M., Ghani, J., Rizal, M., Ahmad, M. A. F., & Haron, C. (2019). Cutting tool wear progression index via signal element variance. Journal of Mechanical Engineering and Sciences, 13, 4596–4612.

Abellan-Nebot, J. V., & Romero Subirón, F. (2010). A review of machining monitoring systems based on artificial intelligence process models. The International Journal of Advanced Manufacturing Technology, 47(1), 237–257.

Martin, H. R., & Honarvar, F. (1995). Application of statistical moments to bearing failure detection. Applied Acoustics, 44(1), 67–77.

Ait-Amir, B., Pougnet, P., & El Hami, A. (2015). 6 - Meta-Model Development. In A. El Hami & P. Pougnet (Eds.), Embedded Mechatronic Systems 2 (pp. 151–179). Elsevier.

Nuawi, M. Z., Nor, M. J. M., Jamaludin, N., Abdullah, S., Lamin, F., & Nizwan, C. K. E. (2008). Development of integrated Kurtosis-based algorithm for Z-filter technique. Journal of Applied Sciences, 8(8), 1541–1547.

How to Cite
Kasim, N. A., Mohamed, M. G., & Nuawi, M. Z. (2022). Non-Stationary Vibratory Signatures Bearing Fault Detection Using Alternative Novel Kurtosis-based Statistical Analysis. Journal of Applied Science & Process Engineering, 9(1), 1139-1148.