Abstract: A method for identifying sensor drift can include: setting an autocorrelation threshold for a sensor in a long-short term memory (LSTM) model developed based on historical process measurements from an analogous sensor to a sensor; collecting measured data from the sensor; applying the LSTM model to the measured data from the sensor, wherein applying the LSTM model comprises: applying the LSTM model to the measured data from the sensor to yield LSTM predicted data; calculating key performance indicators (KPIs) of the LSTM data based on an accumulated slow drift error (ASDE) model, wherein the KPIs comprise an error, an accumulated prediction error, an accumulated slow-drift error, and an estimated autocorrelation; and identifying sensor drift when the estimated autocorrelation violates the autocorrelation threshold.

Abstract: A method for identifying sensor drift can include: setting an autocorrelation threshold for a sensor in a long-short term memory (LSTM) model developed based on historical process measurements from an analogous sensor to a sensor; collecting measured data from the sensor; applying the LSTM model to the measured data from the sensor, wherein applying the LSTM model comprises: applying the LSTM model to the measured data from the sensor to yield LSTM predicted data; calculating key performance indicators (KPIs) of the LSTM data based on an accumulated slow drift error (ASDE) model, wherein the KPIs comprise an error, an accumulated prediction error, an accumulated slow-drift error, and an estimated autocorrelation; and identifying sensor drift when the estimated autocorrelation violates the autocorrelation threshold.