METHOD OF GENERATING AN ANOMALIES DETECTION MODEL AND METHOD OF DETECTING ANOMALIES USING SUCH MODEL

1 . A computer-implemented method for producing an anomaly detection model, the method comprising:

obtaining a learning data stream from a physical system;

incrementally computing principal components of the learning data stream;

performing orthonormalization of the principal components computed so as to obtain an orthonormal base representing the learning data stream; and

producing the anomaly detection model including the orthonormal base and a detection threshold defined by a user.

2 . The method according to claim 1 , wherein obtaining the learning data stream comprises using a sensor to monitor the physical system.

3 . The method according to claim 1 , wherein the incremental computing of the principal components of the data stream comprises using a method for analyzing covariance-free incremental principal components.

4 . The method according to claim 1 , wherein the orthonormalization comprises using a modified Gram-Schmidt algorithm using the principal components of the learning data stream.

5 . The method according to claim 1 , further comprising generating a computer program product for detecting anomalies comprising instructions which, when the computer program is executed by a computer, cause the computer to implement the anomaly detection model.

6 . The method according to claim 5 , further comprising executing the computer program to perform real time anomaly detection of a real-world physical system.

7 . The method according to claim 1 , wherein the detection threshold is greater than or equal to 70%.

8 . A non-transitory computer-readable storage medium storing a computer program product for implementing the method according claim 1 .

9 . A computer-implemented method for detecting anomalies, the method comprising:

obtaining a stream of data to be monitored from a sensor monitoring a physical system;

performing a vectorial projection of the data to be monitored onto an orthonormal base representing a learning data stream supplied by an anomaly detection model;

computing energy of the data projected onto the orthonormal base;

computing energy of the data to be monitored;

computing an energy ratio between the computed energy of the data to be monitored and the computed energy of the data projected onto the orthonormal base;

comparing the computed ratio with a detection threshold supplied by the anomaly detection model;

evaluating the data to be monitored as being normal data or abnormal data according to a result of the comparing; and

in response to evaluating the data as abnormal data, generating a notification indicating an anomaly within the physical system.

10 . The method according to claim 9 , wherein the sensor comprises an accelerometer and the physical system comprises rotary machine.

11 . The method according to claim 9 , wherein the physical system comprises an electrical apparatus and the data to be monitored provides information related to an electric current consumed by the apparatus.

12 . The method according to claim 9 , wherein evaluating the data comprises evaluating the data as being normal when the ratio is greater than or equal to the detection threshold and as being abnormal when the ratio is lower than the detection threshold.

13 . The method according to claim 9 , wherein evaluating the data comprises evaluating the data as being abnormal further, the method further comprising generating an alert signal in order to notify detection of an anomaly.

14 . The method of claim 9 , further comprising producing the anomaly detection model.

15 . The method of claim 14 , wherein producing the anomaly detection model comprises:

obtaining the learning data stream;

incrementally computing principal components of the learning data stream;

performing orthonormalization of the principal components computed so as to obtain the orthonormal base representing the learning data stream; and

producing the anomaly detection model including the orthonormal base and the detection threshold defined by a user.

16 . A non-transitory computer-readable storage medium storing a computer program product for implementing the method according claim 9 .

17 . A microcontroller comprising:

a processing unit; and

a non-transitory memory coupled to the processing unit and storing a computer program that includes instructions that, when executed by the processing unit, cause the processing unit to:

obtain a stream of data to be monitored;

perform a vectorial projection of the data to be monitored onto an orthonormal base representing a learning data stream supplied by an anomaly detection model;

compute energy of the data projected onto the orthonormal base;

compute energy of the data to be monitored;

compute an energy ratio between the computed energy of the data to be monitored and the computed energy of the data projected onto the orthonormal base;

perform a comparison of the computed ratio with a detection threshold supplied by the anomaly detection model; and

evaluate the data to be monitored as being normal data or abnormal data according to a result of the comparison.

18 . A system comprising:

the microcontroller according to claim 17 ;

a physical system; and

a sensor coupled to the physical system and the microcontroller.

19 . The system according to claim 18 , further comprising an alarm coupled to the microcontroller.

20 . The system according to claim 18 , wherein the sensor comprises an accelerometer and the physical system comprises rotary machine.

21 . The system according to claim 18 , wherein the physical system comprises an electrical apparatus and the sensor comprises a current sensor.