IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i18p5699-d632830.html
   My bibliography  Save this article

Advanced Control Design and Fault Diagnosis

Author

Listed:
  • Silvio Simani

    (Engineering Department, University of Ferrara, Via Saragat 1E, 44123 Ferrara, Italy)

  • Elena Zattoni

    (Dipartimento di Ingegneria dell’Energia Elettrica e dell’Informazione “G. Marconi”, Alma Mater Studiorum Università di Bologna, Viale Risorgimento 2, 40136 Bologna, Italy)

Abstract

This document provides the motivations and a brief introduction to the Special Issue entitled “Advanced Control Design and Fault Diagnosis”, which aims at presenting several solutions to the advanced control design and fault diagnosis systems. These methodologies can be considered in the general framework of advanced control, fault diagnosis and fault tolerant control systems, which are also able to improve the safety of the system under monitoring. The focuses of the current research in this field addressed in this Special Issue are also presented with emphasis on the practical application to simulated and realistic examples, which should provide an overall picture of current and future developments in this area. The works of this Special Issue represent suitably extended contributions selected by the proponents from the ACD2019—the 15th European Workshop on Advanced Control and Diagnosis, which was organised in Bologna, Italy on 21st–22nd November.

Suggested Citation

  • Silvio Simani & Elena Zattoni, 2021. "Advanced Control Design and Fault Diagnosis," Energies, MDPI, vol. 14(18), pages 1-6, September.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5699-:d:632830
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/18/5699/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/18/5699/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dumitru Popescu & Catalin Dimon & Pierre Borne & Severus Constantin Olteanu & Mihaela Ancuta Mone, 2020. "Advanced Control for Hydrogen Pyrolysis Installations," Energies, MDPI, vol. 13(12), pages 1-15, June.
    2. Nan Jin & Chao Pan & Yanyan Li & Shiyang Hu & Jie Fang, 2020. "Model Predictive Control for Virtual Synchronous Generator with Improved Vector Selection and Reconstructed Current," Energies, MDPI, vol. 13(20), pages 1-16, October.
    3. Di Wang & Xiao Wu & Jiong Shen, 2020. "An Efficient Robust Predictive Control of Main Steam Temperature of Coal-Fired Power Plant," Energies, MDPI, vol. 13(15), pages 1-24, July.
    4. Li, Naipeng & Gebraeel, Nagi & Lei, Yaguo & Fang, Xiaolei & Cai, Xiao & Yan, Tao, 2021. "Remaining useful life prediction based on a multi-sensor data fusion model," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    5. Marcin Witczak & Marcin Mrugalski & Bogdan Lipiec, 2021. "Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework," Energies, MDPI, vol. 14(8), pages 1-23, April.
    6. Giovanni Bucci & Fabrizio Ciancetta & Andrea Fioravanti & Edoardo Fiorucci & Simone Mari & Alberto Prudenzi, 2020. "Testing System for the On-Site Checking of Magneto-Thermal Switches with Arc Fault Detection," Energies, MDPI, vol. 13(18), pages 1-18, September.
    7. Faiçal Hamidi & Severus Constantin Olteanu & Dumitru Popescu & Houssem Jerbi & Ingrid Dincă & Sondess Ben Aoun & Rabeh Abbassi, 2020. "Model Based Optimisation Algorithm for Maximum Power Point Tracking in Photovoltaic Panels," Energies, MDPI, vol. 13(18), pages 1-20, September.
    Full references (including those not matched with items on IDEAS)

