Device for forecasting a physical parameter measured by a sensorized rolling element and associated methods and system

A method for training a conditional imputation model configured to forecast a machine physical parameter from a set of machine operating conditions, the machine physical parameter being measured by a sensorized rolling element in a machine while the machine is operating, the machine comprising a rolling bearing including a stationary ring and a rotatable ring configured to rotate concentrically relative to one another and a plurality of rolling elements interposed between a first raceway of the stationary ring a second raceway of the rotatable ring, at least one of the plurality of rolling elements being the sensorized rolling element, the operating condition parameter being representative of the operating condition of the machine, the method comprising:

• determining at least one training set comprising time series values of the machine physical parameter measured by the sensorized rolling element in the machine while the machine is operating and time series values of the machine operating conditions associated with the time series values of the machine physical parameter,
• implementing the conditional imputation model to determine an output set of machine physical parameter time series values from the machine operating conditions time series values of the training set,
• comparing the machine physical parameter time series values of the training set and the output set, and
• tuning the conditional imputation model according to a result of the comparing to capture long-term dependencies in time series values and machine operating conditions time series values.