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
The present invention relates to a method and an apparatus for detecting anomalies in an operation behavior of a device under test (DUT), in particular during electromagnetic susceptibility (EMS) measurements. The apparatus comprises a monitoring unit adapted to generate a first set of observation data of the operation behavior of a DUT while the DUT is not subjected to disturbances and adapted to generate a second set of observation data of the operation behavior of a DUT while the DUT is subjected to disturbances and comprising an 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 DUT while being subjected to the disturbances and comprising a reporting unit adapted to reporting anomalies in the operation behavior of the DUT detected by the trained AI module.
1 . 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 implemented as an artificial neural network, using only the generated first set of observation data of the operational behavior of the device under test, DUT, during the time period where the DUT was not subject to disturbances, thereby excluding any observation data acquired while the DUT is 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, 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; 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.
2 . The method of claim 1 ,
wherein the test environment comprises a test chamber into which the device under test, DUT, is placed.
3 . The method of claim 1 ,
wherein the detected anomalies in the operation behavior of the device under test, DUT, detected by the trained artificial intelligence, AI, algorithm are automatically reported in a notification report.
4 . The method of claim 3 ,
wherein the notification report provides a rating of a significance of a detected anomaly in the operation behavior of the device under test, DUT.
5 . The method of claim 3 ,
wherein the notification report provides information where an anomaly in the operation behavior of the device under test, DUT, has been detected by the trained artificial intelligence, AI, algorithm.
6 . The method of claim 3 ,
wherein the notification report is announced via a warning message, an alarm signal or via a log-file.
7 . The method of claim 1 ,
wherein the trained artificial intelligence, AI, algorithm determines a probability that the detected anomalies in the operational behavior of the device under test, DUT, go beyond changes of the operational behavior of the device under test, DUT, to be expected due to disturbances.
8 . The method of claim 1 ,
wherein the electromagnetic disturbances are electromagnetic radiation generated by an antenna.
9 . The method of claim 1 ,
wherein the mechanical disturbances are vibrations applied by a plate.
10 . The method of claim 1 ,
wherein the environmental disturbances are at least one of the following:
humidity,
dust,
temperature variations,
pressure variations.
11 . The method of claim 1 ,
wherein the two sets of observation data comprises image data generated by at least one camera provided in the test environment.
12 . The method of claim 1 ,
wherein the two set of observation data comprises audio data generated by at least one microphone provided in the test environment.
13 . The method of claim 1 ,
wherein the method is stopped automatically once a significant anomaly in the operation behavior of the device under test, DUT, has been detected by the trained artificial intelligence, AI, algorithm.
14 . 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.
15 . The test apparatus of claim 14 ,
wherein the test environment of the test apparatus comprises a test chamber used to receive the device under test, DUT.
16 . The test apparatus of claim 14 ,
further comprising at least one camera adapted to provide image data or at least one microphone adapted to provide audio data stored as observation data for each set of observation data in a data memory of the test apparatus.
17 . 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.