  Kalman filter is widely used for residual generation in fault detection. It
leads to optimality in fault detection using some performance indices and also
leads to statistically sound residual evaluation and threshold setting. This
paper shows that these nice features do not necessarily imply an optimal fault
detection performance. Based on a performance index related to fault detection
rate and false alarm rate, several occasions where Kalman filter should not be
used are pointed out; further the residual evaluation and threshold setting are
discussed, in which it is pointed out that in stochastic setting an optimal
statistical test of Kamlan filter is not related to optimality of commonly used
detection performance indicators. The theoretical analysis is verified through
Monte Carlo simulations and Tennessee Eastman process (TEP) dataset.
