Reliability Evaluation Based on the Colored Petri Net Converted from AADL Models for the Wheel Brake System of Aircraft

Huang, Mingyuan; Cheng, Dawei; Zhou, Jia; Lu, Zhong

doi:10.3390/aerospace10090739

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…These distinct characteristics reflect the gradual changes within the system before any fault manifestations occur. In the present study, linear regression was used to ascertain the rate of linear degradation during the healthy stage of the dataset [41]. Presuming the existence of specific measurements y i that correlate to x i within the time-series dataset D referred within the present study, the corresponding ensemble of data points could be denoted as (x 1 , y 1 ) , (x 2 , y 2 ) , .…”

Section: D-wgan-gp Denoising Methods Based On Physical Propertiesmentioning

confidence: 99%

Construction health indicator using physically-informed 1D-WGAN-GP joint attention LSTM-DenseNet method

Yang,

Sun

et al. 2024

Meas. Sci. Technol.

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In data-driven prognosis methods, the accuracy of predicting the remaining useful life (RUL) of mechanical systems is predominantly contingent upon the efficacy of system health indicators, typically amalgamated from statistical features derived from collected signals. Nevertheless, the majority of extant health indicators are beset by two principal shortcomings: (1) During traditional data denoising processes, degradation information from raw data is prone to loss owing to the lack of incorporation of the true physical properties of the data; and (2) The performance evaluation of constructed health indicators is imbalanced due to the influence of network structures on single models, often resulting in strong performance in only one or two indicators. To overcome such shortcomings, a mechanical health indicator construction method based on physical properties was proposed, termed 1D-WGAN-GP Joint Attention LSTM-DenseNet (ALD). Firstly, artificial sample data is generated by analysing the physical properties of the original dataset, which is then used to train the 1D-WGAN-GP model to achieve data denoising. Subsequently, the fusion of the Attention LSTM (A-LSTM) network and DenseNet network is utilised to extract crucial feature vectors of health indicators under varying health conditions from the denoised data. Finally, the extracted feature vectors are used to construct system health indicators using the Euclidean distance method, and these indicators are used for predicting the system's RUL. The results indicate that the proposed method outperformed traditional methods in terms of denoising effectiveness. Further, through ablation experiment analysis, the health indicators constructed by the proposed method demonstrated obvious complementarity in terms of monotonicity, correlation, robustness, and comprehensive evaluation. In RUL prediction applications, the proposed method also exhibited good performance, thereby validating its effectiveness.

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Section: D-wgan-gp Denoising Methods Based On Physical Propertiesmentioning

confidence: 99%

Construction health indicator using physically-informed 1D-WGAN-GP joint attention LSTM-DenseNet method

Yang,

Sun

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

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A novel Modelica-based reliability modeling approach for ship electric propulsion systems

Gai,

Fang,

et al. 2024

Meas. Sci. Technol.

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As the primary power source for ships, the reliability of electric propulsion systems directly impacts the safety, stability, and economic efficiency of maritime operations. However, the composition of ship electric propulsion systems is complex and is continuously exposed to the dynamic and variable marine environment, which complicates their reliability modeling and analysis. This paper introduces a novel approach to reliability modeling for electric propulsion systems based on the Modelica language. The aim is to overcome the limitations of traditional reliability modeling methods by considering the heterogeneity, dynamicity, and interactivity of electric propulsion systems. The approach addresses system heterogeneity through multi-domain modeling, captures environmental dynamics through parametric modeling, and establishes device interactions using Modelica language connectors. Additionally, modeling efficiency is enhanced by reusing device model packages, which benefits system optimization. Using a specific ship's electric propulsion system as a case study, the modeling process and simulation results are presented to demonstrate the effectiveness and flexibility of the proposed approach. This approach offers a new tool for reliability modeling of complex electromechanical systems and contributes to enhancing the accuracy and efficiency of system reliability assessments.

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Reliability Evaluation Based on the Colored Petri Net Converted from AADL Models for the Wheel Brake System of Aircraft

Cited by 2 publications

References 24 publications

Construction health indicator using physically-informed 1D-WGAN-GP joint attention LSTM-DenseNet method

Construction health indicator using physically-informed 1D-WGAN-GP joint attention LSTM-DenseNet method

A novel Modelica-based reliability modeling approach for ship electric propulsion systems

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