IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i17p5496-d628376.html
   My bibliography  Save this article

A Method for Structure Breaking Point Detection in Engine Oil Pressure Data

Author

Listed:
  • Aleksandra Grzesiek

    (Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Na Grobli 15, 50-421 Wroclaw, Poland)

  • Radosław Zimroz

    (Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Na Grobli 15, 50-421 Wroclaw, Poland)

  • Paweł Śliwiński

    (KGHM Polska Miedź S.A., M. Skłodowskiej-Curie 48, 59-301 Lubin, Poland)

  • Norbert Gomolla

    (DMT GmbH & Co. KG, Am Technologiepark 1, 45307 Essen, Germany)

  • Agnieszka Wyłomańska

    (Faculty of Pure and Applied Mathematics, Hugo Steinhaus Center, Wrocław University of Science and Technology, Wyspiańskiego 27, 50-370 Wrocław, Poland)

Abstract

In this paper, a heavy-duty loader operated in an underground mine is discussed. Due to extremely harsh operational conditions, an important maintenance problem is related to engine oil pressure. We have found that when the degradation process appears, the nature of variation of pressure engine oil changes. Following this observation, we have proposed a data analysis procedure for the structure break point detection. It is based on specific data pre-processing and further statistical analysis. The idea of the paper is to transform the data into a nearly monotonic function that describes the variation of machine condition or in the statistical language—change of the regime inside the process. To achieve that goal we proposed an original data processing procedure. The dataset analyzed in the paper covers one month of observation. We have received confirmation that during that period, maintenance service has been done. The purpose of our research was to remove ambiguity related to direct oil pressure analysis and visualize oil pressure variation in the diagnostic context. As a fleet of machines in the considered company covers more than 1000 loaders/trucks/drilling machines, the importance of this approach is serious from a practical point of view. We believe that it could be also an inspiration for other researchers working with industrial data.

