Intelligent diagnostics and prognostics for machinery systems

机械系统的智能诊断和预测

• 批准号: 312402-2010
• 负责人: Wang, Wilson(Quansheng)
• 金额: $ 1.75万
• 依托单位: Lakehead University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2010
• 资助国家: 加拿大
• 起止时间: 2010-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=457406
• 关键词: Intelligent; diagnostics; prognostics; machinery; systems

Production quality, operational safety, and economics have a direct impact on the competitiveness in the global market of organizations in Canada. A reliable online monitoring system is crucially needed in industries to provide an early warning of potential damage that will subsequently prevent machinery performance degradation, malfunction, and catastrophic failure. The principal disadvantages of classical monitoring systems are the lack of reliability and robustness, especially for time-varying operating conditions. The goal of this research is to develop a new generation of intelligent diagnostic and prognostic (IDP) systems for online, more reliable monitoring of the health conditions of machinery. When such an IDP system is implemented, it will be able to recognize the health conditions of machinery. When a potential problem arises, the IDP system can pinpoint the faulty components, estimate the fault propagation trend, and forecast the remaining useful life of the damaged unit. The first focus of this research is to develop a new technique to extract specific rotation waveforms to detect bearing faults in rotary machinery. Many techniques have been proposed in the literature for bearing fault detection; however, each has its own merits and limitations. The secondary objective is to comprehensively investigate the robustness of all classical and proposed signal processing techniques for bearing and motor fault detection corresponding to different conditions. A new transformation technique has been developed to map a model-based paradigm to a neural fuzzy prototype in an effort to further improve the performance of the resulting scheme. An IDP intelligent tool is proposed to effectively integrate the processing information from both the classifier and the predictor to provide a more positive assessment of the health conditions of machinery. New criteria and strategies for online/offline training will be developed to extract new knowledge during real-time operations that will increase adaptive capabilities and robustness of the monitoring systems and accommodate different machinery conditions.

生产质量、操作安全和经济性直接影响加拿大组织在全球市场的竞争力。工业中迫切需要可靠的在线监测系统，以提供潜在损坏的早期预警，从而防止机械性能下降，故障和灾难性故障。经典的监测系统的主要缺点是缺乏可靠性和鲁棒性，特别是对于时变的操作条件。这项研究的目标是开发新一代智能诊断和预测（IDP）系统，用于在线，更可靠地监测机器的健康状况。当这样一个国内流离失所者系统得到实施时，它将能够识别机器的健康状况。当潜在的问题出现时，IDP系统可以精确定位故障部件，估计故障传播趋势，并预测损坏单元的剩余使用寿命。本研究的第一个重点是发展一种新的技术来提取特定的旋转波形，以检测旋转机械中的轴承故障。在文献中已经提出了许多技术轴承故障检测，但是，每一个都有自己的优点和局限性。第二个目标是全面调查的鲁棒性的所有经典和建议的信号处理技术，轴承和电机故障检测对应于不同的条件。一种新的转换技术已被开发，以映射一个基于模型的范例的神经模糊原型，以进一步提高所得到的计划的性能。IDP智能工具，提出了有效地整合处理信息的分类器和预测器，提供一个更积极的评估健康状况的机械。将开发在线/离线培训的新标准和策略，以在实时操作期间提取新知识，这将提高监控系统的自适应能力和鲁棒性，并适应不同的机器条件。