Mechanical Engineering
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Item Prognostics of Ball Bearings in Cooling Fans(2012) Oh, Hyunseok; Pecht, Michael; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Ball bearings have been used to support rotating shafts in machines such as wind turbines, aircraft engines, and desktop computer fans. There has been extensive research in the areas of condition monitoring, diagnostics, and prognostics of ball bearings. As the identification of ball bearing defects by inspection interrupts the operation of rotating machines and can be costly, the assessment of the health of ball bearings relies on the use of condition monitoring techniques. Fault detection and life prediction methods have been developed to improve condition-based maintenance and product qualification. However, intermittent and catastrophic system failures due to bearing problems still occur resulting in loss of life and increase of maintenance and warranty costs. Inaccurate life prediction of ball bearings is of concern to industry. This research focuses on prognostics of ball bearings based on vibration and acoustic emission analysis to provide early warning of failure and predict life in advance. The failure mechanisms of ball bearings in cooling fans are identified and failure precursors associated with the defects are determined. A prognostic method based on Bayesian Monte Carlo method and sequential probability ratio test is developed to predict time-to-failure of ball bearings in advance. A benchmark study is presented to demonstrate the application of the developed prognostic method to desktop computer fans. The prognostic method developed in this research can be extended as a general method to predict life of a component or system.Item ADVANCES IN SYSTEM RELIABILITY-BASED DESIGN AND PROGNOSTICS AND HEALTH MANAGEMENT (PHM) FOR SYSTEM RESILIENCE ANALYSIS AND DESIGN(2011) Hu, Chao; Youn, Byeng D.; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Failures of engineered systems can lead to significant economic and societal losses. Despite tremendous efforts (e.g., $200 billion annually) denoted to reliability and maintenance, unexpected catastrophic failures still occurs. To minimize the losses, reliability of engineered systems must be ensured throughout their life-cycle amidst uncertain operational condition and manufacturing variability. In most engineered systems, the required system reliability level under adverse events is achieved by adding system redundancies and/or conducting system reliability-based design optimization (RBDO). However, a high level of system redundancy increases a system's life-cycle cost (LCC) and system RBDO cannot ensure the system reliability when unexpected loading/environmental conditions are applied and unexpected system failures are developed. In contrast, a new design paradigm, referred to as resilience-driven system design, can ensure highly reliable system designs under any loading/environmental conditions and system failures while considerably reducing systems' LCC. In order to facilitate the development of formal methodologies for this design paradigm, this research aims at advancing two essential and co-related research areas: Research Thrust 1 - system RBDO and Research Thrust 2 - system prognostics and health management (PHM). In Research Thrust 1, reliability analyses under uncertainty will be carried out in both component and system levels against critical failure mechanisms. In Research Thrust 2, highly accurate and robust PHM systems will be designed for engineered systems with a single or multiple time-scale(s). To demonstrate the effectiveness of the proposed system RBDO and PHM techniques, multiple engineering case studies will be presented and discussed. Following the development of Research Thrusts 1 and 2, Research Thrust 3 - resilience-driven system design will establish a theoretical basis and design framework of engineering resilience in a mathematical and statistical context, where engineering resilience will be formulated in terms of system reliability and restoration and the proposed design framework will be demonstrated with a simplified aircraft control actuator design problem.Item Advanced methodologies for reliability-based design optimization and structural health prognostics(2010) Wang, Pingfeng; Youn, Byeng Dong; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Failures of engineered systems can lead to significant economic and societal losses. To minimize the losses, reliability must be ensured throughout the system's lifecycle in the presence of manufacturing variability and uncertain operational conditions. Many reliability-based design optimization (RBDO) techniques have been developed to ensure high reliability of engineered system design under manufacturing variability. Schedule-based maintenance, although expensive, has been a popular method to maintain highly reliable engineered systems under uncertain operational conditions. However, so far there is no cost-effective and systematic approach to ensure high reliability of engineered systems throughout their lifecycles while accounting for both the manufacturing variability and uncertain operational conditions. Inspired by an intrinsic ability of systems in ecology, economics, and other fields that is able to proactively adjust their functioning to avoid potential system failures, this dissertation attempts to adaptively manage