Kulkarni Chetan S, Daigle Matthew J, Gorospe George, Goebel Kai
SGT, Inc., NASA Ames Research Center, Moffett Field, CA, 94035, USA.
NASA Ames Research Center, Moffett Field, CA, 94035, USA.
Int J Progn Health Manag. 2017;8:018. Epub 2018 Jan 19.
Because valves control many critical operations, they are prime candidates for deployment of prognostic algorithms. But, similar to the situation with most other components, examples of failures experienced in the field are hard to come by. This lack of data impacts the ability to test and validate prognostic algorithms. A solution sometimes employed to overcome this shortcoming is to perform run-to-failure experiments in a lab. However, the mean time to failure of valves is typically very high (possibly lasting decades), preventing evaluation within a reasonable time frame. Therefore, a mechanism to observe development of fault signatures considerably faster is sought. Described here is a testbed that addresses these issues by allowing the physical injection of leakage faults (which are the most common fault mode) into pneumatic valves. What makes this testbed stand out is the ability to modulate the magnitude of the fault almost arbitrarily fast. With that, the performance of end-of-life estimation algorithms can be tested. Further, the testbed is mobile and can be connected to valves in the field. This mobility helps to bring the overall process of prognostic algorithm development for this valve a step closer to validation. The paper illustrates the development of a model-based prognostic approach that uses data from the testbed for partial validation.
由于阀门控制着许多关键操作，它们是部署预后算法的主要候选对象。但是，与大多数其他组件的情况类似，现场经历的故障实例很难获得。这种数据的缺乏影响了测试和验证预后算法的能力。有时用于克服这一缺点的一种解决方案是在实验室中进行直至故障的实验。然而，阀门的平均故障时间通常非常长（可能持续数十年），这使得在合理的时间框架内进行评估变得不可能。因此，人们寻求一种能够更快地观察故障特征发展的机制。这里描述的是一个试验台，它通过允许将泄漏故障（最常见的故障模式）物理注入气动阀门来解决这些问题。这个试验台的突出之处在于能够几乎任意快速地调节故障的大小。借此，可以测试寿命终止估计算法的性能。此外，该试验台是可移动的，可以连接到现场的阀门。这种移动性有助于使这种阀门的预后算法开发的整体过程更接近验证。本文阐述了一种基于模型的预后方法的开发，该方法使用来自试验台的数据进行部分验证。
