Aiming at the problems that health state monitoring of displacement sensors mostly relies on external information, which causes low integration and other defects, this article conducts research on a self-monitoring method for the sensor′s grid-scale contamination state based on a self-developed absolute nano time-grating sensor. First, the measurement principle of the “dual precision measurement” absolute nano time-grating sensor is elaborated. Second, a characteristic error model for grid-scale contamination under a differential structure is established. Theoretical analysis shows that when the grid-scale is contaminated by oil contaminants of different morphologies, time-domain bias characteristic errors and frequency-domain second harmonic characteristic errors are introduced, establishing the mapping relationship between the grid-scale contamination state and the time-frequency domain characteristic errors. Then, by fully utilizing the high precision and high consistency of the dual precision measuring sensors, an internal reference benchmark is constructed for the sensor, and a method for self-extracting time-frequency domain characteristic errors based on this internal reference is proposed. By subtracting the displacement values of the dual precision measuring sensors, the time-frequency domain characteristic errors introduced into the displacement information under the grid-scale contamination state can be extracted. Time-frequency domain analysis and feature data extraction are then performed on the characteristic errors. Finally, the weighted K-nearest neighbor (WKNN) algorithm is employed to identify the grid-scale contamination state of the sensor, thereby achieving self-monitoring of the sensor′s grid-scale contamination state. Theoretical modeling and experimental analysis show that the grid-scale contamination state introduces time-domain bias characteristic errors and frequency-domain second harmonic characteristic errors. Using the weighted K-nearest neighbor (WKNN) algorithm to identify the grid-scale contamination state, an identification accuracy of 95% can be achieved. This study provides a theoretical foundation for improving the long-term reliability and environmental adaptability of absolute nano time-grating sensor.