{"id":6063,"date":"2026-03-15T22:30:51","date_gmt":"2026-03-15T22:30:51","guid":{"rendered":"https:\/\/fs.wp.odu.edu\/h1park\/project-vulnerable-multimodal-mobility-4\/"},"modified":"2026-03-16T21:33:21","modified_gmt":"2026-03-16T21:33:21","slug":"project-vulnerable-multimodal-mobility-4","status":"publish","type":"page","link":"https:\/\/fs.wp.odu.edu\/h1park\/project-vulnerable-multimodal-mobility-4\/","title":{"rendered":"Multimodal Adaptive Condition-based Monitoring"},"content":{"rendered":"\n\"Multimodal\n
← All Projects<\/a>\ud83d\udeb6 MSC \u2014 Pending<\/span><\/div>\n\n

Project Overview<\/h2>\n

Development of self-adapting models that autonomously integrate sub-system\ninteractions to comprehensively explain the state of a ship at the system level.<\/p>\n\n

Research Objectives<\/h2>\n

[Obj1]. Machine learning capable of predicting state, fault location, and remaining useful life of a single sub-system.\n[Obj2]. Multimodal multivariate feature integration across multi sub-systems using probabilistic latent state model.\n[Obj3]. Data-driven framework developed to be integrated with physics models for each equipment in the future.<\/p>\n\n

Publications<\/h2>\n

[1] A. Ghavidel, H. Park, S. Kovacic, and A. Sousa-Poza. GMM-LSTM Integration for Predicting Latent State, RUL, and Fault Location in Rotating Machinery. International Journal of Computer Integrated Manufacturing (In Press) [2] F. Javadnejad, H. Park, S. Kovacic, and A. Sousa-Poza, 2025. A Novel BiGMM-HMM Integration Frame-work with Divergence-Based Sensor and Subsystem Analysis for Predictive Maintenance in Naval Ves-\nsel Propulsion Systems. Discover Oceans 2 (33) [3] A. Ghavidel, H. Park, S. Kovacic, and A. Sousa-Poza. A New Predictive Maintenance Approach: novel integration of GMM-LSTM for prediction of latent state and failure location of Rotating Machinery. 19th Annual System of Systems Engineering Conference, Tacoma, WA, June 23-26, 2024.\n[4]. F. Javadnejad, H. Park, S. Kovacic, and A. Sousa-Poza. A Novel BiGMM-HMM Framework for Predictive Maintenance in Naval Vessel Propulsion Systems: Capturing Multimodality and Optimizing Performance. 19th Annual System of Systems Engineering Conference, Tacoma, WA, June 23-26, 2024. <\/p>\n","protected":false},"excerpt":{"rendered":"

← All Projects\ud83d\udeb6 MSC \u2014 Pending Project Overview Development of self-adapting models that autonomously integrate sub-system interactions to comprehensively explain the state of a ship at the system level. Research Objectives [Obj1]. Machine learning capable of predicting state, fault location, and remaining useful life of a single sub-system. [Obj2]. Multimodal multivariate feature integration across multi…<\/p>\n","protected":false},"author":26990,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/fs.wp.odu.edu\/h1park\/wp-json\/wp\/v2\/pages\/6063"}],"collection":[{"href":"https:\/\/fs.wp.odu.edu\/h1park\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/fs.wp.odu.edu\/h1park\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/fs.wp.odu.edu\/h1park\/wp-json\/wp\/v2\/users\/26990"}],"replies":[{"embeddable":true,"href":"https:\/\/fs.wp.odu.edu\/h1park\/wp-json\/wp\/v2\/comments?post=6063"}],"version-history":[{"count":3,"href":"https:\/\/fs.wp.odu.edu\/h1park\/wp-json\/wp\/v2\/pages\/6063\/revisions"}],"predecessor-version":[{"id":6114,"href":"https:\/\/fs.wp.odu.edu\/h1park\/wp-json\/wp\/v2\/pages\/6063\/revisions\/6114"}],"wp:attachment":[{"href":"https:\/\/fs.wp.odu.edu\/h1park\/wp-json\/wp\/v2\/media?parent=6063"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}