IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v231y2023ics0951832022006238.html
   My bibliography  Save this article

Deep learning approach for energy efficiency prediction with signal monitoring reliability for a vinyl chloride monomer process

Author

Listed:
  • Panjapornpon, Chanin
  • Bardeeniz, Santi
  • Hussain, Mohamed Azlan

Abstract

A major concern for a real-time operation is the reliability of measurements. Especially for the petrochemical industry, which reveals complexity and uncertainty, the measurement fault causes consequences on safety, profitability, and utility management. Degraded signal quality not only leads to improper control action, but also creates more challenges for real-time energy efficiency management by reducing model performance and wasting more utility than standard operating practice. To improve system reliability and establish an effective energy efficiency monitoring tool, the combined framework for fault detection identification and energy efficiency prediction (FDI-EEP) based on a deep learning approach is proposed in this study. The FDI-EEP model uses the fault detection and identification result as a co-predictor for estimating energy efficiency aimed at improving the performance and reproducibility of the model and studying the effect of these faults on the downstream data-driven framework. Since process information is time-dependent, the long-short term memory layer is deployed on both networks to avoid gradient vanishing problems. A case study on the vinyl chloride monomer process datasets demonstrates that the proposed model precisely detected the measurement uncertainty and accurately performed the prediction task compared to other machine learning and prediction-based data cleaning methods.

Suggested Citation

  • Panjapornpon, Chanin & Bardeeniz, Santi & Hussain, Mohamed Azlan, 2023. "Deep learning approach for energy efficiency prediction with signal monitoring reliability for a vinyl chloride monomer process," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:reensy:v:231:y:2023:i:c:s0951832022006238
    DOI: 10.1016/j.ress.2022.109008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832022006238
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2022.109008?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. Saeed, Umer & Jan, Sana Ullah & Lee, Young-Doo & Koo, Insoo, 2021. "Fault diagnosis based on extremely randomized trees in wireless sensor networks," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    2. Ding, Yifei & Zhuang, Jichao & Ding, Peng & Jia, Minping, 2022. "Self-supervised pretraining via contrast learning for intelligent incipient fault detection of bearings," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    3. Quintanilha, Igor M. & Elias, Vitor R.M. & da Silva, Felipe B. & Fonini, Pedro A.M. & da Silva, Eduardo A.B. & Netto, Sergio L. & Apolinário, José A. & de Campos, Marcello L.R. & Martins, Wallace A., 2021. "A fault detector/classifier for closed-ring power generators using machine learning," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    4. Costa, Nahuel & Sánchez, Luciano, 2022. "Variational encoding approach for interpretable assessment of remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    5. Duan, Chaoqun & Li, Yifan & Pu, Huayan & Luo, Jun, 2022. "Adaptive monitoring scheme of stochastically failing systems under hidden degradation processes," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    6. Shi, Zunya & Chehade, Abdallah, 2021. "A dual-LSTM framework combining change point detection and remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    7. Sahu, Atma Ram & Palei, Sanjay Kumar, 2022. "Fault analysis of dragline subsystem using Bayesian network model," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    8. Li, Xinhong & Jia, Ruichao & Zhang, Renren & Yang, Shangyu & Chen, Guoming, 2022. "A KPCA-BRANN based data-driven approach to model corrosion degradation of subsea oil pipelines," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    9. Zhang, Xiao-Han & Zhu, Qun-Xiong & He, Yan-Lin & Xu, Yuan, 2018. "Energy modeling using an effective latent variable based functional link learning machine," Energy, Elsevier, vol. 162(C), pages 883-891.
    10. Abrishami, Shokoufeh & Khakzad, Nima & Hosseini, Seyed Mahmoud, 2020. "A data-based comparison of BN-HRA models in assessing human error probability: An offshore evacuation case study," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    11. Mazare, Mahmood & Taghizadeh, Mostafa, 2022. "Uncertainty estimator-based dual layer adaptive fault-tolerant control for wind turbines," Renewable Energy, Elsevier, vol. 188(C), pages 545-560.
    12. Yoo, Minji & Kim, Taejin & Yoon, Joung Taek & Kim, Yunhan & Kim, Sooho & Youn, Byeng D., 2020. "A resilience measure formulation that considers sensor faults," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    13. Zhang, Chen & Hu, Di & Yang, Tao, 2022. "Anomaly detection and diagnosis for wind turbines using long short-term memory-based stacked denoising autoencoders and XGBoost," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    14. Melani, Arthur Henrique de Andrade & Michalski, Miguel Angelo de Carvalho & da Silva, Renan Favarão & de Souza, Gilberto Francisco Martha, 2021. "A framework to automate fault detection and diagnosis based on moving window principal component analysis and Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    15. Liu, Junqiang & Pan, Chunlu & Lei, Fan & Hu, Dongbin & Zuo, Hongfu, 2021. "Fault prediction of bearings based on LSTM and statistical process analysis," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    16. Hao, Xiaochen & Guo, Tongtong & Huang, Gaolu & Shi, Xin & Zhao, Yantao & Yang, Yue, 2020. "Energy consumption prediction in cement calcination process: A method of deep belief network with sliding window," Energy, Elsevier, vol. 207(C).
    17. Liu, Junqiang & Lei, Fan & Pan, Chunlu & Hu, Dongbin & Zuo, Hongfu, 2021. "Prediction of remaining useful life of multi-stage aero-engine based on clustering and LSTM fusion," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    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. Zhao, Shuaiyu & Duan, Yiling & Roy, Nitin & Zhang, Bin, 2024. "A deep learning methodology based on adaptive multiscale CNN and enhanced highway LSTM for industrial process fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 249(C).

