An Integrated Model-Based and Data-Driven Gap Metric Method for Fault Detection and Isolation
提出一种结合模型与数据驱动的间隙度量方法，在随机框架下检测和隔离执行器、传感器及部件故障，尤其针对早期故障，通过设计故障聚类中心模型和半径提高可隔离性。
This article proposes an integrated approach of model-based and data-driven gap metric fault detection and isolation in a stochastic framework. For actuator and sensor faults, an adaptive Kalman filter combining with the generalized likelihood ratio method is suggested. For component faults, especially incipient faults, the model-based scheme maybe not a good choice due to the existence of disturbances or noises. Hence, a novel data-driven gap metric strategy is presented. The design of the appropriate fault cluster center model and radius via the gap metric technique is put forward to enhance the isolability of the incipient faults. Numerical simulation results are given to demonstrate the effectiveness of the proposed fault detection and isolation algorithm.