Machine Learning-based Predictive Maintenance for Fault Detection in Rotating Machinery: A Case Study

Authors

  • Ardalan F. Khalil Department of Mechanical and Manufacturing Engineering, Technical College of Engineering, Sulaimani Polytechnic University, Kurdistan Region, Iraq
  • Sarkawt Rostam Department of Mechanical and Manufacturing Engineering, Technical College of Engineering, Sulaimani Polytechnic University, Kurdistan Region, Iraq https://orcid.org/0000-0002-8403-3840
Volume: 14 | Issue: 2 | Pages: 13181-13189 | April 2024 | https://doi.org/10.48084/etasr.6813

Abstract

In the realm of industrial production, condition monitoring plays a pivotal role in ensuring the reliability and longevity of rotating machinery. Since most of the production facilities rely heavily on vibration analysis, it has become the cornerstone of condition monitoring practices. However, manual analysis of vibration signals is a time-consuming and expertise-intensive task, often requiring specialized domain knowledge. The current research addresses the aforementioned challenges by proposing a novel semi-automated diagnostics system. The approach leverages historical vibration data in the form of Fast Fourier Transform (FFT) spectrums. The system extracts energy features from the frequency domain by dividing the frequency range into a predefined number of bins and summing the energy values within each bin. Subsequently, each datapoint is labeled based on the corresponding machine condition, enabling the system to learn diagnostic patterns by employing machine learning models. This approach facilitates efficient and accurate diagnostics with minimal manual intervention. The resulting dataset effectively represents and provides an interpretable result. Support Vector Machines (SVM), and ensemble algorithms are utilized to diagnose the faults instantaneously and with minimal error rates. The proposed system is capable of providing early warnings and thus prevents further deterioration and unplanned downtimes. Experimental validation using real-world data demonstrates the system's efficacy, achieving an accuracy of over 90%.

Keywords:

condition monitoring, predictive maintenance, FFT, SVM, ensemble

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References

V. G. Salunkhe and R. G. Desavale, "An Intelligent Prediction for Detecting Bearing Vibration Characteristics Using a Machine Learning Model," Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems, vol. 4, no. 3, Feb. 2021, Art. no. 031004.

D. Ganga and V. Ramachandran, "SVM Based Vibration Analysis for Effective Classification of Machine Conditions," in International Congress and Workshop on Industrial AI, Lulea, Sweden, Oct. 2021, pp. 415–423.

J. Vives, "Vibration analysis for fault detection in wind turbines using machine learning techniques," Advances in Computational Intelligence, vol. 2, no. 1, Jan. 2022, Art. no. 15.

Z. Chen, K. Gryllias, and W. Li, "Mechanical fault diagnosis using Convolutional Neural Networks and Extreme Learning Machine," Mechanical Systems and Signal Processing, vol. 133, Nov. 2019, Art. no. 106272.

L. Eren, "Bearing Fault Detection by One-Dimensional Convolutional Neural Networks," Mathematical Problems in Engineering, vol. 2017, Jul. 2017, Art. no. e8617315.

I. I. E. Amarouayache, M. N. Saadi, N. Guersi, and N. Boutasseta, "Bearing fault diagnostics using EEMD processing and convolutional neural network methods," The International Journal of Advanced Manufacturing Technology, vol. 107, no. 9, pp. 4077–4095, Apr. 2020.

M. H. Mohd Ghazali and W. Rahiman, "Vibration Analysis for Machine Monitoring and Diagnosis: A Systematic Review," Shock and Vibration, vol. 2021, Sep. 2021, Art. no. e9469318.

V. Vakharia, V. K. Gupta, and P. K. Kankar, "Ball Bearing Fault Diagnosis using Supervised and Unsupervised Machine Learning Methods," The International Journal of Acoustics and Vibration, vol. 20, no. 4, pp. 244–250, 2015.

X. Chen, B. Zhang, and D. Gao, "Bearing fault diagnosis base on multi-scale CNN and LSTM model," Journal of Intelligent Manufacturing, vol. 32, no. 4, pp. 971–987, Apr. 2021.

