IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i14p2816-d250569.html
   My bibliography  Save this article

Health Assessment and Fault Detection System for an Industrial Robot Using the Rotary Encoder Signal

Author

Listed:
  • Riyadh Nazar Ali Algburi

    (Engineering Research Center of Advanced Driving Energy-saving Technology, Ministry of Education, Chengdu 610036, China
    Mechatronics Department, School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610036, China)

  • Hongli Gao

    (Engineering Research Center of Advanced Driving Energy-saving Technology, Ministry of Education, Chengdu 610036, China
    Mechatronics Department, School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610036, China)

Abstract

In an industrial robot, rotary encoders have been extensively used for dynamic control and positioning. This study shows that the encoder signal, after appropriate processing, can also be efficiently utilized for the health observation of energy performance of industrial robots system. Singular spectrum analysis (SSA) and Hilbert transform (HT) is proposed in this work, for detecting weak position oscillations to estimate the instantaneous amplitudes (IA) and the instantaneous frequencies (IF) of an industrial robot based on the encoder signal. Compared with empirical mode decomposition (EMD) and HT, the singular spectrum analysis and Hilbert transform (SSAHT) outperforms empirical mode decomposition Hilbert transform (EMDHT) in terms of ability and precision to determine source noise, and it can accurately catch the weak oscillations without signal deformation in both position and speed introduced via mechanical flaws. Combined with SSA, the IA and IF of both oscillations and residual are extracted by HT. They are obtained from the robot arm movement. These features play an important role in improving the performance detecting weak oscillations and the residual, essential information to evaluate the health conditions and fault detection to serve the energy performance for the industrial robot. The efficiency of the proposed system has been verified both numerical simulation and experimental data. The outcomes prove that the proposed SSAHT can detect flaw indications and additionally, it can also identify faulty components. Thus, the study presents a promising tool for the health monitoring of an industrial robot instead of the vibration-based monitoring scheme.

Suggested Citation

  • Riyadh Nazar Ali Algburi & Hongli Gao, 2019. "Health Assessment and Fault Detection System for an Industrial Robot Using the Rotary Encoder Signal," Energies, MDPI, vol. 12(14), pages 1-25, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2816-:d:250569
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/14/2816/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/14/2816/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jonathan Gillard & Vincent Knight, 2014. "Using Singular Spectrum Analysis to obtain staffing level requirements in emergency units," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(5), pages 735-746, May.
    2. Eduardo Garcia & Nicolás Montés, 2019. "Real-Time Idle Time Cancellation by Means of Miniterm 4.0," Energies, MDPI, vol. 12(7), pages 1-13, March.
    3. Theodore Alexandrov & Silvia Bianconcini & Estela Bee Dagum & Peter Maass & Tucker S. McElroy, 2012. "A Review of Some Modern Approaches to the Problem of Trend Extraction," Econometric Reviews, Taylor & Francis Journals, vol. 31(6), pages 593-624, November.
    4. Neeraj Bokde & Andrés Feijóo & Daniel Villanueva & Kishore Kulat, 2019. "A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction," Energies, MDPI, vol. 12(2), pages 1-42, January.
    5. Vitanov, Nikolay K. & Hoffmann, Norbert P. & Wernitz, Boris, 2014. "Nonlinear time series analysis of vibration data from a friction brake: SSA, PCA, and MFDFA," Chaos, Solitons & Fractals, Elsevier, vol. 69(C), pages 90-99.
    6. Yuyang Gao & Chao Qu & Kequan Zhang, 2016. "A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 9(10), pages 1-28, September.
    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. Prashant Kumar & Salman Khalid & Heung Soo Kim, 2023. "Prognostics and Health Management of Rotating Machinery of Industrial Robot with Deep Learning Applications—A Review," Mathematics, MDPI, vol. 11(13), pages 1-37, July.

