Simulation based approach for reliability and remaining useful life estimation of spur gear pair under non-Markov and non-stationary load transitions

Dixit, Yashanshu; Kulkarni, Makarand S.

doi:10.1016/j.cie.2024.110026

Computers & Industrial Engineering

2024

DOI: 10.1016/j.cie.2024.110026

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Simulation based approach for reliability and remaining useful life estimation of spur gear pair under non-Markov and non-stationary load transitions

Yashanshu Dixit,

Makarand S. Kulkarni

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Cited by 2 publications

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

Gai,

Fang,

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

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Data and Model Synergy-Driven Rolling Bearings Remaining Useful Life Prediction Approach Based on Deep Neural Network and Wiener Process

Zhu,

Zhou,

Cheng

et al. 2024

Journal of Manufacturing Science and Engineering

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Various Remaining Useful Life (RUL) prediction methods, encompassing model-based, data-driven, and hybrid methods, have been developed and successfully applied to prognostics and health management for diverse rolling bearing. Hybrid methods that integrate the advantages of model-based and data-driven approaches have garnered significant attention. However, the effective integration of the two methods to address the randomness in rolling bearing full lifecycle processes remains a significant challenge. To overcome the challenge, this paper proposes a data and model synergy-driven RUL prediction framework that includes two data and model synergy approaches. Firstly, a convolutional stacked bidirectional long and short-term memory network with temporal attention mechanism is established to construct health index. The RUL prediction is achieved based on HI and polynomial model. Secondly, a three-phase prediction model based on the Wiener process is established by considering the evolution law of different degradation phases. Then, two synergy methods are designed. Strategy 1: HI is adopted as the observation value for online updating of physics degradation model parameters under Bayesian framework, and the RUL prediction results are obtained from the physics degradation model. Strategy 2: The RUL prediction results from the data-driven and physics-based model are weighted linearly combined to improve the overall prediction accuracy. The effectiveness of the proposed model is verified using two bearing full lifecycle datasets. The results indicate that the proposed approach can accommodate both short-term and long-term RUL predictions, outperforming state-of-the-art single models.

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Simulation based approach for reliability and remaining useful life estimation of spur gear pair under non-Markov and non-stationary load transitions

Cited by 2 publications

References 36 publications

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

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

Data and Model Synergy-Driven Rolling Bearings Remaining Useful Life Prediction Approach Based on Deep Neural Network and Wiener Process

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