Real-Time Non-Destructive Evaluation of Fatigue Crack Damage for Health Monitoring of Mechanical Structures

提出的研究目标是开发用于机械结构和老化机械的健康监测和剩余寿命预测的实时无损评估（NDE）方法。具体目标是：疲劳裂纹损伤的量化和估计；以及对大面积疲劳损伤发生时间的评估。该技术方法依赖于来自基于物理的分析模型和实时传感器数据的异构信息的融合。研究的分析部分将由一个特殊用途的疲劳测试设备的实验室实验来支持，该设备配备了多种传感装置——光学、电磁和超声波。将多个传感器数据与疲劳裂纹扩展的随机状态空间模型融合，可以对当前损伤状态进行无损评估，并实时预测剩余寿命。这种软硬件组合构成了一种新颖的NDE系统，它可以在廉价的平台上执行，例如奔腾处理器，并且非常适合于适应操作机器。因此，NDE系统还可以作为控制应用中疲劳裂纹损伤的实时分析传感器。提议的NDE系统将提高广泛行业的安全性和生产率，从而降低生命周期成本。具体来说，NDE系统将适用于主动（例如涡轮机和泵）、半主动（例如热交换器管）和被动（例如压力容器）结构的实时健康监测和剩余寿命预测，这些结构会受到低周期和高周期疲劳损伤。此外，它将允许产生早期预警，以避免关键工厂部件即将发生故障，这将减少紧急停机和灾难性事故的可能性。

The goal of the proposed research is to develop real-time non-destructive evaluation (NDE) methods for health monitoring and residual life prediction of mechanical structures and aging machinery. The specific objectives are: quantification & estimation of fatigue crack damage; and assessment of the time to onset of widespread fatigue damage. The technical approach relies on fusion of heterogeneous information derived from physics-based analytical models and real-time sensor data. The analytical part of the research will be supported by laboratory experimentation on a special-purpose fatigue test apparatus that is equipped with multiple sensing devices - optical, electromagnetic, and ultrasonic. Fusion of multiple sensor data with a stochastic state-space model of fatigue crack growth will allow non-destructive evaluation of the current state of damage and prediction of the residual life in real time. This hardware-software combination constitutes a novel NDE system that can be executed on inexpensive platforms such as a Pentium processor and is ideally suited for adaptation to operating machinery. As such the NDE system will also serve as a real-time analytical sensor of fatigue crack damage for control applications. The proposed NDE system will enhance safety and productivity in a wide range of industries and thereby reduce life cycle costs. Specifically, the NDE system will be applicable to real-time health monitoring and residual life prediction in active (e.g., turbines and pumps), semi-active (e.g., heat exchanger tubes), and passive (e.g., pressure vessels) structures that are subjected to both low-cycle and high-cycle fatigue damage. Furthermore, it will allow generation of early warnings to avert impending failures of critical plant components, which will reduce the probability of emergency shutdowns and catastrophic accidents.