A new method proposed here detects, reconstructs, and identifies faulty sen
sors using a normal process model, which can be built from first principles
or statistical methods such as partial least squares or principal componen
t analysis. The model residual is used to detect sensor faults that demonst
rate a deviation from the normal process model. To identify which sensor is
faulty, a structured residual approach with maximized sensitivity is propo
sed to make one residual insensitive to one subset of faults but most sensi
tive to other faults. The structured residuals are subject to exponentially
weighted moving average filtering to reduce the effect of noise and dynami
c transients. The confidence limits for these filtered structured residuals
are determined using statistical inferential techniques. In addition, othe
r indices including generalized likelihood ratio test, cumulative sum, and
cumulative variance of the structured residuals are compared to identify fa
ulty sensors. The fault magnitude is then estimated based on the model and
faulty data. Four types of sensor faults, including bias, precision degrada
tion, drifting and complete failure, are simulated to test this method Data
from an industrial boiler process are used to test its effectiveness. Both
single faults and simultaneous double faults are detected and uniquely ide
ntified with the method.