An Evidential Reasoning Rule-Based Quality State Assessment Method of Complex Systems Considering Feature Selection

Zhou, Zhijie; Ning, Pengyun; Wang, Jie; Zhang, Chun-Chao; Feng, Zhichao; Zhou, Xiangyi

doi:10.1109/tim.2023.3267351

Cited by 5 publications

(3 citation statements)

References 38 publications

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“…It mainly includes the following four aspects: 1) Zero bias error; 2) Scale factor error; 3) Error of gyro self-locking effect; 4) gyro random drift error. The bias parameter is not only one of the important error parameters of LIMU, but also an important index to measure the accuracy level of laser gyro [35], and its stability has a great influence on the accuracy of inertial navigation [36]. Construction of quality evaluation index system of LIMU.…”

Section: Construction Of Quality Evaluation Index System Of Limumentioning

confidence: 99%

Evaluating LIMU System Quality with Interval Evidence and Input Uncertainty

2023

KSII TIIS

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The laser inertial measurement unit is a precision device widely used in rocket navigation system and other equipment, and its quality is directly related to navigation accuracy. In the quality evaluation of laser inertial measurement unit, there is inevitably uncertainty in the index input information. First, the input numerical information is in interval form. Second, the index input grade and the quality evaluation result grade are given according to different national standards. So, it is a key step to transform the interval information input by the index into the data form consistent with the evaluation result grade. In the case of uncertain input, this paper puts forward a method based on probability distribution to solve the problem of asymmetry between the reference grade given by the index and the evaluation result grade when evaluating the quality of laser inertial measurement unit. By mapping the numerical relationship between the designated reference level and the evaluation reference level of the index information under different distributions, the index evidence symmetrical with the evaluation reference level is given. After the uncertain input information is transformed into evidence of interval degree distribution by this method, the information fusion of interval degree distribution evidence is carried out by interval evidential reasoning algorithm, and the evaluation result is obtained by projection covariance matrix adaptive evolution strategy optimization. Taking a five-meter redundant laser inertial measurement unit as an example, the applicability and effectiveness of this method are verified.

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Section: Construction Of Quality Evaluation Index System Of Limumentioning

confidence: 99%

Evaluating LIMU System Quality with Interval Evidence and Input Uncertainty

2023

KSII TIIS

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“…Zhou et al. [9] introduced an evaluation method grounded in ER rules and conducted experiments on an inertial navigation system. Liao et al.…”

Section: Introductionmentioning

confidence: 99%

“…Yang and Singh first proposed an evidential reasoning (ER) method based on D-S evidence theory and successfully applied it to motorbike performance evaluation. Zhou et al [9] introduced an evaluation method grounded in ER rules and conducted experiments on an inertial navigation system. Liao et al proposed an evaluation method combining cloud computing and ER [10] and applied it to the maintenance application of high-voltage transformers.…”

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confidence: 99%

Aviation fuel pump health state assessment based on evidential reasoning and random forests

Zhang,

Chen,

et al. 2024

Electronics Letters

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As the power source of the engine, the Fuel Pump(FP) plays a vital role in the safe operation of the aircraft. Due to the complexity of the working mechanism of Aviation Fuel Pumps (AFP) and the strong correlation between the components, the assessment model cannot reflect the health state of the FPs better, while the initial parameters in the assessment model will affect the assessment effect of the model. Therefore, this paper proposes a health status assessment model that can fully integrate monitoring data. To improve the accuracy of the model parameters, the Random Forest algorithm is used to give the feature weights to make up for the limitation of relying on expert knowledge, and the model parameters are optimized by the Covariance Matrix Adaptive Evolutionary Strategy algorithm, which achieves an accurate assessment of the state. Finally, the AFP test bed was built and the AFP was tested. Compared with other methods, the accuracy of the proposed method in this question reaches 96%, which is greatly superior to other methods and verifies the effectiveness of the proposed method. It also provides an outlook on future research directions for health state assessment.

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