IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i2p310-d306476.html
   My bibliography  Save this article

Locating Sensors in Complex Engineering Systems for Fault Isolation Using Population-Based Incremental Learning

Author

Listed:
  • Jinxin Wang

    (College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China)

  • Zhongwei Wang

    (College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China)

  • Xiuzhen Ma

    (College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China)

  • Guojin Feng

    (Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK)

  • Chi Zhang

    (College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China)

Abstract

Fault diagnostics aims to locate the origin of an abnormity if it presents and therefore maximize the system performance during its full life-cycle. Many studies have been devoted to the feature extraction and isolation mechanisms of various faults. However, limited efforts have been spent on the optimization of sensor location in a complex engineering system, which is expected to be a critical step for the successful application of fault diagnostics. In this paper, a novel sensor location approach is proposed for the purpose of fault isolation using population-based incremental learning (PBIL). A directed graph is used to model the fault propagation of a complex engineering system. The multidimensional causal relationships of faults and symptoms were obtained via traversing the directed path in the directed graph. To locate the minimal quantity of sensors for desired fault isolatability, the problem of sensor location was firstly formulated as an optimization problem and then handled using PBIL. Two classical cases, including a diesel engine and a fluid catalytic cracking unit (FCCU), were taken as examples to demonstrate the effectiveness of the proposed approach. Results show that the proposed method can minimize the quantity of sensors while keeping the capacity of fault isolation unchanged.

