Detection of anomalies in behavior of a device under test subjected to disturbances using AI algorithm trained on observations from when the DUT is not subjected to disturbances

A test apparatus for detecting anomalies in an operation behavior of a device under test, DUT, arranged in a test environment of said test apparatus, the test apparatus comprising:

• a monitoring unit adapted to generate a first set of observation data of the operation behavior of a device under test, DUT, in a time period where the device under test, DUT, is not subjected to disturbances and adapted to generate a second set of observation data of the operation behavior of a device under test, DUT, in a time period where the device under test (DUT) is subjected to disturbances, wherein the disturbances to which the device under test, DUT, is subjected to generate the second set of observation data comprise at least one of the following:
• electromagnetic disturbances,
• mechanical disturbances,
• environmental disturbances;
• an artificial intelligence, AI, module trained with the first set of observation data generated by the monitoring unit and adapted to process the second set of observation data to detect anomalies in the operation behavior of the device under test, DUT, while being subjected to the disturbances;
• and
• a reporting unit adapted to reporting anomalies in the operation behavior of the device under test, DUT, detected by the artificial intelligence, AI, module.

A non-transitory computer readable medium comprising a test software adapted to perform a method for detecting anomalies in an operation behavior of a device under test, DUT, the method comprising:

• monitoring the device under test, DUT, arranged in a test environment to generate a first set of observation data of the operation behavior of the device under test, DUT, in a time period where the device under test, DUT, is not subjected to disturbances;
• training an artificial intelligence, AI, algorithm with the generated first set of observation data of the operational behavior of the device under test, DUT, while the device is not subjected to disturbances;
• monitoring the device under test, DUT, arranged in the test environment to generate a second set of observation data of the operation behavior of the device under test, DUT, while subjecting the device under test, DUT, to disturbances;
• and
• processing the second set of observation data by the trained artificial intelligence, AI, algorithm to detect anomalies in the operation behavior of the device under test, DUT, in a time period where the device under test is subjected to the disturbances;
• and
• reporting detected anomalies in the operation behavior of the device under test, DUT.