Combining Physics-Based Models with Signal-Based Methods for Machinery Diagnostics in Time-Varying Systems with Production and Environmental Risks

可靠性是一个组件或系统在规定的情况下，在规定的时间内，在最低标准以上执行其预期功能的概率。建模系统可靠性的研究关注减少设备和过程故障对安全、过程性能和环境的影响。良好的系统可靠性模型描述了过程对损害累积的影响（由于最终使系统不可靠的机制），使用系统的可观察特征，以减少不确定性和操作风险。系统可靠性的目标是根据观测到的系统输出和给定观测数据的系统状态的条件概率选择风险最低的选项。在该计划中，模型将结合联合收割机经验操作和维护知识，故障物理学和数据驱动模型，以产生更准确的机械故障检测，识别和随时间变化的服务，其中故障可能会影响过程性能，环境影响和安全性。将进行两项相互关联的调查，以减少油砂作业的影响。

Reliability is the probability that a component or system will perform its intended function above a minimum standard for a specified period of time in defined circumstances. Research on modeling system reliability concerns reducing the effect of equipment and process faults on safety, process performance, and the environment. Good models of system reliability describe the effect of the process on damage accumulation (due to mechanisms that eventually make the system unreliable), using observable features of the system, to reduce uncertainty and operational risk. The goal for system reliability is to choose the lowest risk option based on the observed system outputs and conditional probabilities of what state the system is in, given the observed data. In this program, models will combine empirical operating & maintenance knowledge, failure physics, and data-driven models to yield more accurate machinery fault detection, identification, and prognostics in time-varying service, where faults can affect process performance, environmental impacts, and safety. Two inter-related investigations will be undertaken, with applications to reducing the impact of oilsands operations.