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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
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    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.
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