Sensor Fault Diagnosis and Tolerant Control Based on Belief Rule Base for Complex System

FENG Zhichao, ZHOU Zhijie, BAN Xiaojun, HU Changhua, ZHANG Xiaobo

系统科学与复杂性(英文) ›› 2023, Vol. 36 ›› Issue (3) : 1002-1023.

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系统科学与复杂性(英文) ›› 2023, Vol. 36 ›› Issue (3) : 1002-1023. DOI: 10.1007/s11424-023-1135-y

Sensor Fault Diagnosis and Tolerant Control Based on Belief Rule Base for Complex System

    FENG Zhichao1, ZHOU Zhijie1, BAN Xiaojun2, HU Changhua1, ZHANG Xiaobo1
作者信息 +

Sensor Fault Diagnosis and Tolerant Control Based on Belief Rule Base for Complex System

    FENG Zhichao1, ZHOU Zhijie1, BAN Xiaojun2, HU Changhua1, ZHANG Xiaobo1
Author information +
文章历史 +

摘要

This paper develops a new fault diagnosis and tolerant control framework of sensor failure (SFDTC) for complex system such as rockets and missiles. The new framework aims to solve two problems: The lack of data and the multiple uncertainty of knowledge. In the SFDTC framework, two parts exist: The fault diagnosis model and the output reconstruction model. These two parts of the new framework are constructed based on the new developed belief rule base with power set (BRB-PS). The multiple uncertainty of knowledge can be addressed by the local ignorance and global ignorance in the new developed BRB-PS model. Then, the stability of the developed framework is proved by the output error of the BRB-PS model. For complex system, the sensor state is determined by many factors and experts cannot provide accurate knowledge. The multiple uncertain knowledge will reduce the performance of the initial SDFTC framework. Therefore, in the SFDTC framework, to handle the influence of the uncertainty of expert knowledge and improve the framework performance, a new optimization model with two optimization goals is developed to ensure the smallest output uncertainty and the highest accuracy simultaneously. A case study is conducted to illustrate the effectiveness of the developed framework.

Abstract

This paper develops a new fault diagnosis and tolerant control framework of sensor failure (SFDTC) for complex system such as rockets and missiles. The new framework aims to solve two problems: The lack of data and the multiple uncertainty of knowledge. In the SFDTC framework, two parts exist: The fault diagnosis model and the output reconstruction model. These two parts of the new framework are constructed based on the new developed belief rule base with power set (BRB-PS). The multiple uncertainty of knowledge can be addressed by the local ignorance and global ignorance in the new developed BRB-PS model. Then, the stability of the developed framework is proved by the output error of the BRB-PS model. For complex system, the sensor state is determined by many factors and experts cannot provide accurate knowledge. The multiple uncertain knowledge will reduce the performance of the initial SDFTC framework. Therefore, in the SFDTC framework, to handle the influence of the uncertainty of expert knowledge and improve the framework performance, a new optimization model with two optimization goals is developed to ensure the smallest output uncertainty and the highest accuracy simultaneously. A case study is conducted to illustrate the effectiveness of the developed framework.

关键词

Belief rule base / fault diagnosis and tolerant control / optimization model / uncertainty

Key words

Belief rule base / fault diagnosis and tolerant control / optimization model / uncertainty

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FENG Zhichao , ZHOU Zhijie , BAN Xiaojun , HU Changhua , ZHANG Xiaobo. Sensor Fault Diagnosis and Tolerant Control Based on Belief Rule Base for Complex System. 系统科学与复杂性(英文), 2023, 36(3): 1002-1023 https://doi.org/10.1007/s11424-023-1135-y
FENG Zhichao , ZHOU Zhijie , BAN Xiaojun , HU Changhua , ZHANG Xiaobo. Sensor Fault Diagnosis and Tolerant Control Based on Belief Rule Base for Complex System. Journal of Systems Science and Complexity, 2023, 36(3): 1002-1023 https://doi.org/10.1007/s11424-023-1135-y

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基金

This work was supported in part by the Natural Science Foundation of China under Grant Nos. 61370031, 61374138, 61973046, 61833013, 61773389 and 71601168, the Fundamental Research Funds for the Central Universities under Grant No. D5000210690, the Shaanxi Outstanding Youth Science Foundation under Grant No. 2020JC-34 and the Natural Science Foundation of Shaanxi Province under Grant Nos. 2020JM-357, 2022JQ-580, 2021KJXX-22 and 2020JQ-298.
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