Prediction of Failures in Aircraft Parts Using Hybrid Machine Learning Algorithm

Authors

  • Jyoti Shekhawat Vivekananda Global University, Jaipur
  • Shoaib Mohammed Department of ISME, ATLAS SkillTech University, Mumbai, Maharashtra, India
  • B. P. Singh Maharishi University of Information Technology, Lucknow, India -226036
  • Hannah Jessie Rani R. JAIN (Deemed-to-be University), Karnataka - 562112, India
  • Shikhar Gupta Chitkara University, Rajpura, Punjab, India

Keywords:

aircraft parts, hybrid machine learning algorithm, Altered Genetic Algorithms (AGA), aviation sector, Min-max normalization

Abstract

In aviation maintenance, ensuring the dependability and safety of aircraft components is crucial. Predicting failures in aviation components can considerably improve maintenance plans, cut downtime, and avert accidents. The aviation sector has a lot of information and maintenance data that might be utilized to forecast future activities and produce useful results. The Hybridized Gradient Random Forest with Modified Support Vector Machine (HGRF-MSVM) choices for features and data removal to anticipate aviation failures in the system is a novel approach presented in this paper for predicting failures in aircraft parts. Over the course of two years, nine participants input and one output variables were collected from aircraft maintenance and failure data painstakingly discovered. To increase the effectiveness of failure count prediction, HGRF-MSVM is suggested. To get rid of noisy or inconsistent data, Min-max normalization is changed for pre-processing. Altered Genetic Algorithms (AGA), attribute assessment feature selection, is employed in the initial step to identify the most and least effective parameters. Real-world aircraft data from a fleet of commercial aircraft is used to validate the HGRF-MSVM. Additionally, the models are assessed using performance metrics including the correlation coefficient (CC), mean absolute error (MAE), and root mean square error (RMSE). The outcomes show that the HGRF-MSVM Prediction equipment failures are successful.

Downloads

Download data is not yet available.

References

Celikmih, K., Inan, O. and Uguz, H., 2020. Failure prediction of aircraft equipment using machine learning with a hybrid data preparation method. Scientific Programming, 2020, pp.1-10.

Dangut, M.D., Skaf, Z. and Jennions, I.K., 2021. An integrated machine learning model for aircraft components rare failure prognostics with log-based dataset. ISA transactions, 113, pp.127-139.

Dangut, M.D., Jennions, I.K., King, S. and Skaf, Z., 2023. A rare failure detection model for aircraft predictive maintenance using a deep hybrid learning approach. Neural Computing and Applications, 35(4), pp.2991-3009.

Dangut, M.D., Skaf, Z. and Jennions, I.K., 2022. Handling imbalanced data for aircraft predictive maintenance using the BACHE algorithm. Applied Soft Computing, 123, p.108924.

Asif, O., Haider, S.A., Naqvi, S.R., Zaki, J.F., Kwak, K.S. and Islam, S.R., 2022. A Deep Learning Model for Remaining Useful Life Prediction of Aircraft Turbofan Engine on C-MAPSS Dataset. IEEE Access, 10, pp.95425-95440.

Ji, S., Han, X., Hou, Y., Song, Y. and Du, Q., 2020. Remaining useful life prediction of airplane engine based on PCA–BLSTM. Sensors, 20(16), p.4537.

Zeng, W., Chu, X., Xu, Z., Liu, Y. and Quan, Z., 2022. Aircraft 4D trajectory prediction in civil aviation: A review. Aerospace, 9(2), p.91.

Basturk, O. and Cetek, C., 2021. Prediction of aircraft estimated time of arrival using machine learning methods. The Aeronautical Journal, 125(1289), pp.1245-1259.

Dangut, M.D., Skaf, Z. and Jennions, I., 2020, February. Rescaled-LSTM for predicting aircraft component replacement under imbalanced dataset constraint. In 2020 Advances in Science and Engineering Technology International Conferences (ASET) (pp. 1-9). IEEE.

Lu, F., Zhou, G., Liu, Y. and Zhang, C., 2022. Ensemble transfer learning for cutting energy consumption prediction of aviation parts towards green manufacturing. Journal of Cleaner Production, 331, p.129920.

Koc, K., Ekmekcioğlu, Ö. and Gurgun, A.P., 2021. Integrating feature engineering, genetic algorithm and tree-based machine learning methods to predict the post-accident disability status of construction workers. Automation in Construction, 131, p.103896.

