Fault Diagnosis of Rotating Machinery based on the Minutiae Algorithm


  • Shyam Mogal M.V.P.S’s K.B.T. College of Engineering, India
  • Sudhanshu Deshmukh M.V.P.S’s K.B.T. College of Engineering, India
  • Sopan Talekar M.V.P.S’s K.B.T. College of Engineering, India
Volume: 13 | Issue: 5 | Pages: 11649-11654 | October 2023 | https://doi.org/10.48084/etasr.6175


Rotary machinery plays an important role in industry. Combined faults can be observed in rotating machinery, making fault classification difficult. In this paper, the Minutiae algorithm is used to classify the faults from the frequency domain of a particular fault. This paper provides a fault classification technique based on image processing for fault analysis of rotating machinery, recognizing function extraction automatically. Minutiae algorithm, a rising method within the discipline of image processing for characteristic extraction, is utilized in this paper to classify specific faults from the converted recurrence plot. The results reveal the effectiveness of the proposed method, providing a rather powerful tool for fault diagnosis of rotating machinery. The proposed model achieved an accuracy of 100% for combined faults, 98.33% for loosened faults, and 95% for unbalanced faults proving its applicability.


Minutiae algorithm, rotating machinery, fault diagnosis


Download data is not yet available.


Y. Zhang, B. Tang, and X. Xiao, "Time–frequency interpretation of multi-frequency signal from rotating machinery using an improved Hilbert–Huang transform," Measurement, vol. 82, pp. 221–239, Mar. 2016.

H. Pan, Y. Yang, X. Li, J. Zheng, and J. Cheng, "Symplectic geometry mode decomposition and its application to rotating machinery compound fault diagnosis," Mechanical Systems and Signal Processing, vol. 114, pp. 189–211, Jan. 2019.

M. Zhang, Z. Jiang, and K. Feng, "Research on variational mode decomposition in rolling bearings fault diagnosis of the multistage centrifugal pump," Mechanical Systems and Signal Processing, vol. 93, pp. 460–493, Sep. 2017.

H. K. Srinivas, K. S. Srinivasan, and K. N. Umesh, "Application of Artificial Neural Network and Wavelet Transform for Vibration Analysis of Combined Faults of Unbalances and Shaft Bow," Advances in Theoretical and Applied Mechanics, vol. 3, no. 4, pp. 159–176, 2010.

Z. Zhang, Y. Wang, and K. Wang, "Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural network," Journal of Intelligent Manufacturing, vol. 24, no. 6, pp. 1213–1227, Dec. 2013.

W. Deng, S. Zhang, H. Zhao, and X. Yang, "A Novel Fault Diagnosis Method Based on Integrating Empirical Wavelet Transform and Fuzzy Entropy for Motor Bearing," IEEE Access, vol. 6, pp. 35042–35056, 2018.

N. S. Vyas and D. Satishkumar, "Artificial neural network design for fault identification in a rotor-bearing system," Mechanism and Machine Theory, vol. 36, no. 2, pp. 157–175, Feb. 2001.

H. Ma, X. Zhao, Y. Teng, and B. Wen, "Analysis of Dynamic Characteristics for a Rotor System with Pedestal Looseness," Shock and Vibration, vol. 18, no. 1–2, pp. 13–27, 2011.

M. C. S. Reddy and A. S. Sekhar, "Application of Artificial Neural Networks for Identification of Unbalance and Looseness in Rotor Bearing Systems," International Journal of Applied Science and Engineering, vol. 11, no. 1, pp. 69–84, 2013.

G. Chen, Y. Liu, W. Zhou, and J. Song, "Research on intelligent fault diagnosis based on time series analysis algorithm," The Journal of China Universities of Posts and Telecommunications, vol. 15, no. 1, pp. 68–74, Mar. 2008.

C.-C. Wang, Y. Kang, P.-C. Shen, Y.-P. Chang, and Y.-L. Chung, "Applications of fault diagnosis in rotating machinery by using time series analysis with neural network," Expert Systems with Applications, vol. 37, no. 2, pp. 1696–1702, Mar. 2010.

A. Yaşar, A. Keskin, Ş. Yıldızhan, and E. Uludamar, "Emission and vibration analysis of diesel engine fuelled diesel fuel containing metallic based nanoparticles," Fuel, vol. 239, pp. 1224–1230, Mar. 2019.

H. Liu and M. Han, "A fault diagnosis method based on local mean decomposition and multi-scale entropy for roller bearings," Mechanism and Machine Theory, vol. 75, pp. 67–78, May 2014.

Q. Hu, X.-S. Si, Q.-H. Zhang, and A.-S. Qin, "A rotating machinery fault diagnosis method based on multi-scale dimensionless indicators and random forests," Mechanical Systems and Signal Processing, vol. 139, May 2020, Art no. 106609.

R. Yan, R. X. Gao, and X. Chen, "Wavelets for fault diagnosis of rotary machines: A review with applications," Signal Processing, vol. 96, pp. 1–15, Mar. 2014.

H. Ahmed and A. K. Nandi, Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines, 1st ed. Hoboken, NJ, USA: Wiley-IEEE Press, 2020.

V. K. Rai and A. R. Mohanty, "Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert–Huang transform," Mechanical Systems and Signal Processing, vol. 21, no. 6, pp. 2607–2615, Aug. 2007.

S. Fei and X. Zhang, "Fault diagnosis of power transformer based on support vector machine with genetic algorithm," Expert Systems with Applications, vol. 36, no. 8, pp. 11352–11357, Oct. 2009.

S. Khan, I. Ali, F. Ghaffar, and Q. Mazhar-ul-Haq, "Classification of Macromolecules Based on Amino Acid Sequences Using Deep Learning," Engineering, Technology & Applied Science Research, vol. 12, no. 6, pp. 9491–9495, Dec. 2022.

M. Sreenatha and P. B. Mallikarjuna, "A Fault Diagnosis Technique for Wind Turbine Gearbox: An Approach using Optimized BLSTM Neural Network with Undercomplete Autoencoder," Engineering, Technology & Applied Science Research, vol. 13, no. 1, pp. 10170–10174, Feb. 2023.

M. O. Genc and N. Kaya, "Vibration Damping Optimization using Simulated Annealing Algorithm for Vehicle Powertrain System," Engineering, Technology & Applied Science Research, vol. 10, no. 1, pp. 5164–5167, Feb. 2020.


How to Cite

S. Mogal, S. Deshmukh, and S. Talekar, “Fault Diagnosis of Rotating Machinery based on the Minutiae Algorithm”, Eng. Technol. Appl. Sci. Res., vol. 13, no. 5, pp. 11649–11654, Oct. 2023.


Abstract Views: 376
PDF Downloads: 203

Metrics Information

Most read articles by the same author(s)