DATA-DRIVEN DIAGNOSTIC MODEL BUILDING FOR HELICOPTER GEAR HEALTH AND USAGE MONITORING
DOI:
https://doi.org/10.15588/1607-3274-2025-1-11Keywords:
data-driven diagnosis, data dimensionality reduction, classification, health and usage monitoring systemAbstract
Context. Modern technical objects (in particular vehicles) are extremely complex and place high demands on reliability. This requires automation of condition monitoring and fault diagnosis of objects and their components. The predictive maintenance improves operational readiness of technical objects. The object of study is a technical object health and usage monitoring process. The subject of study is a methods of computational intelligence for data-driven model building and related data processing tasks for health and usage monitoring system.
Objective. The purpose of the work is to formulate data processing problems, to form a data set for data-driven model building and construct simple method for automatic diagnostic model building on example of helicopter health and usage monitoring system.
Method. The method is proposed for the mapping of multidimensional data into a two-dimensional space preserving local properties of class separation, allowing for the visualization of multidimensional data and the production of simple diagnostic models for the automatic classification of diagnostic objects. The proposed method allows obtaining highly accurate diagnostic model with small training samples, provided that the frequency of classes in the samples is preserved. A method for synthesizing diagnostic models based on a two-layer feed-forward neural network is also proposed, which allows obtaining models in a non-iterative mode.
Results. A sample of observations of the state of helicopter gears was obtained, which can be used to compare data-driven diagnostic methods and data processing methods that solve the problems of data dimensionality reduction. The Software has been developed that allows displaying a sample from a multidimensional to a two-dimensional space, which makes it possible to visualize data and reduces the dimensionality of the data. Diagnostic models have been obtained that allow automating the decision-making process on whether the diagnosed object (helicopter gear) belongs to one of two classes of states.
Conclusions. The results of conducted experiments allow to conclude that the proposed method provides a significant reduction in the data dimensionality (in particular, for the considered problem of constructing a model for helicopter gear diagnosis, it reduces
the data dimensionality due to the compression of features by 46876 times). As the results of the conducted experiments for randomly selected instances in a two-dimensional system of artificial features obtained on the basis of the proposed method showed a significant reduction of the sample for individual tasks may allow to provide acceptable accuracy. And taking into account individual estimates of the instance significance will allow, even for small samples, to ensure the topological representativeness of the formed sample in relation to the original sample. The prospects for further research are to compare methods for constructing data-driven models, as well as methods for reducing the dimensionality of data based on the proposed sample. Additionally, it may be of interest to study a possible combination of the
proposed method with methods for sample forming using metrics of the value of instances.
References
Land J. E. HUMS – The benefits – Past, present and future, 2001 IEEE Aerospace Conference, 10–17 March 2001, Big Sky : proceedings. Los Alamitos, IEEE, 2001, Vol. 6, pp. 3083–3094.
DOI: 10.1109/AERO.2001.931326
Bechhoefer E., Butterworth B. A comprehensive analysis of the performance of gear fault detection algorithms [Electronic resource], Proceedings of the Annual Conference of the PHM Society, 2019, Vol. 11, № 1:). Mode of Access: https://papers.phmsociety.org/index.php/phmconf/article/view/823
Giurgiutiu V., Cuc A., Goodman P. Review of vibrationbased helicopters health and usage monitoring methods [Electronic resource], 55th Meeting of the Society for Machinery Failure Prevention Technology, Virginia Beach, 2–5 April 2001 : proceedings, pp. 1–10. Mode of
Access: https://apps.dtic.mil/sti/tr/pdf/ADP013516.pdf
Draper A., Gourlay J. The operational benefits of health and usage monitoring systems in UK military helicopters [Electronic resource], HUMS 2003 Conference, pp. 71–79. Mode of Access: https://humsconference.com.au/Papers2003/HUMSp408.pdf
Simani S., Fantuzzi C., Patton R.J. Model-based fault diagnosis in dynamic systems using identification techniques. London, Springer, 2013, 282 p. DOI: 10.1007/978-1-4471-3829-7
Wen G., Chen X., Lei Z., Huang X. New generation artificial intelligence-driven diagnosis and maintenance techniques. Advanced machine learning models, methods and applications. Singapore, Springer Nature, 2024, 349 p.
Engelbrecht A. Computational intelligence: an introduction. Sidney, John Wiley & Sons, 2007, 597 p. DOI: 10.1002/9780470512517
Kruse S., Plattner H. Efficient Discovery of Approximate Dependencies / S. Kruse, // Proceedings of the VLDB Endowment. – 2018. – Vol. 11. – No. 7. – P. 759– 772.
DOI: 10.14778/3192965.3192968
Subbotin S. A., Oliinyk A. A. The dimensionality reduction methods based on computational intelligence in problems of object classification and diagnosis, Advances in Intelligent Systems and Computing, 2017, Vol. 543, pp. 11– 19. DOI: https://doi.org/10.1007/978-3-319-48923-0_2
Subbotin S.A. Experimental investigation and analysis of information quality indices correlation for diagnostic neuromodels, Radio Electronics, Computer Science, Control, 2011, № 1, pp. 104–110. DOI: 10.15588/1607-3274-2011-1-19
Subbotin S. The quality indicators of decision tree and forest based models, Computer Modeling and IntelligentWorkshop on. Zaporizhzhia, Ukraine, April 27-May 1, 2020 /ed.: S. Subbotin : proceedings, Aachen, CEUR-WS, 2020, pp. 718–743. (CEUR-WS.org, vol. 2608). Access mode: http://ceur-ws.org/Vol-2608/paper55.pdf
Webb A. R. Statistical pattern recognition. Chichister, Wiley, 2003, 514 p.
