A hidden fault prediction model based on the belief rule base with power set and considering attribute reliability

Zhou, Zhijie; Feng, Zhichao; Hu, Changhua; Han, Xuefei; Zhou, Zhiguo; Li, Gailing

doi:10.1007/s11432-018-9620-7

Cited by 13 publications

(5 citation statements)

References 18 publications

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“…Thus, the distance between observed data can represent the fluctuations caused by perturbations. Therefore, in this paper, the average distance method is used to calculate the reliability of attributes [44]. In the IBRB-r model, the reliability of the attributes is a key measure of the extent to which the observation data are disturbed by noise.…”

Section: The Calculation Methods Of Attribute Reliabilitymentioning

confidence: 99%

An interpretable spacecraft flywheel system health status assessment method under perturbation

Zhang,

He,

et al. 2024

Meas. Sci. Technol.

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Health status assessment is an important measure for maintaining the safety of spacecraft flywheel systems. The influence of noise, sensor quality, and other disturbance factors can lead to a decrease in the reliability of the collected information. This can affect the model accuracy. Moreover, a loss of belief in the model is frequently caused by the opaque nature of the procedure and the incomprehensibility of the outcomes, particularly in fields such as aerospace. It is urgent to maintain the interpretability of the model and successfully identify the unreliability of the observed data. Therefore, this paper proposes a spacecraft flywheel system health status assessment method under perturbation based on interpretable belief rule base with attribute reliability (IBRB-r). First, the attribute reliability is calculated based on the average distance method, and a new fusion method of attribute reliability is proposed to reduce the interference of unreliable information. Then, a new interpretable constraint strategy is proposed to improve the rationality and interpretability of the parameters. Finally, the proposed method is validated by a case study of the health status assessment of a spacecraft flywheel system. Experiments show that the IBRB-r maintains high accuracy and interpretability under unreliable observation data.

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Section: The Calculation Methods Of Attribute Reliabilitymentioning

confidence: 99%

An interpretable spacecraft flywheel system health status assessment method under perturbation

Zhang,

He,

et al. 2024

Meas. Sci. Technol.

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“…It cannot handle local ignorance information. This reduces the accuracy of the model [ 27 ]. To solve this problem, the model is made capable of handling both local ignorance and global ignorance information.…”

Section: H-brbp Disease Diagnosis Modelmentioning

confidence: 99%

A new method for disease diagnosis based on hierarchical BRB with power set

Han¹,

Kang²,

He³

et al. 2023

Heliyon

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“…( ) Problem 2: In engineering practice, the reliability of the collected information may be compromised by the presence of complex environmental disturbances [25]. For example, the performance of the sensors degrades over time and therefore the quality of the information collected decreases.…”

Section: A Problem Formulationmentioning

confidence: 99%

A New Fault Diagnosis Method Based on Belief Rule Base With Attribute Reliability Considering Multi-Fault Features

Li,

Yin,

et al. 2023

IEEE Access

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Fault diagnosis plays a critical role in system health management. However, practical fault diagnosis encounters several challenges such as limited observational information, system complexity, and environmental disturbances. Belief rule base with attribute reliability (BRB-r) provides a valuable solution to these problems. In BRB-r, the reliability of the input information directly affects the reliability of the observed metrics and subsequently the accuracy of the output belief degree. To strike a balance between the reliability and accuracy of fault diagnosis models, a new fault diagnosis method for BRB-r considering multi-fault features (BRB-mr) is introduced. In the BRB-mr model, the reliability calculation method for attributes considering multi-fault features is proposed. The obtained attribute reliability is then introduced into the calculation of the matching degree, which ultimately reduces the interference of unreliable information to obtain more accurate and reliable diagnostic results. In addition, an optimization model is used to mitigate the effect of uncertainty in expert knowledge. The effectiveness of the method is validated by a case study of diesel engine fault diagnosis.INDEX TERMS Fault diagnosis, belief rule base, attribute reliability, multi-fault features.

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A hidden fault prediction model based on the belief rule base with power set and considering attribute reliability

Cited by 13 publications

References 18 publications

An interpretable spacecraft flywheel system health status assessment method under perturbation

An interpretable spacecraft flywheel system health status assessment method under perturbation

A new method for disease diagnosis based on hierarchical BRB with power set

A New Fault Diagnosis Method Based on Belief Rule Base With Attribute Reliability Considering Multi-Fault Features

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