Machinery Fault Detection using Advanced Machine Learning Techniques
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Abstract
Manufacturing industries are expanding rapidly, making it essential to detect early signs of machine faults for safety and productivity. With the extension of machines' runtime due to industrial automation, breakdown risks have increased, leading to economic and productivity consequences and sometimes even causalities. The surge in industrial big data from low-cost sensing technologies has enabled the development of intelligent data-driven Machinery Fault Detection (MFD) systems based on machine learning techniques in recent years. However, most existing methods are based on supervised pattern classification techniques to detect previously known fault types, which have limitations such as lack of generalization across different operational settings, focusing only on specific machinery and/or data types, and considering the identical and independent distribution of training and testing data. Therefore, my PhD research aims to develop a robust MFD framework for practical use by addressing these limitations.I will explore the potential of ensemble learning, unsupervised and semi-supervised anomaly detection, reinforcement learning, transfer learning, and cross-domain adaptation approaches in MFD. My PhD research will contribute to the field of data-driven MFD by proposing novel, effective solutions that can be applied across various manufacturing applications.
How to Cite
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Machinery fault detection, Machine learning, Reinforcement learning, Data fusion, Domain adaptation, Semi-supervised learning
Khan, N. S., & Taib, S. M. (2022). Deep reinforcement
learning for anomaly detection: A systematic review.
IEEE Access, 10, 124017–124035.
Das, O., Das, D. B.,&Birant, D. (2023). Machine learning for
fault analysis in rotating machinery: A comprehensive
review. Heliyon.
Deng, J., Sierla, S., Sun, J., & Vyatkin, V. (2023). Offline
reinforcement learning for industrial process control: A
case study from steel industry. Information Sciences,
632, 221–231.
Hoang, D. T., & Kang, H. J. (2019). A motor current signalbased
bearing fault diagnosis using deep learning and
information fusion. IEEE Transactions on Instrumentation
and Measurement, 69(6), 3325–3333.
Junhuai, L., Yunwen, W., Huaijun, W., & Jiang, X. (2023).
Fault detection method based on adversarial reinforcement
learning. Frontiers in Computer Science, 4,
1007665.
Kibrete, F., Woldemichael, D. E., & Gebremedhen, H. S.
(2024). Multi-sensor data fusion in intelligent fault diagnosis
of rotating machines: A comprehensive review.
Measurement, 114658.
Li,W., Huang, R., Li, J., Liao, Y., Chen, Z., He, G., . . . Gryllias,
K. (2022). A perspective survey on deep transfer
learning for fault diagnosis in industrial scenarios: Theories,
applications and challenges. Mechanical Systems
and Signal Processing, 167, 108487.
Marug´an, A. P. (2023). Applications of reinforcement learning
for maintenance of engineering systems: A review.
Advances in Engineering Software, 183, 103487.
Neupane, D., Kim, Y., & Seok, J. (2021). Bearing fault detection
using scalogram and switchable normalizationbased
cnn (sn-cnn). IEEE Access, 9, 88151-88166. doi:
10.1109/ACCESS.2021.3089698
Neupane, D., & Seok, J. (2020). Bearing fault detection
and diagnosis using case western reserve university dataset with deep learning approaches: A
review. IEEE Access, 8, 93155–93178. Retrieved from
https://ieeexplore.ieee.org/document/9078761
doi: 10.1109/ACCESS.2020.2990528
Nian, R., Liu, J., & Huang, B. (2020). A review on reinforcement
learning: Introduction and applications in
industrial process control. Computers & Chemical Engineering,
139, 106886.
Siraskar, R., Kumar, S., Patil, S., Bongale, A., & Kotecha, K.
(2023). Reinforcement learning for predictive maintenance:
A systematic technical review. Artificial Intelligence
Review, 56(11), 12885–12947.
Teimourzadeh, H., Moradzadeh, A., Shoaran, M.,
Mohammadi-Ivatloo, B., & Razzaghi, R. (2021). High
impedance single-phase faults diagnosis in transmission
lines via deep reinforcement learning of transfer functions.
IEEE Access, 9, 15796-15809. doi: 10.1109/ACCESS.
2021.3051411
Vos, K., Peng, Z., & Wang, W. (2023). Aircraft fleet readiness
optimisation using reinforcement learning: a proof
of concept. In Aiac 2023: 20th australian international
aerospace congress: 20th australian international
aerospace congress (pp. 125–132).
Wang, R., Jiang, H., Li, X.,&Liu, S. (2020). A reinforcement
neural architecture search method for rolling bearing
fault diagnosis. Measurement, 154, 107417.
Zhang, S., Lei, S., Jiefei, G., Ke, L., Lang, Z., & Pecht,
M. (2023). Rotating machinery fault detection and
diagnosis based on deep domain adaptation: A survey.
Chinese Journal of Aeronautics, 36(1), 45–74.
Zhang, S., Ye, F., Wang, B., & Habetler, T. G. (2020).
Few-shot bearing anomaly detection via model-agnostic
meta-learning. In 2020 23rd international conference
on electrical machines and systems (icems) (p. 1341-
1346). doi: 10.23919/ICEMS50442.2020.9291099
Zhong, X., Zhang, L., & Ban, H. (2023, May). Deep
reinforcement learning for class imbalance fault
diagnosis of equipment in nuclear power plants. Annals
of Nuclear Energy, 184, 109685. Retrieved from
https://doi.org/10.1016/j.anucene.2023.109685
doi: 10.1016/j.anucene.2023.109685
Zong, X., Yang, R., Wang, H., Du, M., You, P., Wang,
S., & Su, H. (2022). Semi-supervised transfer
learning method for bearing fault diagnosis with
imbalanced data. Machines, 10(7). Retrieved from
https://www.mdpi.com/2075-1702/10/7/515
doi: 10.3390/machines10070515
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