… fault detection is addressed within a statistical framework. A linear model with nuisance
parameters and a general covariance matrix … (for instance, to mask a fault). Two different invariant …

… the fastest detection of a fault and the detected minimum fault rate. … covariance matrices of
the innovation sequences in case of a fault and it can be used in the problems of fault detection…

… covariance matrix of the Kalman filter innovation sequence are compared with respect to
detected minimum fault rate and detection … the trace of the covariance matrix of the innovation …

… covariance matrix (ICSCM) and motor current signal analysis, which achieves multi-sensor
data fusion for rotating machinery fault detection. Firstly… machinery fault detection in this study. …

… covariance matrix is close to the classical covariance matrix. In the small fault case, the fault
detection is … In the huge fault case, for β = 0.5, it is not possible to detect faults. The robust …

… Considering the problem of incipient fault detection, we propose … detected by monitoring
the statistics of TCs. We also use rank-one modification to update the sample covariance matrix …

… cost due to the repeated EVD of the covariance matrix which is not practical for a real-time …
for online fault detection where an eigenvalue decomposition of a covariance matrix are …

… fault detection (in terms of nonlinear activation) in mechanical systems using a KPCA-based
method. The detection … may be found from the main p eigenvectors of the covariance matrix …

… In addition to process variability control, fault detection and isolation (FDI) is a key to …
based fault detection and isolation scheme, in which the trace of the covariance matrix of …

… on the change of the covariance matrices of the innovation sequences in the case of a fault
and can be used in the problems of fault detection and fault diagnosis of dynamic systems. …