Suggested Citation

  • Jinxin Wang & Zhongwei Wang & Xiuzhen Ma & Guojin Feng & Chi Zhang, 2020. "Locating Sensors in Complex Engineering Systems for Fault Isolation Using Population-Based Incremental Learning," Energies, MDPI, vol. 13(2), pages 1-14, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:2:p:310-:d:306476
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/2/310/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/2/310/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hong Wang & Hongbin Wang & Guoqian Jiang & Jimeng Li & Yueling Wang, 2019. "Early Fault Detection of Wind Turbines Based on Operational Condition Clustering and Optimized Deep Belief Network Modeling," Energies, MDPI, vol. 12(6), pages 1-22, March.
    2. Luis Fernando Grisales-Noreña & Daniel Gonzalez Montoya & Carlos Andres Ramos-Paja, 2018. "Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques," Energies, MDPI, vol. 11(4), pages 1-27, April.
    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. Oscar Danilo Montoya & Walter Gil-González & Jesus C. Hernández, 2023. "Optimal Power Flow Solution for Bipolar DC Networks Using a Recursive Quadratic Approximation," Energies, MDPI, vol. 16(2), pages 1-17, January.
    2. Andrés Alfonso Rosales-Muñoz & Luis Fernando Grisales-Noreña & Jhon Montano & Oscar Danilo Montoya & Alberto-Jesus Perea-Moreno, 2021. "Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Direct Current Electrical Networks," Sustainability, MDPI, vol. 13(16), pages 1-28, August.
    3. Zhao Zhang & Xiao He, 2020. "Fault-Structure-Based Active Fault Diagnosis: A Geometric Observer Approach," Energies, MDPI, vol. 13(17), pages 1-17, August.
    4. Oscar Danilo Montoya & Farhad Zishan & Diego Armando Giral-Ramírez, 2022. "Recursive Convex Model for Optimal Power Flow Solution in Monopolar DC Networks," Mathematics, MDPI, vol. 10(19), pages 1-14, October.
    5. Yuanyuan Yang & Md Muhie Menul Haque & Dongling Bai & Wei Tang, 2021. "Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review," Energies, MDPI, vol. 14(21), pages 1-26, October.
    6. Shanmugarajah Vinothine & Lidula N. Widanagama Arachchige & Athula D. Rajapakse & Roshani Kaluthanthrige, 2022. "Microgrid Energy Management and Methods for Managing Forecast Uncertainties," Energies, MDPI, vol. 15(22), pages 1-22, November.
    7. Ghaeth Fandi & Ibrahim Ahmad & Famous O. Igbinovia & Zdenek Muller & Josef Tlusty & Vladimir Krepl, 2018. "Voltage Regulation and Power Loss Minimization in Radial Distribution Systems via Reactive Power Injection and Distributed Generation Unit Placement," Energies, MDPI, vol. 11(6), pages 1-17, May.
    8. Mateusz Andrychowicz, 2021. "RES and ES Integration in Combination with Distribution Grid Development Using MILP," Energies, MDPI, vol. 14(2), pages 1-19, January.
    9. Mateusz Andrychowicz, 2020. "Comparison of the Use of Energy Storages and Energy Curtailment as an Addition to the Allocation of Renewable Energy in the Distribution System in Order to Minimize Development Costs," Energies, MDPI, vol. 13(14), pages 1-20, July.
    10. Mahesh Kumar & Amir Mahmood Soomro & Waqar Uddin & Laveet Kumar, 2022. "Optimal Multi-Objective Placement and Sizing of Distributed Generation in Distribution System: A Comprehensive Review," Energies, MDPI, vol. 15(21), pages 1-48, October.
    11. Luis Fernando Grisales-Noreña & Oscar Danilo Montoya & Ricardo Alberto Hincapié-Isaza & Mauricio Granada Echeverri & Alberto-Jesus Perea-Moreno, 2021. "Optimal Location and Sizing of DGs in DC Networks Using a Hybrid Methodology Based on the PPBIL Algorithm and the VSA," Mathematics, MDPI, vol. 9(16), pages 1-18, August.
    12. Mohsin Shahzad & Waseem Akram & Muhammad Arif & Uzair Khan & Barkat Ullah, 2021. "Optimal Siting and Sizing of Distributed Generators by Strawberry Plant Propagation Algorithm," Energies, MDPI, vol. 14(6), pages 1-13, March.
    13. Walter Gil-González & Oscar Danilo Montoya & Arul Rajagopalan & Luis Fernando Grisales-Noreña & Jesus C. Hernández, 2020. "Optimal Selection and Location of Fixed-Step Capacitor Banks in Distribution Networks Using a Discrete Version of the Vortex Search Algorithm," Energies, MDPI, vol. 13(18), pages 1-21, September.
    14. Nissim Amar & Aaron Shmaryahu & Michael Coletti & Ilan Aharon, 2021. "Sizing Procedure for System Hybridization Based on Experimental Source Modeling in Grid Application," Energies, MDPI, vol. 14(15), pages 1-19, August.
    15. Walter Gil-González & Oscar Danilo Montoya & Luis Fernando Grisales-Noreña & Alberto-Jesus Perea-Moreno & Quetzalcoatl Hernandez-Escobedo, 2020. "Optimal Placement and Sizing of Wind Generators in AC Grids Considering Reactive Power Capability and Wind Speed Curves," Sustainability, MDPI, vol. 12(7), pages 1-20, April.
    16. Oscar Danilo Montoya & Luis Fernando Grisales-Noreña & Alberto-Jesus Perea-Moreno, 2021. "Optimal Investments in PV Sources for Grid-Connected Distribution Networks: An Application of the Discrete–Continuous Genetic Algorithm," Sustainability, MDPI, vol. 13(24), pages 1-19, December.
    17. Tiago P. Abud & Andre A. Augusto & Marcio Z. Fortes & Renan S. Maciel & Bruno S. M. C. Borba, 2022. "State of the Art Monte Carlo Method Applied to Power System Analysis with Distributed Generation," Energies, MDPI, vol. 16(1), pages 1-24, December.
    18. Yolanda Vidal, 2023. "Artificial Intelligence for Wind Turbine Condition Monitoring," Energies, MDPI, vol. 16(4), pages 1-4, February.
    19. Zahid Javid & Ulas Karaagac & Ilhan Kocar & Ka Wing Chan, 2021. "Laplacian Matrix-Based Power Flow Formulation for LVDC Grids with Radial and Meshed Configurations," Energies, MDPI, vol. 14(7), pages 1-21, March.
    20. Wu, Yueqi & Ma, Xiandong, 2022. "A hybrid LSTM-KLD approach to condition monitoring of operational wind turbines," Renewable Energy, Elsevier, vol. 181(C), pages 554-566.

    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:13:y:2020:i:2:p:310-:d:306476. 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.