Hierarchical anomaly detection and data representation method to identify system level degradation

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

• train first and second learning models of a diagnostic model for diagnosing a production system, based on training data derived from sensor data characterizing a plurality of sub-systems of the production system,
• wherein the diagnostic model comprises:
• for each sub-system of the plurality of sub-systems, a corresponding first learning model of the first learning models arranged to receive input data, and to generate compressed data for the corresponding sub-system in a corresponding latent space;
• and
• the second learning model arranged to receive the compressed data generated by the first learning models, and generate further compressed data for the production system in a further latent space.

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

• for each sub-system of a plurality of sub-systems of a subject production system, input data derived from sensor data characterizing the sub-system of the subject production system into a corresponding first trained learning model to generate corresponding compressed data for the sub-system of the subject production system in a corresponding latent space;
• input the compressed data generated by the first trained learning models into a second trained learning model to generate corresponding further compressed data for the subject production system in a further latent space;
• and
• diagnose the subject production system based on data generated by at least one selected from:
• (i) at least one first trained learning model of the first trained learning models and/or (ii) the second trained learning model.