Z. Qingbo, J. Han, C. Shi, and H. Gao, "Prediction of Bearing Vibration Fault State based on Fused Bi-LSTM and SVM," Journal of Imaging Science and Technology, vol. 67, no. 4, pp. 1–10, Jul. 2023.

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.

S. Malek, C. Hui, N. Aziida, S. Cheen, S. Toh, and P. Milow, "Ecosystem Monitoring Through Predictive Modeling," in Encyclopedia of Bioinformatics and Computational Biology, S. Ranganathan, M. Gribskov, K. Nakai, and C. Schönbach, Eds. Oxford, UK: Academic Press, 2019, pp. 1–8.

R. Gholami and N. Fakhari, "Support Vector Machine: Principles, Parameters, and Applications," in Handbook of Neural Computation, P. Samui, S. Sekhar, and V. E. Balas, Eds. Cambridge, MA, USA: Academic Press, 2017, pp. 515–535.

Y. Sun, Y. Cao, G. Xie, and T. Wen, "Condition Monitoring for Railway Point Machines Based on Sound Analysis and Support Vector Machine," Chinese Journal of Electronics, vol. 29, no. 4, pp. 786–792, 2020.

S. K. Jalali, H. Ghandi, and M. Motamedi, "Intelligent Condition Monitoring of Ball Bearings Faults by Combination of Genetic Algorithm and Support Vector Machines," Journal of Nondestructive Evaluation, vol. 39, no. 1, Feb. 2020, Art. no. 25.

P. Ilius, M. Almuhaini, M. Javaid, and M. Abido, "A Machine Learning–Based Approach for Fault Detection in Power Systems," Engineering, Technology & Applied Science Research, vol. 13, no. 4, pp. 11216–11221, Aug. 2023.

B. T. Atmaja, H. Ihsannur, Suyanto, and D. Arifianto, "Lab-Scale Vibration Analysis Dataset and Baseline Methods for Machinery Fault Diagnosis with Machine Learning," Journal of Vibration Engineering & Technologies, May 2023.

J. Zhou, M. Xiao, Y. Niu, and G. Ji, "Rolling Bearing Fault Diagnosis Based on WGWOA-VMD-SVM," Sensors, vol. 22, no. 16, Jan. 2022, Art. no. 6281.

W. Zhao, Y. Lv, J. Liu, C. K. M. Lee, and L. Tu, "Early fault diagnosis based on reinforcement learning optimized-SVM model with vibration-monitored signals," Quality Engineering, vol. 35, no. 4, pp. 696–711, Oct. 2023.

I. Lupea and M. Lupea, "Machine Learning Techniques for Multi-Fault Analysis and Detection on a Rotating Test Rig Using Vibration Signal," Symmetry, vol. 15, no. 1, Jan. 2023, Art. no. 86.

M. Pule, O. Matsebe, and R. Samikannu, "Application of PCA and SVM in Fault Detection and Diagnosis of Bearings with Varying Speed," Mathematical Problems in Engineering, vol. 2022, Apr. 2022, Art. no. e5266054.

H. Shi, W. Fu, B. Li, K. Shao, and D. Yang, "Intelligent Fault Identification for Rolling Bearings Fusing Average Refined Composite Multiscale Dispersion Entropy-Assisted Feature Extraction and SVM with Multi-Strategy Enhanced Swarm Optimization," Entropy, vol. 23, no. 5, May 2021, Art. no. 527.

D. Jallepalli and F. Davoudi Kakhki, "Data-Driven Fault Classification Using Support Vector Machines," in International Conference on Intelligent Human Systems Integration, Palermo, Italy, Feb. 2021, pp. 316–322.

J. Guo, X. Liu, S. Li, and Z. Wang, "Bearing Intelligent Fault Diagnosis Based on Wavelet Transform and Convolutional Neural Network," Shock and Vibration, vol. 2020, Nov. 2020, Art. no. e6380486.

