Bektas Oguz, Jones Jeffrey A, Sankararaman Shankar, Roychoudhury Indranil, Goebel Kai
Warwick Manufacturing Group, University of Warwick, Coventry, CV4 7AL, United Kingdom.
Pricewaterhouse Cooper, San Jose, CA, 95110, United States.
MethodsX. 2019 Feb 20;6:383-390. doi: 10.1016/j.mex.2019.02.015. eCollection 2019.
Prognostic performance is associated with accurately estimating remaining useful life. Difficulty in accurate prognostic applications can be tackled by processing raw sensor readings into more meaningful and comprehensive health condition indicators that will then provide performance information for remaining useful life estimations. To that end, typically, multiple tasks on data pre-processing and predictions have to be carried out such that tasks can be assessed using different methodological aspects. However, incompatible methods may result in poor performance and consequently lead to undesirable error rates. The present research evaluates data training and prediction stages. A data-driven prognostic method based on a feed-forward neural network framework is first defined to calculate the performance of a complex system. Then, the health indicators are used in a similarity based remaining useful life estimation method. This framework presents a conceptual prognostic protocol that overcomes challenges presented by multi-regime condition monitoring data.
预后性能与准确估计剩余使用寿命相关。通过将原始传感器读数处理成更有意义和全面的健康状况指标，可以解决准确预后应用中的困难，这些指标随后将为剩余使用寿命估计提供性能信息。为此，通常必须执行数据预处理和预测方面的多个任务，以便可以从不同的方法角度评估这些任务。然而，不兼容的方法可能导致性能不佳，从而导致不理想的错误率。本研究评估数据训练和预测阶段。首先定义一种基于前馈神经网络框架的数据驱动预后方法，以计算复杂系统的性能。然后，将健康指标用于基于相似度的剩余使用寿命估计方法中。该框架提出了一种概念性预后协议，克服了多工况状态监测数据带来的挑战。
