Abstract

Modern industrial assets (e.g., generators, turbines, engines) are outfitted with numerous sensors to monitor key operating and environmental variables. Unusual sensor readings, such as high temperature, excessive vibration, or low current, could trigger rule-based actions (also known as faults) that range from warning alarms to immediate shutdown of the asset to prevent potential damage. In the case study of this article, a wind park experienced a sudden surge in vibration-induced shutdowns. We utilize fault data logs from the park with the goal of detecting common change points across turbines. Another important goal is the localization of fault occurrences to an identifiable set of turbines. The literature on change point detection and localization for multiple assets is highly sparse. Our technical development is based on the generalized linear modeling framework. We combine well-known solutions to change point detection for a single asset with a heuristics-based approach to identify a common change point(s) for multiple assets. The performance of the proposed detection and localization algorithms is evaluated through synthetic (Monte Carlo) fault data streams. Several novel performance metrics are defined to characterize different aspects of a change point detection algorithm for multiple assets. For the case study example, the proposed methodology identified the change point and the subset of affected turbines with a high degree of accuracy. The problem described here warrants further study to accommodate general fault distributions, change point detection algorithms, and very large fleet sizes.

摘要

现代工业资产（例如发电机、涡轮机、发动机）配备了许多传感器来监控关键的操作和环境变量。异常的传感器读数，例如高温、过度振动或低电流，可能会触发基于规则的操作（也称为故障），范围从警告警报到立即关闭资产以防止潜在损坏。在本文的案例研究中，一个风电场因振动引起的停机突然激增。我们利用来自公园的故障数据日志，目的是检测涡轮机之间的常见变化点。另一个重要目标是将故障发生定位到一组可识别的涡轮机。关于多资产变化点检测和定位的文献非常稀少。我们的技术开发基于广义线性建模框架。我们将用于单个资产的变化点检测的知名解决方案与基于启发式的方法相结合，以识别多个资产的共同变化点。所提出的检测和定位算法的性能通过合成（蒙特卡罗）故障数据流进行评估。定义了几个新的性能指标来表征多个资产的变化点检测算法的不同方面。对于案例研究示例，所提出的方法以高精度识别了变化点和受影响涡轮机的子集。这里描述的问题值得进一步研究以适应一般故障分布、变化点检测算法和非常大的车队规模。我们将用于单个资产的变化点检测的知名解决方案与基于启发式的方法相结合，以识别多个资产的共同变化点。所提出的检测和定位算法的性能通过合成（蒙特卡罗）故障数据流进行评估。定义了几个新的性能指标来表征多个资产的变化点检测算法的不同方面。对于案例研究示例，所提出的方法以高精度识别了变化点和受影响涡轮机的子集。这里描述的问题值得进一步研究以适应一般故障分布、变化点检测算法和非常大的车队规模。我们将用于单个资产的变化点检测的知名解决方案与基于启发式的方法相结合，以识别多个资产的共同变化点。所提出的检测和定位算法的性能通过合成（蒙特卡罗）故障数据流进行评估。定义了几个新的性能指标来表征多个资产的变化点检测算法的不同方面。对于案例研究示例，所提出的方法以高精度识别了变化点和受影响涡轮机的子集。这里描述的问题值得进一步研究以适应一般故障分布、变化点检测算法和非常大的车队规模。