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停机时间。为了避免任何潜在的运行中断和不必要的停机时间，维护工程师需要获取和处理机器零件的实时监控数据，以便能够以智能和有计划的方式响应。我的总体研究计划解决了这一问题，开发了用于监测和优化的通用数值和实验方案，这些方案可以转化为各种工业部门的实用设计工具，以提高生产率，提高质量并避免灾难性故障。本研究计划的长期目标是为工业提供新颖有效的旋转设备在线监测技术，并促进机械过程的优化。所开发的方法将有助于定制在线监测方法，以早期检测面向轴承的缺陷和故障操作条件。第二个目标应用将是钢铁行业冷却过程的先进优化，以生产定制钢，降低能源消耗和绿色环境。这项为期5年的研究计划的具体目标是：a)开发用于旋转机器的在线监测工具，重点是滚子轴承和轴颈轴承；b)开发一种仿真工具，对钢厂跳动台上钢板的冷却过程进行建模和优化。轴承故障可能导致旋转机械的重大故障，对各种轴承有效可靠的健康监测技术的需求不断增长。我们打算利用在线测量和先进的信号处理来生产一种设计工具，适用于早期诊断早期缺陷和估计故障轴承的剩余安全寿命。将研究和改进先进的信号处理和优化技术，如小波包变换（WPT）、模糊逻辑和遗传算法（FGA），用于结合振动/声学测量、有限元建模和逆分析的状态监测，以检测和识别故障轴承。第二个具体目标是优化在跳动台（ROT）上冷却钢板的工艺参数，以获得具有新型机械性能的钢。ROT中的冷却通常与沸腾传热和热运动板上的水通过多个冲击水射流的湍流沸腾撞击池有关。表面温度测量中的问题导致测量亚表面温度（表面以下1毫米）和使用逆热传导（IHC）分析来量化热通量和冷却速率。各种无梯度和随机优化技术，如粒子群方法将被修改并实施到IHC分析中，以从受阻碍的热电偶的读数计算表面温度和热通量。我们从中试规模的UBC ROT设施测试中获得的广泛实验数据库将用于验证模拟和构建ROT冷却的现代计算工程模型，以评估钢铁生产中的关键ROT变量（如板温，板速度，射流配置和射流线间距）的影响。