[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125168-en":3,"doc-seo-125168-105":29,"detail-sidebar-cat-0-en-105":89},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},125168,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Interpretable Machine Learning Approach for Reliability Analysis - Key research overview","Reliability assessment of stochastic dynamic engineering systems focuses on estimating the probability of not failing under specified objectives and constraints. Traditional reliability techniques exist, yet reliability analysis for complex, interdependent structures—where system physics evolve with degradation and maintenance—is often neglected. The proposed model-agnostic framework combines Bayesian statistics, interpretable machine learning, and stochastic differential equation discovery to estimate reliability when physical models are unknown or only approximate, supported by numerical demonstrations and expanded nuclear-system test cases.","Interpretable Machine Learning Approach for Reliability Analysis  \nKalpesh More 1, Yogesh Mathpati 1, Tapas Tripura 1, Rajdip Nayek 1, Syed Bahauddin Alam3,  \nSouvik Chakraborty 1,2 *  \n1Department of Applied Mechanics, Indian Institute of Technology Delhi, India 2Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi, India 3Nuclear, Plasma & Radiological Engineering, University of Illinois Urbana-Champaign, USA  \n*Corresponding Author [[souvik@am.iitd.ac.in](souvik@am.iitd.ac.in)]  \nAbstract  \nThe reliability assessment of stochastic dynamic systems is a crucial issue, specifically for complex engineering systems. Mathematically, reliability can be estimated as the probability of not failing while meeting particular objective functions and constraints. It is achieved by shrinking the area under the probability distribution function (PDF) while moving the average value. There are well-established techniques available in the literature for reliability estimation; however, the reliability analysis of existing systems, particularly complex interdependent and integral structures, is often overlooked despite being an equally significant problem.  \nIt is widely recognized that the behavior of structures can change over time due to degradation. Understanding, controlling, and mitigating component degradation are key priorities for complex engineering assets. As expensive engineering systems age beyond their design lifetimes, it is important to ensure reliability: detect and track degradation and changes in degradation rates; monitor system components for degradation; classify and characterize their degradation modes; and perform prognosis of their future state. Similarly, industrial systems that undergo multiple maintenance tasks and component replacements can also experience alterations in their governing physics, making it challenging to estimate their reliability using traditional methods that rely on a model based on the design blueprint of the system [1] . To address this issue, We propose and develop an innovative approach named \"model-agnostic reliability analysis framework\". This development has been published [2], and now we are extending this tool for trustworthy reliability analysis of complex nuclear systems with Missouri S&T. This method integrates Bayesian statistics, interpretable machine learning, and identification of stochastic dynamic equations (SDEs) to estimate the reliability of complex systems with unknown/approximate physics. In the conference presentation, we will demonstrate the effectiveness of our development for complex systems while extending the test cases for nuclear systems and structures through numerical examples, highlighting its potential application in reliability analysis in the domain of nuclear systems.  \nKeywords: RELIABILITY; EQUATION DISCOVERY; BAYESIAN; UNCERTAINTY  \nReferences:  \n[1] Mangan, Niall M., et al. \"Model selection for dynamical systems via sparse regression and information criteria.\" Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 473.2204 (2017): 20170009.  \n[2] Mathpati, Yogesh Chandrakant, et al. \"MAntRA: A framework for model agnostic reliability analysis.\" Reliability Engineering & System Safety 235 (2023): 109233.","cbCaipdSkeMjHDTd","https://ap.wps.com/l/cbCaipdSkeMjHDTd","pdf",142058,1,"English","en",105,"# Abstract\n## Reliability assessment of stochastic dynamic systems\n## Degradation, maintenance, and model uncertainty\n## Model-agnostic reliability analysis framework\n## Expected conference demonstrations","[{\"question\":\"How is reliability defined for stochastic dynamic systems in this work?\",\"answer\":\"Reliability is estimated as the probability of not failing while meeting specific objective functions and constraints.\"},{\"question\":\"Why do traditional reliability methods struggle with complex engineering systems?\",\"answer\":\" System behavior can change over time due to degradation and maintenance, altering governing physics and making reliability estimation difficult when relying on design-blueprint models.\"},{\"question\":\"What core components make the proposed framework model-agnostic?\",\"answer\":\"It integrates Bayesian statistics, interpretable machine learning, and identification of stochastic dynamic equations to handle unknown or approximate physics.\"}]","Interpretable Machine Learning Approach for Reliability Analysis - 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