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1. Recovering Missing Component Dependence for System Reliability Prediction via Synergy Between Physics and Data

   https://doi.org/10.1115/1.4052624  

   Li, Huiru; Du, Xiaoping (April 2022, Journal of Mechanical Design)

   Abstract Predicting system reliability is often a core task in systems design. System reliability depends on component reliability and dependence of components. Component reliability can be predicted with a physics-based approach if the associated physical models are available. If the models do not exist, component reliability may be estimated from data. When both types of components coexist, their dependence is often unknown, and therefore, the component states are assumed independent by the traditional method, which can result in a large error. This study proposes a new system reliability method to recover the missing component dependence, thereby leading to a more accurate estimate of the joint probability density function (PDF) of all the component states. The method works for series systems whose load is shared by its components that may fail due to excessive loading. For components without physical models available, the load data are recorded upon failure, and equivalent physical models are created; the model parameters are estimated by the proposed Bayesian approach. Then models of all component states become available, and the dependence of component states, as well as their joint PDF, can be estimated. Four examples are used to evaluate the proposed method, and the results indicate that the method can produce more accurate predictions of system reliability than the traditional method that assumes independent component states. 

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2. Power Grid Cascading Failure Prediction Based on Transformer

   https://doi.org/10.1007/978-3-030-91434-9_15  

   Zhou, Tianxin; Li, Xiang; Lu, Haibing (December 2021, Lecture notes in computer science)

   Smart grids can be vulnerable to attacks and accidents, and any initial failures in smart grids can grow to a large blackout because of cascading failure. Because of the importance of smart grids in modern society, it is crucial to protect them against cascading failures. Simulation of cascading failures can help identify the most vulnerable transmission lines and guide prioritization in protection planning, hence, it is an effective approach to protect smart grids from cascading failures. However, due to the enormous number of ways that the smart grids may fail initially, it is infeasible to simulate cascading failures at a large scale nor identify the most vulnerable lines efficiently. In this paper, we aim at 1) developing a method to run cascading failure simulations at scale and 2) building simplified, diffusion based cascading failure models to support efficient and theoretically bounded identification of most vulnerable lines. The goals are achieved by first constructing a novel connection between cascading failures and natural languages, and then adapting the powerful transformer model in NLP to learn from cascading failure data. Our trained transformer models have good accuracy in predicting the total number of failed lines in a cascade and identifying the most vulnerable lines. We also constructed independent cascade (IC) diffusion models based on the attention matrices of the transformer models, to support efficient vulnerability analysis with performance bounds. 

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3. A Bayesian Approach to Recovering Missing Component Dependence for System Reliability Prediction via Synergy Between Physics and Data

   https://doi.org/10.1115/DETC2021-67958  

   Li, Huiru; Du, Xiaoping (August 2021, Proceedings of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   Abstract Predicting system reliability is often a core task in systems design. System reliability depends on component reliability and dependence of components. Component reliability can be predicted with a physics-based approach if the associated physical models are available. If the models do not exist, component reliability may be estimated from data. When both types of components coexist, their dependence is often unknown, and the component states are therefore assumed independent by the traditional method, which can result in a large error. This work proposes a new system reliability method to recover the missing component dependence, thereby leading to a more accurate estimate of the joint probability density (PDF) of all the component states. The method works for series systems whose load is shared by its components that may fail due to excessive loading. For components without physical models available, the load data are recorded upon failure, and equivalent physical models are created; the model parameters are estimated by the proposed Bayesian approach. Then models of all component states become available, and the dependence of component states, as well as their joint PDF, can be estimated. Four examples are used to evaluate the proposed method, and the results indicate that the proposed method can produce more accurate predictions of system reliability than the traditional method that assumes independent component states. 

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4. Simulation of ductile failure of a notched-tension specimen of 3D printed 316L stainless steel

   https://doi.org/10.1007/s10704-025-00851-5  

   Xie, Jianing; Ravi-Chandar, Krishnaswamy (August 2025, International Journal of Fracture)

   The rapid development of 3D printing of 316L stainless steel thin-walled structures obtained by direct energy deposition has generated an increased interest in the mechanical properties of such materials for use in applications; in particular, failure models are needed to ensure structural reliability. We consider the response of uniaxial tenson specimens, with and without notches, to characterize the constitutive and failure behavior of the material. Specifically, we use numerical simulations of the notched tension experiment, achieved with a simple power- law strain hardening model and a failure criterion based on attaining a triaxiality-dependent critical strain-to-failure, to demonstrate that this model is capable of reproducing the material behavior accurately. 

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5. Evaluation of Power Transmission Lines Hardening Scenarios Using a Machine Learning Approach

   https://doi.org/10.1115/1.4063012  

   Montoya-Rincon, Juan P.; Gonzalez-Cruz, Jorge E.; Jensen, Michael P. (September 2023, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering)

   Abstract The power transmission infrastructure is vulnerable to extreme weather events, particularly hurricanes and tropical storms. A recent example is the damage caused by Hurricane Maria (H-Maria) in the archipelago of Puerto Rico in September 2017, where major failures in the transmission infrastructure led to a total blackout. Numerous studies have been conducted to examine strategies to strengthen the transmission system, including burying the power lines underground or increasing the frequency of tree trimming. However, few studies focus on the direct hardening of the transmission towers to accomplish an increase in resiliency. This machine learning-based study fills this need by analyzing three direct hardening scenarios and determining the effectiveness of these changes in the context of H-Maria. A methodology for estimating transmission tower damage is presented here as well as an analysis of impact of replacing structures with a high failure rate with more resilient ones. We found the steel self-support-pole to be the best replacement option for the towers with high failure rate. Furthermore, the third hardening scenario, where all wooden poles were replaced, exhibited a maximum reduction in damaged towers in a single line of 66% while lowering the mean number of damaged towers per line by 10%. 

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