A novel intelligent system for machinery condition monitoring and failure prognostics

可靠的状态监测和故障预测系统的开发一直是各种行业中各种事业的焦点，以防止机器/系统性能下降，故障，甚至灾难性故障。虽然在文献中已经提出了各种方法，机器当前和未来的健康状况的可靠评估仍然是一个具有挑战性的任务，在研究和现实世界的工业应用。这是因为大多数机器结构复杂，并且在噪声和时变环境中操作，其中传统的信号处理技术、模式分类方案和故障预测方法不能有效地实施。相应地，我提出的研究计划的目标是开发一种新的智能系统来应对这些挑战。最终目标是为工业监测和预测应用开发更可靠的技术，以提高产品质量，降低加拿大公司的制造成本。

The development of reliable condition monitoring and failure prognostic systems has been the focus of various undertakings in a wide array of industries to prevent machine/system performance degradation, malfunction, or even catastrophic failure. Although a variety of methods have been proposed in the literature, reliable assessment of machine current and future health conditions still remains a challenging task in both research and real-world industrial applications. This is because most machines are complex in structure and are operating in a noisy and time-varying environment, wherein the traditional signal processing techniques, pattern classification schemes, and failure prognostic approaches cannot be effectively implemented. Correspondingly, the objective of my proposed research program is to develop a novel intelligent system to address these challenges. The ultimate goal is to develop more reliable techniques for industrial monitoring and prognostic applications so as to improve product quality and to reduce the manufacturing cost in Canadian companies.