Advanced Machine Learning Techniques for Fault Diagnostics and Prognostics: From Modern Complex Manufacturing Systems to Healthcare

用于故障诊断和预测的先进机器学习技术：从现代复杂制造系统到医疗保健

• 批准号: RGPIN-2019-06966
• 负责人: Naderkhani, Farnoosh
• 金额: $ 1.89万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=681351
• 关键词: Advanced; Machine; Learning; Techniques; Fault

In today's globalized, interconnected, and competitive market, it is critical and of paramount importance that the Modern Complex Manufacturing and Service (MCMS) systems, including but not limited to aerospace, transportation and smart power grid, operate at their full potential with highest achievable reliability. Such MCMS systems are subject to random failures due to degradation and low-quality parts, which could lead to a variety of severe consequences ranging from destruction of infrastructures to endangering human lives. To avoid costly failures, tremendous efforts have to be invested in both Quality and Maintenance concepts, which are the target areas of the proposed research program. The objective of this research program, entitled "Advanced Machine Learning Techniques for Fault Diagnostics and Prognostic: From Modern Complex Manufacturing Systems to Healthcare," is to investigate and implement promising research ideas in the design, development, and application of state-of-the-art Artificial Intelligence (AI) and Machine Learning (ML) techniques that contribute to advancement of fault diagnostics/prognostics in process monitoring and maintenance. ******Recently, advancements in sensing technologies with progressive advancements in computation and communication technologies have resulted in exponential growth of high-dimensional and multi-modal streaming (HDMS) condition monitoring (CM) data. Efficient utilization of HDMS data leads to highly accurate prediction results in process/system health diagnostics/prognostics. To achieve the aforementioned goal (i.e., to efficiently utilize these ever growing sources of CM data), recently, there has been a great surge of interest in AI/ML based data-driven methodologies. Similar to digitalization which transformed end-to-end business models, the AI and ML are positioning themselves as the transformative technologies of the century leaving industries with two options: "Embrace the process/system monitoring via AI/ML solutions or get left behind''. ******The "Common Theme'' of this research program is to apply advanced, hybrid (i.e., coupled with state-of-the-art statistical methods), and deep AI/ML techniques for process quality control, maintenance management, and survival analysis. In particular, the proposed research program focuses to address the following rigid research challenges: (i) How to monitor and control manufacturing processes with HDMS data? (ii) How to design "deep'' representations to monitor/control manufacturing processes/systems with HDMS data? (iii) How to develop optimal maintenance policy for a MCMS system subject to degradation via HDMS data? How to combine event data and CM data? In conclusion, the proposed research program is believed to be timely and of significant importance for development of future quality control and CBM programs in Canada. Should the new knowledge prove as valuable as I hope, it is expected they will have a significant impact on our country.

在当今全球化、互联互通和竞争激烈的市场中，现代复杂制造和服务（MCMS）系统（包括但不限于航空航天、交通运输和智能电网）以最高可实现的可靠性充分发挥其潜力是至关重要的。这类MCMS系统容易因退化和低质量部件而发生随机故障，这可能导致从破坏基础设施到危及人类生命的各种严重后果。为了避免代价高昂的失败，必须在质量和维护概念上投入巨大的努力，这是所提议的研究计划的目标领域。这项名为“用于故障诊断和预测的先进机器学习技术：从现代复杂制造系统到医疗保健”的研究计划的目标是在设计、开发和应用最先进的人工智能（AI）和机器学习（ML）技术方面调查和实施有前途的研究思路，这些技术有助于在过程监控和维护中推进故障诊断/预测。******最近，随着计算和通信技术的进步，传感技术的进步导致了高维和多模态流（HDMS）状态监测（CM）数据的指数增长。有效利用HDMS数据可在过程/系统健康诊断/预测中获得高度准确的预测结果。为了实现上述目标（即，有效地利用这些不断增长的CM数据来源），最近，人们对基于AI/ML的数据驱动方法产生了极大的兴趣。与数字化改变了端到端商业模式类似，人工智能和机器学习将自己定位为本世纪的变革性技术，给行业带来了两个选择：“通过人工智能/机器学习解决方案接受流程/系统监控，否则就会落后”。******该研究计划的“共同主题”是应用先进的混合（即与最先进的统计方法相结合）和深度AI/ML技术进行过程质量控制，维护管理和生存分析。特别是，拟议的研究计划侧重于解决以下刚性研究挑战：(i)如何使用HDMS数据监测和控制制造过程？（ii）如何设计“深度”表示，以HDMS数据监测/控制制造过程/系统？（iii）如何通过HDMS数据为MCMS系统制定最佳维护策略？如何结合事件数据和CM数据？综上所述，该研究项目对加拿大未来质量控制和CBM项目的发展具有重要意义。如果这些新知识被证明如我所希望的那样有价值，预计它们将对我们国家产生重大影响。