Identifying deviating modules from a reference population for machine diagnostics

A computer program product comprising a computer-readable storage medium comprising instructions therein which, when executed by a computer system, are configured to cause the computer system to at least:

• obtain a first data set and a second data set, wherein the first data set comprises a plurality of features derived from first sensor data measured from a subject production apparatus, and the second data set comprises a plurality of features derived from second sensor data measured from one or more other production apparatuses;
• perform supervised training of a machine learning model using a first subset of the first data set and a first subset of the second data set, wherein the machine learning model is trained, upon receipt of a data item selectively from the first or second data sets, to output a classification value indicative of whether the received data item is from the first or second data sets;
• input a second subset of the first data set and/or a second subset of the second data set to the trained machine learning model to obtain corresponding classification values;
• evaluate the accuracy of the classification values;
• and
• use the evaluated accuracy of the classification values in performance of a diagnostic process for determination of the presence of a fault in the subject production apparatus.