IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v32y2021i6d10.1007_s10845-021-01768-1.html
   My bibliography  Save this article

Temporal anomaly detection on IIoT-enabled manufacturing

Author

Listed:
  • Peng Zhan

    (Shandong University)

  • Shaokun Wang

    (Shandong University)

  • Jun Wang

    (Shandong University)

  • Leigang Qu

    (Shandong University)

  • Kun Wang

    (Shandong University)

  • Yupeng Hu

    (Shandong University)

  • Xueqing Li

    (Shandong University)

Abstract

Along with the coming of industry 4.0 era, industrial internet of things (IIoT) plays a vital role in advanced manufacturing. It can not only connect all equipment and applications in manufacturing processes closely, but also provide oceans of sensor data for real-time work-in-process monitoring. Considering the corresponding abnormalities existing in these sensor data sequences, how to effectively implement temporal anomaly detection is of great significance for smart manufacturing. Therefore, in this paper, we proposed a novel time series anomaly detection method, which can effectively recognize corresponding abnormalities within the given time series sequences by standing on the hierarchical temporal representation. Extensive comparison experiments on the benchmark datasets have been conducted to demonstrate the superiority of our method in term of detection accuracy and efficiency on IIOT-enabled manufacturing.

Suggested Citation

  • Peng Zhan & Shaokun Wang & Jun Wang & Leigang Qu & Kun Wang & Yupeng Hu & Xueqing Li, 2021. "Temporal anomaly detection on IIoT-enabled manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1669-1678, August.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:6:d:10.1007_s10845-021-01768-1
    DOI: 10.1007/s10845-021-01768-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-021-01768-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-021-01768-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chayma Sellami & Carlos Miranda & Ahmed Samet & Mohamed Anis Bach Tobji & François de Beuvron, 2020. "On mining frequent chronicles for machine failure prediction," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 1019-1035, April.
    2. Hui Yang & Soundar Kumara & Satish T.S. Bukkapatnam & Fugee Tsung, 2019. "The internet of things for smart manufacturing: A review," IISE Transactions, Taylor & Francis Journals, vol. 51(11), pages 1190-1216, November.
    3. Ying Cheng & Luning Bi & Fei Tao & Ping Ji, 2020. "Hypernetwork-based manufacturing service scheduling for distributed and collaborative manufacturing operations towards smart manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1707-1720, October.
    4. Chia-Yu Hsu & Wei-Chen Liu, 2021. "Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 823-836, March.
    5. Lei Dong & Peng Wang & Fang Yan, 2019. "Damage forecasting based on multi-factor fuzzy time series and cloud model," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 521-538, 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. Akshansh Mishra & Anish Dasgupta, 2022. "Supervised and Unsupervised Machine Learning Algorithms for Forecasting the Fracture Location in Dissimilar Friction-Stir-Welded Joints," Forecasting, MDPI, vol. 4(4), pages 1-11, September.

