Sensor and Actuator/Surface Failure Detection Based on the Spectral Norm of an Innovation Matrix

The problem of changes detection in dynamical properties of signals and systems appears in many problems of signal processing, navigation and control (Basseville & Benveniste, 1986; Benveniste et al., 1987; Gadzhiev, 1992; Chen & Patton, 1999; Chan et al., 1999; Hajiyev & Caliskan, 2003; Vaswani, 2004; Tykierko, 2008; Li & Jaimoukha, 2009). Abnormal measurements, sudden shifts appearing in the measuring channel, faultiness of measuring devices, changes in statistical characteristics of noises of an object or of measurements, malfunctions in the computer, and also a sharp change in the trajectory of a monitoring process, etc. should be enumerated among these changes. In real situations of exploiting an object, the problem occurs of operative detection of such changes in order to subsequently correct estimators or to make timely decisions on the necessity and character of control actions with respect to the process of technical exploitation of the object. Under this process, different methods of control and diagnostics are used. Many fault detection methods have been developed to detect and identify sensor and actuator faults by using analytical redundancy (Zhang & Li, 1997; Rago et al., 1998; Maybeck, 1999; Larson et al., 2002; Lee & Lyou, 2002). In (Larson et al., 2002) an analytical redundancy-based approach for detecting and isolating sensor, actuator, and component (i.e., plant) faults in complex dynamical systems, such as aircraft and spacecraft is developed. The method is based on the use of constrained Kalman filters, which are able to detect and isolate such faults by exploiting functional relationships that exist among various subsets of available actuator input and sensor output data. A statistical change detection technique based on a modification of the standard generalized likelihood ratio (GLR) statistic is used to detect faults in real time. The GLR test requires the statistical characteristics of the system to be known before and after the fault occurs. As this information is usually not available after the fault, the method has limited applications in practice. An integrated robust fault detection and isolation (FDI) and fault tolerant control (FTC) scheme for a fault in actuators or sensors of linear stochastic systems subjected to unknown inputs (disturbances) is presented in (Lee & Lyou, 2002). The FDI modules is constructed using banks of robust two-stage Kalman filters, which simultaneously estimate the state and the fault bias, and generate residual 10