{"379461":{"#nid":"379461","#data":{"type":"event","title":"Ph.D Defense by Li Hao","body":[{"value":"\u003Cp\u003ETitle: \u003Cstrong\u003EResidual Life Prediction and Degradation-Based Control of\u003C\/strong\u003E\u003Cbr \/\u003E\u003Cstrong\u003EMulti-Component Systems\u003C\/strong\u003E\u003Cbr \/\u003E\u003Cbr \/\u003E\u003Cstrong\u003E*Advisors*:\u003C\/strong\u003E Dr. Nagi Gebraeel and Dr. Jianjun Shi\u003Cbr \/\u003E\u003Cbr \/\u003E\u003Cstrong\u003E*Committee members*:\u003C\/strong\u003E Dr. Kamran Paynabar, Dr. Chuck Zhang, and Dr. Jian Liu\u003Cbr \/\u003E(University of Arizona)\u003Cbr \/\u003E\u003Cbr \/\u003E\u003Cstrong\u003E*Date and time*:\u0026nbsp;\u003C\/strong\u003E Thursday, March 05 2015, 10:30AM\u003Cbr \/\u003E\u003Cbr \/\u003E\u003Cstrong\u003E*Location*:\u003C\/strong\u003E\u0026nbsp; Academic Office - Groseclose 204\u003Cbr \/\u003E\u003Cbr \/\u003E\u003Cstrong\u003E*Abstract*:\u003C\/strong\u003E\u003Cbr \/\u003EThe condition monitoring of multi-component systems utilizes multiple\u003Cbr \/\u003Esensors to capture the functional condition of the systems, and allows the\u003Cbr \/\u003Esensor information to be used to reason about the health information of the\u003Cbr \/\u003Esystems or components. This thesis focuses on modeling the relationship\u003Cbr \/\u003Ebetween multi-sensor information and component-level degradation, so as to\u003Cbr \/\u003Eprediction both system-level and component-level lifetimes. In addition,\u003Cbr \/\u003Ethis thesis also investigates the dynamic control of component-level\u003Cbr \/\u003Edegradation so as to control the failure times of individual components\u003Cbr \/\u003Ebased on real-time degradation monitoring.\u003Cbr \/\u003E\u003Cbr \/\u003EThe research topic that Chapter 3 focuses on is identifying component\u003Cbr \/\u003Edegradation signals from mixed sensor signals in order to predict\u003Cbr \/\u003Ecomponent-level residual lives. Specifically, we are interested in modeling\u003Cbr \/\u003Ethe degradation of systems that consist of two or more identical components\u003Cbr \/\u003Eoperating under similar conditions. The key challenge here is that a defect\u003Cbr \/\u003Ein any of the components will excite the same defective frequency, which\u003Cbr \/\u003Eprevents an effective separation of the degradation signals of defective\u003Cbr \/\u003Eand non-defective components. To the best of our knowledge, no existing\u003Cbr \/\u003Emethodologies have investigated this research topic. In Chapter 3, we\u003Cbr \/\u003Epropose a two-stage vibration-based prognostic methodology for modeling the\u003Cbr \/\u003Edegradation processes of components with identical defective frequencies.\u003Cbr \/\u003EThe first stage incorporates the independent component analysis (ICA) to\u003Cbr \/\u003Eidentify component vibration signals and reverse their original amplitude.\u003Cbr \/\u003EThe second stage consists of an adaptive prognostics method to predict\u003Cbr \/\u003Ecomponent residual lives. In the simulated case study, we investigate the\u003Cbr \/\u003Eperformance of the signal separation stage and that of the final\u003Cbr \/\u003Eresidual-life prediction under different conditions. The simulation results\u003Cbr \/\u003Eshow reasonable robustness of the methodology.\u003Cbr \/\u003E\u003Cbr \/\u003EIn Chapter 4, we focus on characterizing the interactive relationship\u003Cbr \/\u003Ebetween product quality degradation and tool wear in multistage\u003Cbr \/\u003Emanufacturing processes (MMPs), in which machine tools are considered as\u003Cbr \/\u003Ecomponents and the product quality measurements are considered as condition\u003Cbr \/\u003Emonitoring information. Due to the sequential structure of MMPs, the\u003Cbr \/\u003Edegradation status of a tool affects the product quality current stage,\u003Cbr \/\u003Ewhich, on the other hand, may affect the degradation of tools at subsequent\u003Cbr \/\u003Estages. To the best of our knowledge, although existing literature has\u003Cbr \/\u003Emodeled the impact of product quality on the tooling catastrophic failure,\u003Cbr \/\u003Eno published work has targeted on the impact of product quality on the\u003Cbr \/\u003Eactual process of tool wear. To address this research topic, we propose an\u003Cbr \/\u003Ehigh-dimensional stochastic differential equation model to capture the\u003Cbr \/\u003Einteraction relationship between the process of tool wear and product\u003Cbr \/\u003Equality. We then leverage real-time quality measurements to on-line predict\u003Cbr \/\u003Ethe residual life of the MMP as a system. In the simulation study, we\u003Cbr \/\u003Econclude that our methodology consistently performs better than a benchmark\u003Cbr \/\u003Emethodology that does not consider the impact of product quality on the\u003Cbr \/\u003Eprocess of tool wear or utilize real-time quality measurements.\u003Cbr \/\u003E\u003Cbr \/\u003EChapter 5 explores a new research direction, which is the dynamic control\u003Cbr \/\u003Eof component-level degradation in the parallel multi-component system, in\u003Cbr \/\u003Ewhich each component operates simultaneously to achieve an engineering\u003Cbr \/\u003Eobjective. This parallel configuration is usually designed with some level\u003Cbr \/\u003Eof redundancy, which means when a small portion of components fails to\u003Cbr \/\u003Eoperate, the remaining components can still achieve the engineering\u003Cbr \/\u003Eobjective by increasing their workloads up to the designed capacities.\u003Cbr \/\u003EConsequently, if the component degradation can be controlled, we can\u003Cbr \/\u003Eachieve better utilization of the redundancy to ensure consistent system\u003Cbr \/\u003Eperformance. To do this, Chapter 5 assumes that the degradation rate of a\u003Cbr \/\u003Ecomponent is directly related to its workload and develops a strategy of\u003Cbr \/\u003Edynamic workload adjustment in order to on-line control the degradation\u003Cbr \/\u003Eprocesses of individual components, and thus to control their failure\u003Cbr \/\u003Etimes. The criterion of selecting the optimal workloads is to prevent the\u003Cbr \/\u003Eoverlap of component failures. We conduct a simulated case study to\u003Cbr \/\u003Eevaluate the performance of our proposed methodology under different\u003Cbr \/\u003Econditions.\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Residual Life Prediction and Degradation-Based Control of Multi-Component Systems"}],"uid":"27707","created_gmt":"2015-02-18 13:23:32","changed_gmt":"2016-10-08 01:46:40","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2015-03-05T09:30:00-05:00","event_time_end":"2015-03-05T11:30:00-05:00","event_time_end_last":"2015-03-05T11:30:00-05:00","gmt_time_start":"2015-03-05 14:30:00","gmt_time_end":"2015-03-05 16:30:00","gmt_time_end_last":"2015-03-05 16:30:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}