    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. Shi, Yaowei & Deng, Aidong & Deng, Minqiang & Xu, Meng & Liu, Yang & Ding, Xue & Bian, Wenbin, 2023. "Domain augmentation generalization network for real-time fault diagnosis under unseen working conditions," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    2. Chen, Zhen & Zhou, Di & Zio, Enrico & Xia, Tangbin & Pan, Ershun, 2023. "Adaptive transfer learning for multimode process monitoring and unsupervised anomaly detection in steam turbines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    3. Shi, Yaowei & Deng, Aidong & Deng, Minqiang & Xu, Meng & Liu, Yang & Ding, Xue & Li, Jing, 2022. "Transferable adaptive channel attention module for unsupervised cross-domain fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    4. Fang, Xiaoyu & Qu, Jianfeng & Chai, Yi, 2023. "Self-supervised intermittent fault detection for analog circuits guided by prior knowledge," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    5. Zhang, Chen & Hu, Di & Yang, Tao, 2024. "Research of artificial intelligence operations for wind turbines considering anomaly detection, root cause analysis, and incremental training," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    6. Zheng, Niannian & Luan, Xiaoli & Shardt, Yuri A.W. & Liu, Fei, 2024. "Dynamic-controlled principal component analysis for fault detection and automatic recovery," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    7. Xiang, Sheng & Qin, Yi & Luo, Jun & Pu, Huayan & Tang, Baoping, 2021. "Multicellular LSTM-based deep learning model for aero-engine remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    8. Zheng, Minglei & Man, Junfeng & Wang, Dian & Chen, Yanan & Li, Qianqian & Liu, Yong, 2023. "Semi-supervised multivariate time series anomaly detection for wind turbines using generator SCADA data," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    9. Shao, Kaixuan & He, Yigang & Xing, Zhikai & Du, Bolun, 2023. "Detecting wind turbine anomalies using nonlinear dynamic parameters-assisted machine learning with normal samples," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    10. Li, Yuanfu & Chen, Yao & Hu, Zhenchao & Zhang, Huisheng, 2023. "Remaining useful life prediction of aero-engine enabled by fusing knowledge and deep learning models," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    11. Coraça, Eduardo M. & Ferreira, Janito V. & Nóbrega, Eurípedes G.O., 2023. "An unsupervised structural health monitoring framework based on Variational Autoencoders and Hidden Markov Models," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    12. Liu, Jie & Xu, Yubo & Wang, Lisong, 2022. "Fault information mining with causal network for railway transportation system," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    13. Zhao, Shuaiyu & Duan, Yiling & Roy, Nitin & Zhang, Bin, 2024. "A deep learning methodology based on adaptive multiscale CNN and enhanced highway LSTM for industrial process fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    14. Xiao, Dasheng & Lin, Zhifu & Yu, Aiyang & Tang, Ke & Xiao, Hong, 2024. "Data-driven method embedded physical knowledge for entire lifecycle degradation monitoring in aircraft engines," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    15. Zhang, Jiusi & Jiang, Yuchen & Wu, Shimeng & Li, Xiang & Luo, Hao & Yin, Shen, 2022. "Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    16. Zhou, Han & Yin, Hongpeng & Chai, Yi, 2023. "Multi-grained mode partition and robust fault diagnosis for multimode industrial processes," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    17. Leoni, Leonardo & De Carlo, Filippo & Abaei, Mohammad Mahdi & BahooToroody, Ahmad & Tucci, Mario, 2023. "Failure diagnosis of a compressor subjected to surge events: A data-driven framework," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    18. Zhang, Huixin & Xi, Xiaopeng & Pan, Rong, 2023. "A two-stage data-driven approach to remaining useful life prediction via long short-term memory networks," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    19. He, Yuxuan & Su, Huai & Zio, Enrico & Peng, Shiliang & Fan, Lin & Yang, Zhaoming & Yang, Zhe & Zhang, Jinjun, 2023. "A systematic method of remaining useful life estimation based on physics-informed graph neural networks with multisensor data," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    20. He, Jiahui & Cheng, Zhijun & Guo, Bo, 2024. "Anomaly detection in telemetry data using a jointly optimal one-class support vector machine with dictionary learning," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

    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:eee:reensy:v:231:y:2023:i:c:s0951832022006238. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.