Developing innovative numerical/experimental schemes for monitoring and optimization of mechanical systems

开发用于监测和优化机械系统的创新数值/实验方案

• 批准号: RGPIN-2014-04097
• 负责人: Gadala, Mohamed
• 金额: $ 1.97万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=566294
• 关键词: Developing; innovative; numerical; experimental; schemes

One of the main rivals to productivity in various industries is unplanned downtime. To avoid any potential interruption in operation and unnecessary downtime, maintenance engineers need to acquire and process real-time monitoring data of machine parts to be able to respond in a smart and planned way. My overall research program has addressed this concern with developing combined generic numerical and experimental schemes for monitoring and optimization that can be translated into practical design tools for various industrial sectors to increase productivity, improve quality and avoid catastrophic failures. Long term goal of this research program is to provide industry with novel and efficient online monitoring techniques for rotating equipment and to facilitate the optimization of mechanical processes. The developed method will be aided to customize monitoring online approach for early detection of defects and faulty operating conditions oriented for bearings. A second target application will be advanced optimization of cooling process in steel making industry for producing tailored steels, reducing energy consumption and for green environment. The specific objectives of this 5-year research program are: a) to develop online monitoring tools for rotary machines with emphasize on roller element bearings and journal bearings and b) to develop a simulation tool to model and optimize the cooling process of steel plates on runout tables in steel mills. Faulty bearings may lead to significant failures to rotating machinery and the demand for effective and reliable health monitoring techniques for various bearings continues to grow. We intend to utilize online measurements with advanced signal processing in producing a design tool suitable for early diagnose of incipient defects and estimation of remaining safe life of faulty bearings. Advanced techniques of signal processing and optimization such as wavelet packet transform (WPT) and fuzzy logic and genetic algorithms (FGA) will be investigated and modified for the condition monitoring that combines vibration/acoustic measurements, finite element modeling and inverse analysis for detection and identification of faulty bearing. The second specific objective is concerned with optimizing the process parameters for cooling of steel plates on runout tables (ROT) in order to obtain steels with novel mechanical properties. The cooling in ROT is typically associated with boiling heat transfer and turbulent boiling impingement pool of water on a hot moving plate passing through multiple impinging water jets. Problems in surface temperature measurements led to measuring sub-surface temperature (1 mm below the surface) and to the use of inverse heat conduction (IHC) analysis to quantify heat fluxes and cooling rates. Various gradient-free and stochastic optimization techniques such as particle swarm method will be modified and implement into the IHC analysis to calculate surface temperature and heat flux from readings of the impeded thermocouples. Our extensive experimental database obtained from testing at a pilot scale UBC ROT facility will be used for validation of simulations and constructing a modern computational engineering model for ROT cooling to assess effects of key ROT variables in steel production such as plate temperature, plate velocity, jet configuration, and jet-lines spacing.

在各个行业中生产力的主要竞争对手之一是计划外的停机时间。为了避免操作和不必要的停机时间的任何潜在中断，维护工程师需要获取和处理机器零件的实时监视数据，以便能够以智能和计划的方式做出响应。我的整体研究计划已经解决了这一关注，通过开发合并的通用数值和实验方案，以监视和优化，可以将其转化为各个工业领域的实用设计工具，以提高生产率，提高质量并避免灾难性的失败。该研究计划的长期目标是为行业提供用于旋转设备的新颖有效的在线监控技术，并促进机械过程的优化。开发的方法将有助于自定义监视在线方法，以尽早发现缺陷和针对轴承的操作条件错误。第二个目标应用将是对钢制制造行业冷却过程的高级优化，用于生产量身定制的钢，减少能源消耗和绿色环境。该5年研究计划的具体目标是：a）为旋转机器开发在线监控工具，强调滚筒元件轴承和日记轴承以及b）开发模拟工具，以建模并优化钢厂中钢板上的钢板冷却过程。故障轴承可能会导致旋转机械的明显失败，并且对各种轴承的有效健康监测技术的需求继续增长。我们打算使用高级信号处理来利用在线测量，以生成适合早期诊断初期缺陷的设计工具，并估算剩余的轴承轴承寿命。信号处理和优化的先进技术，例如小波数据包变换（WPT）以及模糊逻辑和遗传算法（FGA），并将研究并修改，以结合振动/声学测量，有限元元素建模和倒数分析和鉴定故障轴承的状态监测，以进行振动/声学测量和倒数分析。第二个特定目标涉及优化跳线表（腐烂）上钢板冷却的过程参数，以便获得具有新型机械性能的钢。腐烂中的冷却通常与沸腾的传热和湍流的沸腾撞击池在热移动板上经过多个撞击水喷头。表面温度测量的问题导致测量地下温度（表面以下1 mm）和使用反热传导（IHC）分析以量化热通量和冷却速率。将修改各种无梯度和随机优化技术，例如粒子群方法，并在IHC分析中实现，以计算障碍物热电偶读数的表面温度和热通量。我们从试验量表UBC腐烂设施中获得的广泛的实验数据库将用于验证模拟并构建用于腐烂冷却的现代计算工程模型，以评估关键腐烂变量在钢生产中的影响，例如平板温度，板速度，喷射构型，喷射构型和喷气线间距。