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

可靠性是指部件或系统在规定的环境下，在规定的一段时间内，超过最低标准执行其预期功能的概率。对系统可靠性建模的研究涉及减少设备和工艺故障对安全、工艺性能和环境的影响。良好的系统可靠性模型使用系统的可观察特性来描述过程对损害累积的影响(由于最终使系统不可靠的机制)，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系统所处状态的条件概率，在给定观测数据的基础上选择风险最低的选项。在该计划中，模型将结合经验操作和维护知识、故障物理学和数据驱动模型，在时变服务中产生更准确的机械故障检测、识别和预测，其中故障可能影响过程性能、环境影响和安全。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.