Berezsky O., Zarichnyi M. Metric methods in computer vision and pattern recognition, Advances in Intelligent Systems and Computing, 2020, Vol. 1293. Cham, Springer, 2020, pp. 188–209. DOI: 10.1007/978-3-030-63270-0_13
Subbotin S. The neuro-fuzzy network synthesis and simplification on precedents in problems of diagnosis and pattern recognition, Optical Memory and Neural Networks (Information Optics), 2013, Vol. 22, № 2, pp. 97–103. DOI: 10.3103/s1060992x13020082
Kazlas P. T., Monsen P. T., LeBlanc M. J. Neural network-based helicopter gearbox health monitoring system, IEEE-SP Workshop on Neural Networks for Signal Processing, Linthicum Heights, 6–9 September 1993 : proceedings.Los Alamitos: IEEE, 1993, pp. 431–440.
DOI:10.1109/NNSP.1993.471845
d’Amato C., Bryl V., Serafini. L. Data-driven logical reasoning [Electronic resource]. Mode of Access: https://ceur-ws.org/Vol-900/paper5.pdf
Lamperti G., Zanella M. Rule-based diagnosis, Diagnosis of Active Systems, Vol 741, pp. 193–233. DOI: https://doi.org/10.1007/978-94-017-0257-7_7
Pétrowski A., Ben-Hamida S. Evolutionary algorithms. Chichester, Wiley, 2017, 256 p.
Eds: A. Kumar, P. Rathore, R. Agrawal, Diaz V. Swarm Intelligence Optimization. Algorithms and Applications. Chichester, Wiley, 2021,384 p.
Rao S. Engineering optimization. Theory and practice. Chichester,·Wiley, 2019, 832 p.
Lee J. A., Verleysen M. Nonlinear dimensionality reduction. New York, Springer, 2007, 309 p. DOI: 10.1007/978-0-387-39351-3
Jensen R., Shen Q. Computational intelligence and feature selection: rough and fuzzy approaches. Hoboken: John Wiley & Sons, 2008, 339 p. DOI: 10.1002/9780470377888
Guyon I., Elisseeff A. An introduction to variable and feature selection, Journal of machine learning research, 2003. Vol. 3, pp. 1157–1182.
Eds: Motoda H., Liu H. Feature extraction, construction and selection. A data mining perspective. New York, Springer, 2012, 410 p.
Zheng A., Casari A. Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists. Sebastopol, CA: O’Reilly, 2018, 215 p.
Indyk P., Motwani R., Raghavan P., Vempala S. Locality-preserving hashing in multidimensional spaces, Twenty-ninth annual ACM symposium on theory of computing
(STOC '97) : proceedings. New York, ACM, 1997, pp. 618–625. DOI:10.1145/258533.258656.
Subbotin, S. A. Evaluation of informativity and selection of instances based on hashing, Radio Electronics, Computer Science, Control, 2020, № 3, pp. 129–137.
DOI: 10.15588/1607-3274-2020-3-12
Subbotin S. A. Methods of sampling based on exhaustive and evolutionary search, Automatic Control and Computer Sciences, 2013, Vol. 47, № 3, pp. 113–121.
DOI:10.3103/s0146411613030073
Jankowski N., Grochowski M. Comparison of instance selection algorithms I. Algorithms survey, Artificial Intelligence and Soft Computing : 7th International Conference ICAISC-2004, Zakopane, 7–11 June, 2004 : proceedings.– Berlin : Springer, 2004, pp. 598–603. (Lecture
Notes in Computer Science, Vol. 3070). DOI: 10.1007/978-3-540-24844-6_90
Tan L., Jiang J. Digital signal processing. fundamentals and applications. London, Elsevier/Academic Press, 2018, 920 p.
Lymariev I., Subbotin S., Oliinyk A., Drokin I. Diagnostic signal nonstationarity reduction to predict the helicopter transmission state on the basis of intelligent information
technologies, The Second International Workshop on Computer Modeling and Intelligent Systems (CMIS-2019), Zaporizhzhia, 15–19 of April 2019 : proceedings. Mode of Access: . https://ceur-ws.org/Vol-2353/paper40.pdf
Lymariev I. O., Subbotin S. A., Oliinyk A. A., Drokin I.V. Methods of large-scale signals transformation for diagnosis in neural network models, Radio Electronics, Computer Science, Control, 2018, Vol. 4, pp. 63–71. DOI: 10.15588/1607-3274-2018-4-11
Gao R. X., Yan R.. Wavelets. Theory and applications for manufacturing. New York, Springer, 2010. – 224 p. DOI: 10.1007/978-1-4419-1545-0
Scitovski R., Sabo K., Martínez-Álvarez F., Ungar Š. Cluster analysis and applications. Cham, Springer, 2021, 271 p. DOI: 10.1007/978-3-030-74552-3
Hurter C. Image-based visualization. Interactive multidimensional data exploration. Cham,· Springer, 2022,111p.
Haykin S. Neural networks and learning machines. Hoboken, Prentice Hall, 2009, 906 p.
Rumelhart D., Hinton G., Williams R. Learning representations by back-propagating errors, Nature, 1986, Vol. 323, pp. 533–536. DOI:10.1038/323533a0
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 S. A. Subbotin, E. Bechhoefer
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Creative Commons Licensing Notifications in the Copyright Notices
The journal allows the authors to hold the copyright without restrictions and to retain publishing rights without restrictions.
The journal allows readers to read, download, copy, distribute, print, search, or link to the full texts of its articles.
The journal allows to reuse and remixing of its content, in accordance with a Creative Commons license СС BY -SA.
Authors who publish with this journal agree to the following terms:
-
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License CC BY-SA that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
-
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
-
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
