A spate of industrial surveys over the past decade indicate that only about one-third of industrial
controllers provide acceptable performance. Since significant commercial benefits exist in
diagnosing and improving the remaining two-thirds of the industrial controllers, the past few
years have seen an emergence of control loop performance monitoring techniques using routine
operating data. About 20−30% of all control loops oscillate due to valve problems caused by
static friction or hysteresis. In the first of this two-part paper, a qualitative pattern recognition
approach is described for stiction diagnosis. Stiction in control valves leave distinct qualitative
shapes in the controller output (OP) and controlled process variable (PV) data. These shapes
can be generally categorized as being square, triangular, and saw-toothed. To classify the patterns
that evolve due to stiction, a pattern recognition approach using dynamic time warping (DTW)
technique is proposed. The success of our proposed approach is built on a new technique for
detection and time characterization of oscillations. A robust method for generating a stiction
template pattern for each oscillating cycle as opposed to a global pattern for the whole data set
is proposed. The qualitative approach was tested on data sets of varying complexity that include
nonconstant behavior, intermittent stiction, and external disturbances, and results for eight
data sets are presented.