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, WilsonQuansheng
• 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: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=526496
• 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、操作安全和经济对加拿大组织在全球市场上的竞争力有直接影响。一个可靠的在线监测系统在工业中是至关重要的，它可以提供潜在损害的早期预警，从而防止机械性能下降、故障和灾难性故障。传统监测系统的主要缺点是缺乏可靠性和鲁棒性，特别是对于时变工况。这项研究的目标是开发新一代智能诊断和预后（IDP）系统，用于在线，更可靠地监测机械的健康状况。当这样一个国内流离失所系统被实施时，它将能够识别机械的健康状况。当潜在的问题出现时，IDP系统可以精确定位故障部件，估计故障的传播趋势，并预测损坏部件的剩余使用寿命。本研究的第一个重点是开发一种提取特定旋转波形的新技术来检测旋转机械的轴承故障。文献中提出了许多用于轴承故障检测的技术；然而，每种方法都有其优点和局限性。第二个目标是全面研究针对不同条件的轴承和电机故障检测的所有经典和提出的信号处理技术的鲁棒性。为了进一步提高方案的性能，提出了一种新的转换技术，将基于模型的范式映射到神经模糊原型。提出了一种IDP智能工具，有效地整合了分类器和预测器的处理信息，以提供对机械健康状况更积极的评估。将为在线/离线培训制定新的标准和策略，以便在实时操作中提取新的知识，从而提高监测系统的自适应能力和鲁棒性，并适应不同的机械条件。