Sensor Fault Diagnosis Based on Fuzzy Neural Petri Net
Joint Authors
Li, Jiming
Cheng, Xuezhen
Zhu, Xiaolin
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-10-23
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Li, Jiming& Zhu, Xiaolin& Cheng, Xuezhen. 2018. Sensor Fault Diagnosis Based on Fuzzy Neural Petri Net. Complexity،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1136088
Modern Language Association (MLA)
Li, Jiming…[et al.]. Sensor Fault Diagnosis Based on Fuzzy Neural Petri Net. Complexity No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1136088
American Medical Association (AMA)
Li, Jiming& Zhu, Xiaolin& Cheng, Xuezhen. Sensor Fault Diagnosis Based on Fuzzy Neural Petri Net. Complexity. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1136088
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1136088