Suggested Citation

  • Aleksandra Grzesiek & Radosław Zimroz & Paweł Śliwiński & Norbert Gomolla & Agnieszka Wyłomańska, 2021. "A Method for Structure Breaking Point Detection in Engine Oil Pressure Data," Energies, MDPI, vol. 14(17), pages 1-24, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:17:p:5496-:d:628376
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/17/5496/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/17/5496/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kucharczyk, Daniel & Wyłomańska, Agnieszka & Zimroz, Radosław, 2017. "Structural break detection method based on the Adaptive Regression Splines technique," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 499-511.
    2. Zhao, Hongshan & Liu, Huihai & Hu, Wenjing & Yan, Xihui, 2018. "Anomaly detection and fault analysis of wind turbine components based on deep learning network," Renewable Energy, Elsevier, vol. 127(C), pages 825-834.
    3. Thorsten Wagner & Alexandra Kroll & Chandrashekara R Haramagatti & Hans-Gerd Lipinski & Martin Wiemann, 2017. "Classification and Segmentation of Nanoparticle Diffusion Trajectories in Cellular Micro Environments," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-20, January.
    4. Saari, Juhamatti & Odelius, Johan, 2018. "Detecting operation regimes using unsupervised clustering with infected group labelling to improve machine diagnostics and prognostics," Operations Research Perspectives, Elsevier, vol. 5(C), pages 232-244.
    5. Joanna Janczura & Rafał Weron, 2013. "Goodness-of-fit testing for the marginal distribution of regime-switching models with an application to electricity spot prices," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(3), pages 239-270, July.
    6. Arto Laukka & Juhamatti Saari & Jari Ruuska & Esko Juuso & Sulo Lahdelma, 2016. "Condition-based monitoring for underground mobile machines," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 23(1), pages 74-89.
    7. Yonglong Yan & Jian Li & David Wenzhong Gao, 2014. "Condition Parameter Modeling for Anomaly Detection in Wind Turbines," Energies, MDPI, vol. 7(5), pages 1-17, May.
    8. Sikora, Grzegorz & Wyłomańska, Agnieszka & Krapf, Diego, 2018. "Recurrence statistics for anomalous diffusion regime change detection," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 380-394.
    9. Joanna Janczura, 2014. "Pricing electricity derivatives within a Markov regime-switching model: a risk premium approach," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 79(1), pages 1-30, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sergey Zhironkin & Dawid Szurgacz, 2022. "Mining Technologies Innovative Development: Industrial, Environmental and Economic Perspectives," Energies, MDPI, vol. 15(5), pages 1-5, February.
    2. Dawid Szurgacz & Beata Borska & Ryszard Diederichs & Sergey Zhironkin, 2022. "Development of a Hydraulic System for the Automatic Expansion of Powered Roof Support," Energies, MDPI, vol. 15(3), pages 1-15, January.
    3. Dawid Szurgacz & Beata Borska & Sergey Zhironkin & Ryszard Diederichs & Anthony J. S. Spearing, 2022. "Optimization of the Load Capacity System of Powered Roof Support: A Review," Energies, MDPI, vol. 15(16), pages 1-15, August.
    4. Dawid Szurgacz & Beata Borska & Ryszard Diederichs & Anthony J. S. Spearing & Sergey Zhironkin, 2023. "Minimizing Internal Leaks of a Powered Roof Support’s Hydraulic Prop Based on Double Block with Charging," Energies, MDPI, vol. 16(3), pages 1-14, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sikora, Grzegorz & Wyłomańska, Agnieszka & Krapf, Diego, 2018. "Recurrence statistics for anomalous diffusion regime change detection," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 380-394.
    2. Conor McKinnon & James Carroll & Alasdair McDonald & Sofia Koukoura & Charlie Plumley, 2021. "Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data," Energies, MDPI, vol. 14(20), pages 1-20, October.
    3. Conor McKinnon & James Carroll & Alasdair McDonald & Sofia Koukoura & David Infield & Conaill Soraghan, 2020. "Comparison of New Anomaly Detection Technique for Wind Turbine Condition Monitoring Using Gearbox SCADA Data," Energies, MDPI, vol. 13(19), pages 1-19, October.
    4. Kucharczyk, Daniel & Wyłomańska, Agnieszka & Zimroz, Radosław, 2017. "Structural break detection method based on the Adaptive Regression Splines technique," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 499-511.
    5. Izquierdo, J. & Márquez, A. Crespo & Uribetxebarria, J. & Erguido, A., 2020. "On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects," Renewable Energy, Elsevier, vol. 153(C), pages 1100-1110.
    6. Moreno, Manuel & Novales, Alfonso & Platania, Federico, 2019. "Long-term swings and seasonality in energy markets," European Journal of Operational Research, Elsevier, vol. 279(3), pages 1011-1023.
    7. Weron, Rafał & Zator, Michał, 2015. "A note on using the Hodrick–Prescott filter in electricity markets," Energy Economics, Elsevier, vol. 48(C), pages 1-6.
    8. Peng Sun & Jian Li & Junsheng Chen & Xiao Lei, 2016. "A Short-Term Outage Model of Wind Turbines with Doubly Fed Induction Generators Based on Supervisory Control and Data Acquisition Data," Energies, MDPI, vol. 9(11), pages 1-21, October.
    9. Mingzhu Tang & Wei Chen & Qi Zhao & Huawei Wu & Wen Long & Bin Huang & Lida Liao & Kang Zhang, 2019. "Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data," Energies, MDPI, vol. 12(17), pages 1-15, September.
    10. Feng, Chenlong & Liu, Chao & Jiang, Dongxiang, 2023. "Unsupervised anomaly detection using graph neural networks integrated with physical-statistical feature fusion and local-global learning," Renewable Energy, Elsevier, vol. 206(C), pages 309-323.
    11. Zhang, Chen & Yang, Tao, 2021. "Optimal maintenance planning and resource allocation for wind farms based on non-dominated sorting genetic algorithm-ΙΙ," Renewable Energy, Elsevier, vol. 164(C), pages 1540-1549.
    12. Chiu, Singa Wang & Liang, Gang-Ming & Chiu, Yuan-Shyi Peter & Chiu, Tiffany, 2019. "Production planning incorporating issues of reliability and backlogging with service level constraint," Operations Research Perspectives, Elsevier, vol. 6(C).
    13. Xu, Qifa & Fan, Zhenhua & Jia, Weiyin & Jiang, Cuixia, 2020. "Fault detection of wind turbines via multivariate process monitoring based on vine copulas," Renewable Energy, Elsevier, vol. 161(C), pages 939-955.
    14. Maddalena Cavicchioli, 2021. "OLS Estimation of Markov switching VAR models: asymptotics and application to energy use," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(3), pages 431-449, September.
    15. Meng Li & Shuangxin Wang, 2019. "Dynamic Fault Monitoring of Pitch System in Wind Turbines using Selective Ensemble Small-World Neural Networks," Energies, MDPI, vol. 12(17), pages 1-20, August.
    16. Jastrzebska, Agnieszka & Morales Hernández, Alejandro & Nápoles, Gonzalo & Salgueiro, Yamisleydi & Vanhoof, Koen, 2022. "Measuring wind turbine health using fuzzy-concept-based drifting models," Renewable Energy, Elsevier, vol. 190(C), pages 730-740.
    17. Weron, Rafał & Zator, Michał, 2014. "Revisiting the relationship between spot and futures prices in the Nord Pool electricity market," Energy Economics, Elsevier, vol. 44(C), pages 178-190.
    18. Jorge Maldonado-Correa & Sergio Martín-Martínez & Estefanía Artigao & Emilio Gómez-Lázaro, 2020. "Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review," Energies, MDPI, vol. 13(12), pages 1-21, June.
    19. Matthew Boeding & Kelly Boswell & Michael Hempel & Hamid Sharif & Juan Lopez & Kalyan Perumalla, 2022. "Survey of Cybersecurity Governance, Threats, and Countermeasures for the Power Grid," Energies, MDPI, vol. 15(22), pages 1-22, November.
    20. Pawel Maryniak & Stefan Trueck & Rafal Weron, 2016. "Carbon pricing, forward risk premiums and pass-through rates in Australian electricity futures markets," HSC Research Reports HSC/16/10, Hugo Steinhaus Center, Wroclaw University of Technology.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:17:p:5496-:d:628376. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.