System and method for validating unsupervised machine learning models

A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to perform a process for validating unsupervised machine learning models, the process comprising:

• analyzing, in real-time during operation of a machine, via unsupervised machine learning, a plurality of sensory inputs captured by sensors associated with the machine, wherein the unsupervised machine learning outputs in real-time at least one normal behavior pattern of the machine;
• generating, based on the at least one normal behavior pattern, at least one artificial anomaly, wherein each artificial anomaly deviates from the at least one normal behavior pattern;
• injecting the at least one artificial anomaly into the plurality of sensory inputs to create an artificial dataset;
• and
• analyzing the artificial dataset in real-time to determine whether a candidate model is a valid representation of operation of the machine, wherein analyzing the artificial dataset further comprises running the candidate model using the artificial dataset as an input.

A system for validating unsupervised machine learning models, comprising:

• a processing circuitry;
• and
• a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:
• analyze, in real-time during operation of a machine, via unsupervised machine learning, a plurality of sensory inputs captured by sensors associated with the machine, wherein the unsupervised machine learning outputs in real-time at least one normal behavior pattern of the machine via unsupervised machine learning, a plurality of sensory inputs associated with a machine, wherein the unsupervised machine learning outputs at least one normal behavior pattern of the machine;
• generate, based on the at least one normal behavior pattern, at least one artificial anomaly, wherein each artificial anomaly deviates from the at least one normal behavior pattern;
• inject the at least one artificial anomaly into the plurality of sensory inputs to create an artificial dataset;
• and
• analyze the artificial dataset in real-time to determine whether a candidate model is a valid representation of operation of the machine, wherein analyzing the artificial dataset further comprises running the candidate model using the artificial dataset as an input.