    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. Ivanov, Dmitry & Dolgui, Alexandre & Sokolov, Boris, 2022. "Cloud supply chain: Integrating Industry 4.0 and digital platforms in the “Supply Chain-as-a-Service”," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    2. Konstantinos S. Boulas & Georgios D. Dounias & Chrissoleon T. Papadopoulos, 2023. "A hybrid evolutionary algorithm approach for estimating the throughput of short reliable approximately balanced production lines," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 823-852, February.
    3. Masoud Zafarzadeh & Magnus Wiktorsson & Jannicke Baalsrud Hauge, 2021. "A Systematic Review on Technologies for Data-Driven Production Logistics: Their Role from a Holistic and Value Creation Perspective," Logistics, MDPI, vol. 5(2), pages 1-32, April.
    4. Juliana Basulo-Ribeiro & Carina Pimentel & Leonor Teixeira, 2024. "Digital Transformation in Maritime Ports: Defining Smart Gates through Process Improvement in a Portuguese Container Terminal," Future Internet, MDPI, vol. 16(10), pages 1-27, September.
    5. Xiao-kang Wang & Sheng-hui Wang & Hong-yu Zhang & Jian-qiang Wang & Lin Li, 2021. "The Recommendation Method for Hotel Selection Under Traveller Preference Characteristics: A Cloud-Based Multi-Criteria Group Decision Support Model," Group Decision and Negotiation, Springer, vol. 30(6), pages 1433-1469, December.
    6. Jeongsub Choi & Mengmeng Zhu & Jihoon Kang & Myong K. Jeong, 2024. "Convolutional neural network based multi-input multi-output model for multi-sensor multivariate virtual metrology in semiconductor manufacturing," Annals of Operations Research, Springer, vol. 339(1), pages 185-201, August.
    7. Asadi, Shahla & Nilashi, Mehrbakhsh & Iranmanesh, Mohammad & Hyun, Sunghyup Sean & Rezvani, Azadeh, 2022. "Effect of internet of things on manufacturing performance: A hybrid multi-criteria decision-making and neuro-fuzzy approach," Technovation, Elsevier, vol. 118(C).
    8. Kang, Yanfei & Spiliotis, Evangelos & Petropoulos, Fotios & Athiniotis, Nikolaos & Li, Feng & Assimakopoulos, Vassilios, 2021. "Déjà vu: A data-centric forecasting approach through time series cross-similarity," Journal of Business Research, Elsevier, vol. 132(C), pages 719-731.
    9. Mingxing Li & Ray Y. Zhong & Ting Qu & George Q. Huang, 2022. "Spatial–temporal out-of-order execution for advanced planning and scheduling in cyber-physical factories," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1355-1372, June.
    10. Omar, Yamila M. & Minoufekr, Meysam & Plapper, Peter, 2019. "Business analytics in manufacturing: Current trends, challenges and pathway to market leadership," Operations Research Perspectives, Elsevier, vol. 6(C).
    11. Ragosebo Kgaugelo Modise & Khumbulani Mpofu & Olukorede Tijani Adenuga, 2021. "Energy and Carbon Emission Efficiency Prediction: Applications in Future Transport Manufacturing," Energies, MDPI, vol. 14(24), pages 1-15, December.
    12. Eren Bas & Erol Egrioglu & Taner Tunc, 2023. "Multivariate Picture Fuzzy Time Series: New Definitions and a New Forecasting Method Based on Pi-Sigma Artificial Neural Network," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 139-164, January.
    13. Jinwoo Song & Prashant Kumar & Yonghawn Kim & Heung Soo Kim, 2024. "A Fault Detection System for Wiring Harness Manufacturing Using Artificial Intelligence," Mathematics, MDPI, vol. 12(4), pages 1-17, February.
    14. Emily Opoku Aboagye-Dapaah & Michael Karikari Appiah & Joshua Caleb Dagadu, 2024. "Smart Manufacturing and Pro-Environmental Behavior: Moderated Serial Mediation Modelling and Analysis," Sustainability, MDPI, vol. 16(17), pages 1-20, September.
    15. Chien-Chih Wang & Yi-Ying Yang, 2023. "A Machine Learning Approach for Improving Wafer Acceptance Testing Based on an Analysis of Station and Equipment Combinations," Mathematics, MDPI, vol. 11(7), pages 1-14, March.
    16. Sun, Wenqiang & Wang, Qiang & Zhou, Yue & Wu, Jianzhong, 2020. "Material and energy flows of the iron and steel industry: Status quo, challenges and perspectives," Applied Energy, Elsevier, vol. 268(C).
    17. Ye, Zhenggeng & Yang, Hui & Cai, Zhiqiang & Si, Shubin & Zhou, Fuli, 2021. "Performance evaluation of serial-parallel manufacturing systems based on the impact of heterogeneous feedstocks on machine degradation," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    18. Matsumoto, Takao & Chen, Yijun & Nakatsuka, Akihiro & Wang, Qunzhi, 2020. "Research on horizontal system model for food factories: A case study of process cheese manufacturer," International Journal of Production Economics, Elsevier, vol. 226(C).
    19. Meira, Erick & Cyrino Oliveira, Fernando Luiz & Jeon, Jooyoung, 2021. "Treating and Pruning: New approaches to forecasting model selection and combination using prediction intervals," International Journal of Forecasting, Elsevier, vol. 37(2), pages 547-568.
    20. Ronny Seiger & Marco Franceschetti & Barbara Weber, 2023. "An Interactive Method for Detection of Process Activity Executions from IoT Data," Future Internet, MDPI, vol. 15(2), pages 1-31, February.

    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:spr:joinma:v:32:y:2021:i:6:d:10.1007_s10845-021-01768-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.