Robust diagnosis framework based on sliding least squares estimate and directional discrepancy

Proposed in this paper is a diagnosis framework based on SLSE (Sliding Least Squares Estimate) and directional discrepancy. This framework can be used when linear dynamic equation and beforehand baseline data are not available. It is efficient, for SLSE works by recursive computation, removing the oldest datum while accepting the newest. Its computational complexity is low and asks for little memory space. Detection statistic is given by the prediction residual and isolation statistic by directional discrepancy. Both detection and isolation criterion are based on hypothesis-testing procedure. Another feature of the framework is that it can diagnosis unanticipated fault, which has never happened before, without wrongly assigning it as some anticipated. The proposed method is especially fit for real-time diagnosis of stochastic system without accurate dynamic equation or complete fault patterns. The application to fault diagnosis of satellite control system demonstrates its validity.