| CPC G06N 7/00 (2013.01) [G06N 20/00 (2019.01)] | 20 Claims |
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1. A method comprising:
receiving a scenario of machine operations, the machine operations impacted by a plurality of phenomena;
determining sources of the phenomena;
gathering training datasets of samples of the machine operations, corresponding to the phenomena;
generating a seed from each training datasets, the seed comprising a statistical representation of an independent and isolated source of a phenomena;
selecting seeds, based on the scenario;
generating single-sourced synthetic samples from a selected seed, and applying one or more physical constraints when generating the single-source synthetic samples to preserve a total energy of the generated single-source synthetic samples relative to a corresponding real signal;
combining the single-sourced synthetic samples, generating multi-phenomena synthetic samples simulating real samples from the machine operations, as impacted by the plurality of the phenomena;
training a failure prediction model with the multi-phenomena synthetic samples;
generating vibration data by an accelerometer of the sensor attached to a machine;
applying the generated vibration data to the trained failure prediction model; and
determining the occurrence of an operational anomaly of the machine by the trained failure prediction model based on the applied vibration data.
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