Data-driven diagnosing for unanticipated fault by a general process model

The improvement of unanticipated fault detection and diagnosis (UFDD) capability is a difficult point, and is also a tendency for research and application. In this paper, a general process model (GPM) for unanticipated fault diagnosis is established. And combined with the characteristics of monitoring data, the corresponding diagnosis methods are researched. The model and the methods are used for online unanticipated fault detection, isolation and recognition. The GPM for the unanticipated fault diagnosis is designed, by adopting a three-layer progressive structure, which is comprised of an inherent detection layer (IDL), an unanticipated isolation layer (UIL) and an unanticipated recognition layer (URL). Several key problems in the GPM are analyzed, including the establishment and evaluation of detection statistics, the extraction of fault feature direction, and the design of fault isolation criterion and the calculation of contribution factor. The proposed model and methods are driven by pure data and they can detect and diagnose the unanticipated fault. The proposed approach is evaluated by using an example of a satellite's attitude control system, and excellent results have been obtained.