    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. Yuzgec, Ugur & Dokur, Emrah & Balci, Mehmet, 2024. "A novel hybrid model based on Empirical Mode Decomposition and Echo State Network for wind power forecasting," Energy, Elsevier, vol. 300(C).
    2. Dagum, Estela Bee, 2010. "Business Cycles and Current Economic Analysis/Los ciclos económicos y el análisis económico actual," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 28, pages 577-594, Diciembre.
    3. Manisha Sawant & Rupali Patil & Tanmay Shikhare & Shreyas Nagle & Sakshi Chavan & Shivang Negi & Neeraj Dhanraj Bokde, 2022. "A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction," Energies, MDPI, vol. 15(21), pages 1-24, October.
    4. Zhiyan Zhang & Aobo Deng & Zhiwen Wang & Jianyong Li & Hailiang Zhao & Xiaoliang Yang, 2024. "Wind Power Prediction Based on EMD-KPCA-BiLSTM-ATT Model," Energies, MDPI, vol. 17(11), pages 1-15, May.
    5. Bokde, Neeraj Dhanraj & Tranberg, Bo & Andresen, Gorm Bruun, 2021. "Short-term CO2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling," Applied Energy, Elsevier, vol. 281(C).
    6. Mirosław Parol & Paweł Piotrowski & Piotr Kapler & Mariusz Piotrowski, 2021. "Forecasting of 10-Second Power Demand of Highly Variable Loads for Microgrid Operation Control," Energies, MDPI, vol. 14(5), pages 1-29, February.
    7. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou, 2021. "Gold Against the Machine," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 5-28, January.
    8. Vasilios Plakandaras & Theophilos Papadimitriou & Periklis Gogas, 2015. "Forecasting Daily and Monthly Exchange Rates with Machine Learning Techniques," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(7), pages 560-573, November.
    9. Manisha Sawant & Mayur Kishor Shende & Andrés E. Feijóo-Lorenzo & Neeraj Dhanraj Bokde, 2021. "The State-of-the-Art Progress in Cloud Detection, Identification, and Tracking Approaches: A Systematic Review," Energies, MDPI, vol. 14(23), pages 1-26, December.
    10. Linh Nguyen & Vilém Novák & Soheyla Mirshahi, 2020. "Trend‐cycle Estimation Using Fuzzy Transform and Its Application for Identifying Bull and Bear Phases in Markets," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(3), pages 111-124, July.
    11. Huang, Xu & Hassani, Hossein & Ghodsi, Mansi & Mukherjee, Zinnia & Gupta, Rangan, 2017. "Do trend extraction approaches affect causality detection in climate change studies?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 604-624.
    12. Tung-Lam Dao, 2014. "Momentum Strategies with L1 Filter," Papers 1403.4069, arXiv.org.
    13. Renato Ferrero & Mario Collotta & Maria Victoria Bueno-Delgado & Hsing-Chung Chen, 2020. "Smart Management Energy Systems in Industry 4.0," Energies, MDPI, vol. 13(2), pages 1-3, January.
    14. Marlon Fritz, 2019. "Data-Driven Local Polynomial Trend Estimation for Economic Data - Steady State Adjusting Trends," Working Papers Dissertations 49, Paderborn University, Faculty of Business Administration and Economics.
    15. Javier Llopis & Antonio Lacasa & Eduardo Garcia & Nicolás Montés & Lucía Hilario & Judith Vizcaíno & Cristina Vilar & Judit Vilar & Laura Sánchez & Juan Carlos Latorre, 2022. "Manufacturing Maps, a Novel Tool for Smart Factory Management Based on Petri Nets and Big Data Mini-Terms," Mathematics, MDPI, vol. 10(14), pages 1-22, July.
    16. Shengqi Zhang & Yateendra Mishra & Bei Yuan & Jianfeng Zhao & Mohammad Shahidehpour, 2018. "Primary Frequency Controller with Prediction-Based Droop Coefficient for Wind-Storage Systems under Spot Market Rules," Energies, MDPI, vol. 11(9), pages 1-19, September.
    17. Luis Lopez & Ingrid Oliveros & Luis Torres & Lacides Ripoll & Jose Soto & Giovanny Salazar & Santiago Cantillo, 2020. "Prediction of Wind Speed Using Hybrid Techniques," Energies, MDPI, vol. 13(23), pages 1-13, November.
    18. Fritz, Marlon, 2019. "Steady state adjusting trends using a data-driven local polynomial regression," Economic Modelling, Elsevier, vol. 83(C), pages 312-325.
    19. Dan Liu & Yiqun Kang & Heng Luo & Xiaotong Ji & Kan Cao & Hengrui Ma, 2023. "A Grid Status Analysis Method with Large-Scale Wind Power Access Using Big Data," Energies, MDPI, vol. 16(12), pages 1-12, June.
    20. Wu, Huijuan & Meng, Keqilao & Fan, Daoerji & Zhang, Zhanqiang & Liu, Qing, 2022. "Multistep short-term wind speed forecasting using transformer," Energy, Elsevier, vol. 261(PA).

    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:12:y:2019:i:14:p:2816-:d:250569. 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.