    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. Ta, Yuntian & Li, Yanfeng & Cai, Wenan & Zhang, Qianqian & Wang, Zhijian & Dong, Lei & Du, Wenhua, 2023. "Adaptive staged remaining useful life prediction method based on multi-sensor and multi-feature fusion," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    2. Nguyen, Khanh T.P. & Medjaher, Kamal & Gogu, Christian, 2022. "Probabilistic deep learning methodology for uncertainty quantification of remaining useful lifetime of multi-component systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    3. Liu, Lu & Song, Xiao & Zhou, Zhetao, 2022. "Aircraft engine remaining useful life estimation via a double attention-based data-driven architecture," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    4. João Inácio Da Silva Filho & Raphael Adamelk Bispo de Oliveira & Marcos Carneiro Rodrigues & Hyghor Miranda Côrtes & Alexandre Rocco & Mauricio Conceição Mario & Dorotéa Vilanova Garcia & Jair Minoro , 2023. "Predictive Controller Based on Paraconsistent Annotated Logic for Synchronous Generator Excitation Control," Energies, MDPI, vol. 16(4), pages 1-25, February.
    5. Pang, Zhenan & Li, Tianmei & Pei, Hong & Si, Xiaosheng, 2023. "A condition-based prognostic approach for age- and state-dependent partially observable nonlinear degrading system," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    6. Lyu, Dongzhen & Niu, Guangxing & Liu, Enhui & Zhang, Bin & Chen, Gang & Yang, Tao & Zio, Enrico, 2022. "Time space modelling for fault diagnosis and prognosis with uncertainty management: A general theoretical formulation," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    7. Fallahdizcheh, Amirhossein & Wang, Chao, 2022. "Transfer learning of degradation modeling and prognosis based on multivariate functional analysis with heterogeneous sampling rates," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    8. Li, Xilin & Teng, Wei & Peng, Dikang & Ma, Tao & Wu, Xin & Liu, Yibing, 2023. "Feature fusion model based health indicator construction and self-constraint state-space estimator for remaining useful life prediction of bearings in wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    9. Mourad Kchaou & Houssem Jerbi & Dan Stefanoiu & Dumitru Popescu, 2022. "Quantized Fault-Tolerant Control for Descriptor Systems with Intermittent Actuator Faults, Randomly Occurring Sensor Non-Linearity, and Missing Data," Mathematics, MDPI, vol. 10(11), pages 1-20, May.
    10. Wang, Yuan & Lei, Yaguo & Li, Naipeng & Yan, Tao & Si, Xiaosheng, 2023. "Deep multisource parallel bilinear-fusion network for remaining useful life prediction of machinery," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    11. Kumar, Anil & Parkash, Chander & Vashishtha, Govind & Tang, Hesheng & Kundu, Pradeep & Xiang, Jiawei, 2022. "State-space modeling and novel entropy-based health indicator for dynamic degradation monitoring of rolling element bearing," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    12. Jaime A. Rohten & Javier E. Muñoz & Esteban S. Pulido & José J. Silva & Felipe A. Villarroel & José R. Espinoza, 2021. "Very Low Sampling Frequency Model Predictive Control for Power Converters in the Medium and High-Power Range Applications," Energies, MDPI, vol. 14(1), pages 1-18, January.
    13. Chen, Jiaxian & Li, Dongpeng & Huang, Ruyi & Chen, Zhuyun & Li, Weihua, 2023. "Aero-engine remaining useful life prediction method with self-adaptive multimodal data fusion and cluster-ensemble transfer regression," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    14. Matej Bereš & Dobroslav Kováč & Tibor Vince & Irena Kováčová & Ján Molnár & Iveta Tomčíková & Jozef Dziak & Patrik Jacko & Branislav Fecko & Šimon Gans, 2021. "Efficiency Enhancement of Non-Isolated DC-DC Interleaved Buck Converter for Renewable Energy Sources," Energies, MDPI, vol. 14(14), pages 1-15, July.
    15. Guan, Yang & Meng, Zong & Sun, Dengyun & Liu, Jingbo & Fan, Fengjie, 2021. "2MNet: Multi-sensor and multi-scale model toward accurate fault diagnosis of rolling bearing," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    16. Shi, Yong & Zhang, Linzi, 2023. "Modelling long- and short-term multi-dimensional patterns in predictive maintenance with accumulative attention," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    17. Chen, Chong & Liu, Ying & Sun, Xianfang & Cairano-Gilfedder, Carla Di & Titmus, Scott, 2021. "An integrated deep learning-based approach for automobile maintenance prediction with GIS data," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    18. Liyuan Shao & Yong Zhang & Xiujuan Zheng & Xin He & Yufeng Zheng & Zhiwei Liu, 2023. "A Review of Remaining Useful Life Prediction for Energy Storage Components Based on Stochastic Filtering Methods," Energies, MDPI, vol. 16(3), pages 1-22, February.
    19. Guo, Junchao & He, Qingbo & Zhen, Dong & Gu, Fengshou & Ball, Andrew D., 2023. "Multi-sensor data fusion for rotating machinery fault detection using improved cyclic spectral covariance matrix and motor current signal analysis," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    20. Wiktor Olchowik & Marcin Bednarek & Tadeusz Dąbrowski & Adam Rosiński, 2023. "Application of the Energy Efficiency Mathematical Model to Diagnose Photovoltaic Micro-Systems," Energies, MDPI, vol. 16(18), pages 1-24, September.

    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:18:p:5699-:d:632830. 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.