engineered system reliability during its lifecycle by advancing two essential and co-related research areas: system RBDO and prognostics and health management (PHM). System RBDO ensures high reliability of an engineered system in the early design stage, whereas capitalizing on PHM technology enables the system to proactively avoid failures in its operation stage. Extensive literature reviews in these areas have identified four key research issues: (1) how system failure modes and their interactions can be analyzed in a statistical sense; (2) how limited data for input manufacturing variability can be used for RBDO; (3) how sensor networks can be designed to effectively monitor system health degradation under highly uncertain operational conditions; and (4) how accurate and timely remaining useful lives of systems can be predicted under highly uncertain operational conditions. To properly address these key research issues, this dissertation lays out four research thrusts in the following chapters: Chapter 3 - Complementary Intersection Method for System Reliability Analysis, Chapter 4 - Bayesian Approach to RBDO, Chapter 5 - Sensing Function Design for Structural Health Prognostics, and Chapter 6 - A Generic Framework for Structural Health Prognostics. Multiple engineering case studies are presented to demonstrate the feasibility and effectiveness of the proposed RBDO and PHM techniques for ensuring and improving the reliability of engineered systems within their lifecycles.Item Implementation of Prognostics and Health Management for Electronic Systems(2007-06-06) Tuchband, Brian Adam; Pecht, Michael G; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)An assessment has been undertaken to identify the state-of-practice for prognostics and health management of electronics. Based on a review of the prognostic approaches, case studies, publications, and the extent of intellectual property of numerous organizations, I identified the companies, universities, and government branches that are currently researching, developing, and/or implementing prognostics for their products and systems. Next, I developed a sensor selection process such that an optimal sensor system can be chosen prior to in-situ life cycle monitoring of electronic products and systems. I developed a questionnaire that can be used to understand the monitoring requirements of a particular PHM application, and identified criteria that one needs to consider in the sensor selection process in order to make the relevant tradeoffs. Finally, I provided guidelines on sensor selection to help a user validate their final selection. The process was demonstrated for two circuit card assemblies inside an avionics unit.Item Prognostics and Health Management of Electronics by Utilizing Environmental and Usage Loads(2006-07-06) Vichare, Nikhil Manohar; Pecht, Michael G; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Prognostics and health management (PHM) is a method that permits the reliability of a system to be evaluated in its actual application conditions. Thus by determining the advent of failure, procedures can be developed to mitigate, manage and maintain the system. Since, electronic systems control most systems today and their reliability is usually critical for system reliability, PHM techniques are needed for electronics. To enable prognostics, a methodology was developed to extract load-parameters required for damage assessment from irregular time-load data. As a part of the methodology an algorithm that extracts cyclic range and means, ramp-rates, dwell-times, dwell-loads and correlation between load parameters was developed. The algorithm enables significant reduction of the time-load data without compromising features that are essential for damage estimation. The load-parameters are stored in bins with a-priori calculated (optimal) bin-width. The binned data is then used with Gaussian kernel function for density estimation of the load-parameter for use in damage assessment and prognostics. The method was shown to accurately extract the desired load-parameters and enable condensed storage of load histories, thus improving resource efficiency of the sensor nodes. An approach was developed to assess the impact of uncertainties in measurement, model-input, and damage-models on prognostics. The approach utilizes sensitivity analysis to identify the dominant input variables that influence the model-output, and uses the distribution of measured load-parameters and input variables in a Monte-Carlo simulation to provide a distribution of accumulated damage. Using regression analysis of the accumulated damage distributions, the remaining life is then predicted with confidence intervals. The proposed method was demonstrated using an experimental setup for predicting interconnect failures on electronic board subjected to field conditions. A failure precursor based approach was developed for remaining life prognostics by analyzing resistance data in conjunction with usage temperature loads. Using the data from the PHM experiment, a model was developed to estimate the resistance based on measured temperature values. The difference between actual and estimated resistance value in time-domain were analyzed to predict the onset and progress of interconnect degradation. Remaining life was predicted by trending several features including mean-peaks, kurtosis, and 95% cumulative-values of the resistance-drift distributions.