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Sensor Fault Diagnosis Based on Fuzzy Neural Petri Net

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  • Jiming Li
  • Xiaolin Zhu
  • Xuezhen Cheng

Abstract

This study aims to improve the operating stability of the resistance strain weighing sensor and eliminate fuzzy factors in fault diagnosis. Based on fuzzy techniques for fault diagnosis, the proposed fuzzy Petri net model uses the fault logical relationship between a sensor and an improved Petri net model. A formula for confidence-based reasoning is proposed using an algorithm, which combines neural network regulation algorithm with a transition-enabled ignition judgment matrix. This formula can yield an accurate assessment of the operating state of the sensor. Backward inference and the minimum cut set theory are also combined to obtain the priority of faults, which helps avoid blind and ambiguous maintenance. The sensor model was analyzed, and its accuracy and validity were verified through statistical analysis and comparison with other methods of fault diagnosis.

Suggested Citation

  • Jiming Li & Xiaolin Zhu & Xuezhen Cheng, 2018. "Sensor Fault Diagnosis Based on Fuzzy Neural Petri Net," Complexity, Hindawi, vol. 2018, pages 1-11, October.
    • Handle: RePEc:hin:complx:8261549
    DOI: 10.1155/2018/8261549
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    Cited by:

    1. Ogahara, Zoë & Jespersen, Kristjan & Theilade, Ida & Nielsen, Martin Reinhard, 2022. "Review of smallholder palm oil sustainability reveals limited positive impacts and identifies key implementation and knowledge gaps," Land Use Policy, Elsevier, vol. 120(C).
    2. Mingyue Tan & Jiming Li & Xiangqian Chen & Xuezhen Cheng, 2019. "Power Grid Fault Diagnosis Method Using Intuitionistic Fuzzy Petri Nets Based on Time Series Matching," Complexity, Hindawi, vol. 2019, pages 1-14, July.

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