EVALUATING ANOMALY FOR ONE-CLASS CLASSIFIERS IN MACHINE CONDITION MONITORING

• US 20090234607A1
• Filed: 03/23/2009
• Published: 09/17/2009
• Est. Priority Date: 12/07/2005
• Status: Active Grant

• Current Assignee: Siemens AG
• Original Assignee: Siemens Corporate Research Incorporated (Siemens AG)
• Inventors: Neubauer, Claus, Yuan, Chao
• Granted Patent: US 7,930,122 B2
• Time in Patent Office: Days
• US Class Current: 702/81
• CPC Class Codes: G05B 19/4065 Monitoring tool breakage, life or condition
• CPC Class Codes: G05B 2219/31357 Observer based fault detection, use model
• CPC Class Codes: G05B 2219/34477 Fault prediction, analyzing signal trends
• CPC Class Codes: G05B 23/024 Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
• CPC Class Codes: G05B 23/0281 Quantitative, e.g. mathematical distance; Clustering; Neural networks; Statistical analysis
• CPC Class Codes: G06F 18/2433 Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

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 R₁ 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 R₁.

Claims

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 R₁ for the class C₁ 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 the T outside the region, the method comprising the steps of;

training the one-class classifier to establish the decision region R₁ from a set of training samples {x₁, x₂, . . . , x_N};

receiving the anomaly measurement x′

;

determining that the anomaly measurement is outside the region R₁;

determining, using the processor, a distance from the measurement x′

to a boundary of the region R₁; and

evaluating the anomaly measurement x′

based on the distance;

wherein the step of determining a distance from the measurement x′

to the region R₁ is performed iteratively.

2. The method of claim 1, wherein the distance is a Euclidean distance.

3. 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 algorithms a probability density function and an inner product for a support vector representation machine.

4. 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 R₁ for the class C₁ 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 the T outside the region, the method comprising the steps of;

training the one-class classifier to establish the decision region R₁ from a set of training samples {x₁, x₂, . . . , x_N};

receiving the anomaly measurement x′

;

determining that the anomaly measurement is outside the region R₁;

determining a distance from the measurement x′

to a boundary of the region R₁; and

evaluating the anomaly measurement x′

based on the distance;

wherein the step of determining a distance from the measurement x′

to the region R₁ is performed iteratively.

5. The computer-usable medium of claim 4, wherein the distance is a Euclidean distance.

6. The computer-usable medium of claim 4, 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 unction and an inner product for a support vector representation machine.