L. Liu, L. Chen, Z. Wang, and D. Liu, "Early Fault Detection of Planetary Gearbox Based on Acoustic Emission and Improved Variational Mode Decomposition," IEEE Sensors Journal, vol. 21, no. 2, pp. 1735–1745, Jan. 2021.

P. Li, Y. Jiang, and J. Xiang, "Experimental Investigation for Fault Diagnosis Based on a Hybrid Approach Using Wavelet Packet and Support Vector Classification," The Scientific World Journal, vol. 2014, Feb. 2014, Art. no. e145807.

V. N. Gudivada, M. T. Irfan, E. Fathi, and D. L. Rao, "Cognitive Analytics: Going Beyond Big Data Analytics and Machine Learning," in Handbook of Statistics, V. N. Gudivada, V. V. Raghavan, V. Govindaraju, and C. R. Rao, Eds. New York, NY, USA: Elsevier, 2016, pp. 169–205.

S. Simske, "Introduction, overview, and applications," in Meta-Analytics, Amsterdam, Netherlands: Elsevier, 2019, pp. 1–98.

M. Machoke, J. Mbelwa, J. Agbinya, and A. E. Sam, "Performance Comparison of Ensemble Learning and Supervised Algorithms in Classifying Multi-label Network Traffic Flow," Engineering, Technology & Applied Science Research, vol. 12, no. 3, pp. 8667–8674, Jun. 2022.

Y. Lu, R. Xie, and S. Y. Liang, "CEEMD-assisted kernel support vector machines for bearing diagnosis," The International Journal of Advanced Manufacturing Technology, vol. 106, no. 7, pp. 3063–3070, Feb. 2020.

W. Jiao et al., "Multi-Scale Sample Entropy-Based Energy Moment Features Applied to Fault Classification," IEEE Access, vol. 9, pp. 8444–8454, 2021.

A. Kafeel et al., "An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis," Sensors, vol. 21, no. 22, Jan. 2021, Art. no. 7587.

J. Zhang et al., "Coupling a Fast Fourier Transformation With a Machine Learning Ensemble Model to Support Recommendations for Heart Disease Patients in a Telehealth Environment," IEEE Access, vol. 5, pp. 10674–10685, 2017.

B. Popa, M. Roman, and R. L. Constantinescu, "Fast Fourier processing and real-time transformation system for a dynamic vibration signal," in 20th International Carpathian Control Conference, Krakow-Wieliczka, Poland, Dec. 2019, pp. 1–6.

H.-C. Lin, Y.-C. Ye, B.-J. Huang, and J.-L. Su, "Bearing vibration detection and analysis using enhanced fast Fourier transform algorithm," Advances in Mechanical Engineering, vol. 8, no. 10, Oct. 2016, Art. no. 1687814016675080.

B. Boashash et al., "Advanced time-frequency signal and system analysis," in Time-Frequency Signal Analysis and Processing: A Comprehensive Reference, Amsterdam, Netherlands: Elsevier, 2016, pp. 141–236.

L. F. Chaparro and A. Akan, Signals and Systems Using MATLAB. Cambridge, MA, USA: Academic Press, 2018.

I. El-Thalji and E. Jantunen, "A descriptive model of wear evolution in rolling bearings," Engineering Failure Analysis, vol. 45, pp. 204–224, Oct. 2014.

I. El-Thalji and E. Jantunen, "Dynamic modelling of wear evolution in rolling bearings," Tribology International, vol. 84, pp. 90–99, Apr. 2015.

A. R. Mohanty, Machinery Condition Monitoring: Principles and Practices. Boca Raton, FL, USA: CRC Press, 2014.

Y. Bella, A. Oulmane, and M. Mostefai, "Industrial Bearing Fault Detection Using Time-Frequency Analysis," Engineering, Technology & Applied Science Research, vol. 8, no. 4, pp. 3294–3299, Aug. 2018.

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How to Cite

[1]
A. F. Khalil and S. Rostam, “Machine Learning-based Predictive Maintenance for Fault Detection in Rotating Machinery: A Case Study”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 2, pp. 13181–13189, Apr. 2024.

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