Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
DE
EN
Books
Journals
Events
Topic Page
Marketing
Log in
Learn more about individual access
Request company access
13th International Engine Congress 2026 | 24 + 25 February 2026

Meeting Place for the Powertrain and Sustainable Fuels Community

Take part as a speaker in the 13th International Engine Congress 2026, the Meeting Place for the Powertrain, and Sustainable Fuels Community for the exchange of experience. Submit a subject proposal online about our main subject areas: Passenger car engine technology, Commercial vehicle engine technology and Sustainable Fuels & Energy!
EXTENDED SEARCH
Log in
Top
Published in:
Read chapter Read first chapter

2024 | OriginalPaper | Chapter

Automl-Based Predictive Maintenance Model for Accurate Failure Detection

Authors : Elif Cesur, M. Raşit Cesur, Şeyma Duymaz

Published in: Advances in Intelligent Manufacturing and Service System Informatics

Publisher: Springer Nature Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

This chapter delves into the application of Automated Machine Learning (AutoML) for predictive maintenance, focusing on accurate failure detection in machines. By leveraging the Digital Twin concept, the study aims to predict and prevent machine malfunctions before they occur. The research compares two prominent AutoML methods, TPOT and Lazypredict, using a dataset with five factors impacting machine failure. The results demonstrate the effectiveness of AutoML in selecting the best algorithms and parameters for predictive maintenance, contributing to the development of more efficient maintenance policies. The chapter also highlights the potential of AutoML in enhancing predictive maintenance strategies, making it a valuable resource for professionals seeking to optimize industrial operations.
AI Generated

This summary of the content was generated with the help of AI.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Blockchain Enabled Lateral Transshipment System for the Redistribution of Unsold Textile Products in a Circular Economy
next chapter An Intelligent System Proposal for Providing Driving Data for Autonomous Drive Simulations
Literature
1.
Wang, Y.G., Chen, Y., Zhao, Y.W.: Chemical mechanical planarization of silicon wafers at natural pH for green manufacturing. Int. J. Precis. Eng. Manuf. 16(9), 2049–2054 (2015)CrossRef
2.
Carvalho, T., Soares, F., Vita, R., Francisco, R., Basto, J., Alcalá, S.: A systematic literature review of machine learning methods applied to predictive maintenance. Comput. Ind. Eng. 137, 106024 (2019)
3.
Panda, C., Singh, T.R.: ML-based vehicle downtime reduction: a case of air compressor failure detection. Eng. Appl. Artif. Intell. 122, 106031 (2023)CrossRef
4.
Dos Santos, T., Ferreira, F.J.T.E., Pires, J.M., Damasio, C.: Stator winding short-circuit fault diagnosis in induction motors using random forest. In: 2017 IEEE International Electric Machines and Drives Conference, IEEE, Miami, FL, USA (2017)
5.
Uhlmann, E., Pontes, R.P., Geisert, C., Hohwieler, E.: Cluster identification of sensor data for predictive maintenance in a selective laser melting machine tool. Procedia Manuf. 24, 60–65 (2018)CrossRef
6.
Guo, F., Rasmussen, B.: Performance benchmarking of residential air conditioning systems using smart thermostat data. Appl. Therm. Eng. 225, 120195 (2023)CrossRef
7.
Dangut, M.D., Jennions, I.K., King, S., Skaf, Z.: A rare failure detection model for aircraft predictive maintenance using a deep hybrid learning approach. Neural Comput. Appl. 35(4), 2991–3009 (2023)CrossRef
8.
Shaheen, B., Kocsis, Á., Németh, I.: Data-driven failure prediction and RUL estimation of mechanical components using accumulative artificial neural networks. Eng. Appl. Artif. Intell. 119, 105749 (2023)
9.
Einabadi, B., Baboli, A., Ebrahimi, M.: Dynamic predictive maintenance in industry 4.0 based on real time information: case study in automotive industries. IFAC 52(13), 1069–1074 (2019)
10.
Dangut, M.D., Skaf, Z., Jennions, I.K.: An integrated machine learning model for aircraft components rare failure prognostics with log-based dataset. ISA Trans. 113, 127–139 (2021)CrossRef
11.
Calabrese, M., et al.: SOPHIA: an event-based IoT and machine learning architecture for predictive maintenance in industry 4.0. Information 11(4), 202 (2020)
12.
Vincent, A.M., Jidesh, P.: An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms. Sci. Rep. 13(1), 4737 (2023)
13.
Škrlj, B., Bevec, M., Lavrač, N.: Multimodal AutoML via representation evolution. Mach. Learn. Knowl. Extr. 5(1), 1–13 (2022)CrossRef
14.
Sahin, E.K., Demir, S.: Greedy-AutoML: a novel greedy-based stacking ensemble learning framework for assessing soil liquefaction potential. Eng. Appl. Artif. Intell. 119, 105732 (2023)
15.
Raj, R., Mathew, J., Kannath, S.K., Rajan, J.: StrokeViT with AutoML for brain stroke classification. Eng. Appl. Artif. Intell. 119, 105772 (2023)
16.
Ferreira, L., Pilastri, A., Romano, F., Cortez, P.: Using supervised and one-class automated machine learning for predictive maintenance. Appl. Soft Comput. 131, 109820 (2022)
17.
Cinar, E., Kalay, S., Saricicek, I.: A predictive maintenance system design and implementation for intelligent manufacturing. Machines 10(11), 1006 (2022)
18.
Rivas, J., Boya-Lara, C., Poveda, H.: Partial discharge detection in power lines using automated machine learning. In: Proceedings - 2022 8th International Engineering, Sciences and Technology Conference, pp. 223–230, IESTEC (2022)
19.
Kocbek, S., Gabrys, B.: Automated machine learning techniques in prognostics of railway track defects. In: IEEE International Conference on Data Mining Workshops, pp. 777–784, ICDMW (2019)
Metadata
Title
Automl-Based Predictive Maintenance Model for Accurate Failure Detection
Authors
Elif Cesur
M. Raşit Cesur
Şeyma Duymaz
Copyright Year
2024
Publisher
Springer Nature Singapore
Book
Advances in Intelligent Manufacturing and Service System Informatics

Print ISBN: 978-981-9960-61-3

Electronic ISBN: 978-981-9960-62-0

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-981-99-6062-0_59

Premium Partners

    Image Credits