Evaluating anomaly for one class classifiers in machine condition monitoring
View Patent ↗A method for monitoring machine conditions provides additional information using a one-class classifier in which an evaluation function is learned. In the method, a distance is determined from an anomaly measurement x to a boundary of a region R 1 containing all acceptable measurements. The distance is used as a measure of the extent of the anomaly. The distance is found by searching along a line from the anomaly to a closest acceptable measurement within the region R 1 .
1. A method for evaluating an anomaly measurement x′ in a machine condition monitoring system including a processor, wherein measurements x i are evaluated in a one-class classifier having a decision region R 1 for the class C 1 such that an evaluation function ƒ(x) is greater than or equal to a threshold T for a measurement x within the region, and less than T outside the region the method comprising the steps of:
training the one-class classifier to establish the decision region R 1 from a set of training samples {x 1 , x 2 , . . . , x N };
receiving the anomaly measurement x′;
determining that the anomaly measurement is outside the region R 1 ;
determining, using the processor, a distance from the measurement x′ to a boundary of the region R 1 ; and
evaluating the anomaly measurement x′ based on the distance;
wherein the step of determining a distance from the measurement x′ to the region R 1 further comprises the steps of:
finding a closest sample q, in the region R 1 , to the measurement x′;
defining an average point m between the x′ and q;
if a difference between ƒ(m) and T is greater than a permissible error, then if ƒ(m)<T, assign m to x′ and return to the defining step; and if ƒ(m)>T, assign m to q and return to the defining step; and
if the difference is less than the permissible error, assigning a distance from the measurement x′ to m as the distance from the measurement x′ to a boundary of the region R 1 .
2. The method of claim 1 , wherein the distance is a Euclidean distance.
3. The method of claim 1 , wherein the step of determining a distance from the measurement x′ to the region R 1 is performed iteratively.
4. The method of claim 1 , wherein the step of finding a closest sample q, in the region R 1 , to the measurement x′ further comprises using Euclidean distances between x′ and the samples.
5. The method of claim 1 , wherein the step of finding a closest sample q, in the region R 1 , to the measurement x′ further comprises the step of:
reducing a number of samples in the region R 1 by applying a k-mean algorithm.
6. The method of claim 1 , wherein the step of evaluating the anomaly measurement x′ based on the distance further comprises evaluating a degree of the anomaly based on the distance.
7. The method of claim 1 , wherein the step of defining an average point m between the measurement x′ and the closest sample q further comprises calculating
(x′+q)/2.
8. The method of claim 1 , further comprising the step of:
obtaining the evaluation function ƒ(x) using an algorithm selected from the group consisting of a nearest neighbor algorithm, a probability density function and an inner product for a support vector representation machine.
9. A computer-usable medium having computer readable instructions stored thereon for execution by a processor to perform a method evaluating an anomaly measurement x′ in a machine condition monitoring system wherein measurements x i are evaluated in a one-class classifier having a decision region R 1 for the class C 1 such that an evaluation function ƒ(x) is greater than or equal to a threshold T for a measurement x within the region, and less than T outside the region, the method comprising the steps of:
training the one-class classifier to establish the decision region R 1 from a set of training samples {x 1 , x 2 , . . . , x N };
receiving the anomaly measurement x′;
determining that the anomaly measurement is outside the region R 1 ;
determining a distance from the measurement x′ to a boundary of the region R 1 ; and
evaluating the anomaly measurement x′ based on the distance;
wherein the step of determining a distance from the measurement x′ to the region R 1 further comprises the steps of:
finding a closest sample q, in the region R 1 , to the measurement x′;
defining an average point m between the x′ and q;
if a difference between ƒ(m) and T is greater than a permissible error, then if ƒ(m)<T, assign m to x′ and return to the defining step; and if ƒ(m)>T, assign m to q and return to the defining step; and
if the difference is less than the permissible error, assigning a distance from the measurement x′ to m as the distance from the measurement x′ to a boundary of the region R 1 .
10. The computer-usable medium of claim 9 , wherein the distance is a Euclidean distance.
11. The computer-usable medium of claim 9 , wherein the step of determining a distance from the measurement x′ to the region R 1 is performed iteratively.
12. The computer-usable medium of claim 9 , wherein the step of finding a closest sample q, in the region R 1 , to the measurement x′ further comprises using Euclidean distances between x′ and the samples.
13. The computer-usable medium of claim 9 , wherein the step of finding a closest sample q, in the region R 1 , to the measurement x′ further comprises the step of:
reducing a number of samples in the region R 1 by applying a k-mean algorithm.
14. The computer-usable medium of claim 9 , wherein the step of evaluating the anomaly measurement x′ based on the distance further comprises evaluating a degree of the anomaly based on the distance.
15. The computer-usable medium of claim 9 , wherein the step of defining an average point m between the measurement x′ and the closest sample q further comprises calculating
(x′+q)/2.
16. The computer-usable medium of claim 9 , further comprising the step of:
obtaining the evaluation function ƒ(x) using an algorithm selected from the group consisting of a nearest neighbor algorithm, a probability density function and an inner product for a support vector representation machine.