Stiction is a well-known villain in industry because of the limit-cycle imposed on the controller. Several methodologies are reported in the literature to automatically detect this problem using only normal operating data. However, this becomes more difficult when the loop with stiction is affected by disturbances or the sticky valve is inside a cascade loop. This study proposes two methods to automatically diagnose valve stiction when the reference signal is variable and centers primarily on recognizing triangular or sinusoidal patterns. The first method is based on the slope of the signal peaks and the second on data segmentation. These techniques were compared to a curve-fitting method, providing similar results when the reference is fixed. However, for processes significantly affected by disturbances or when the sticky valve was inside a cascade loop, stiction detection was better for both methods proposed. These results are corroborated by simulation and industrial data.