Wen, J., Zhang, Y., Yang, G., He, Z. and Zhang, W., 2019. Path loss prediction based on machine learning methods for aircraft cabin environments. Ieee Access, 7, pp.159251-159261.

Ponnala, R. and Reddy, C.R.K., 2021. Software defect prediction using machine learning algorithms: Current state of the art. Solid State Technology, 64(2).

Truong, D. and Choi, W., 2020. Using machine learning algorithms to predict the risk of small Unmanned Aircraft System violations in the National Airspace System. Journal of Air Transport Management, 86, p.101822.

Wang, H.K., Cheng, Y. and Song, K., 2021. Remaining useful life estimation of aircraft engines using a joint deep learning model based on TCNN and transformer. Computational Intelligence and Neuroscience, 2021.

Khumprom, P., Grewell, D. and Yodo, N., 2020. Deep neural network feature selection approaches for data-driven prognostic model of aircraft engines. Aerospace, 7(9), p.132.

Liu, L., Song, X. and Zhou, Z., 2022. Aircraft engine remaining useful life estimation via a double attention-based data-driven architecture. Reliability Engineering & System Safety, 221, p.108330.

de Pater, I., Reijns, A. and Mitici, M., 2022. Alarm-based predictive maintenance scheduling for aircraft engines with imperfect Remaining Useful Life prognostics. Reliability Engineering & System Safety, 221, p.108341.

Rodby, K.A., Danielson, K.K., Shay, E., Robinson, E., Benjamin, M. and Antony, A.K., 2016. Trends in breast reconstruction by ethnicity: an institutional review centered on the treatment of an urban population. The American Surgeon, 82(6), pp.497-504.

Xu, Y., Kohtz, S., Boakye, J., Gardoni, P. and Wang, P., 2022. Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges. Reliability Engineering & System Safety, p.108900.

Berghout, T., Mouss, L.H., Kadri, O., Saïdi, L. and Benbouzid, M., 2020. Aircraft engines Remaining Useful Life prediction with an adaptive denoising online sequential Extreme Learning Machine. Engineering Applications of Artificial Intelligence, 96, p.103936.

Sun, J., Zheng, L. and Huang, Y., 2022, April. Research Status and Challenges of Deep Learning-Based Remaining Useful Life Prediction of Equipment. In 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP) (pp. 162-170). IEEE.

Zeng, J. and Cheng, Y., 2020. An ensemble learning-based remaining useful life prediction method for aircraft turbine engine. IFAC-PapersOnLine, 53(3), pp.48-53.

Bock, F.E., Keller, S., Huber, N. and Klusemann, B., 2021. Hybrid Modelling by Machine Learning Corrections of Analytical Model Predictions towards High-Fidelity Simulation Solutions. Materials, 14(8), p.1883.

Tang, J., Liu, G. and Pan, Q., 2021. A review on representative swarm intelligence algorithms for solving optimization problems: Applications and trends. IEEE/CAA Journal of Automatica Sinica, 8(10), pp.1627-1643.

Dangut, M.D., Skaf, Z. and Jennions, I.K., 2021. An integrated machine learning model for aircraft components rare failure prognostics with log-based dataset. ISA transactions, 113, pp.127-139.

Liu, X., Huang, D., Jing, T. and Zhang, Y., 2022. Detection of AC Arc Faults of Aviation Cables Based on HIW Three-Dimensional Features and CNN-LSTM Neural Network. IEEE Access, 10, pp.106958-106971.

Chen, J., Xu, Q., Guo, Y. and Chen, R., 2022. Aircraft Landing Gear Retraction/Extension System Fault Diagnosis with 1-D Dilated Convolutional Neural Network. Sensors, 22(4), p.1367.

Che, C., Wang, H., Fu, Q. and Ni, X., 2019. Combining multiple deep learning algorithms for prognostic and health management of aircraft. Aerospace Science and Technology, 94, p.105423.

Sundar Ganesh, C. S. & Daisy Mae, R. B. (2022). A Study on the Usage of Robot Navigation to Determine Robot’s Own Position in its Frame of Reference and then to Plan a Path towards Some Goal Location. Technoarete Transactions on Industrial Robotics and Automation Systems (TTIRAS). 2(4), 1–6.

Downloads

Published

24.03.2024

How to Cite

Shekhawat, J. ., Mohammed, S. ., Singh, B. P. ., Jessie Rani R., H. ., & Gupta, S. . (2024). Prediction of Failures in Aircraft Parts Using Hybrid Machine Learning Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 12(19s), 766–773. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5208

Issue

Vol. 12 No. 19s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.