ANOMALY DETECTION METHOD AND ANOMALY DETECTION SYSTEM

• US 20120316835A1
• Filed: 12/16/2010
• Published: 12/13/2012
• Est. Priority Date: 01/14/2010
• Status: Abandoned Application

• Current Assignee: Hitachi, Ltd.
• Original Assignee: Hitachi, Ltd.
• Inventors: Maeda, Shunji, Shibuya, Hisae
• Application Number:
  US13/521,767
• Publication Number:
  US 20120316835A1
• Time in Patent Office: Days
• US Class Current: 702/183
• CPC Class Codes: G01D 3/08 with provision for safeguarding the apparatus, e.g. against abnormal operation, against breakdown
• CPC Class Codes: G05B 23/0232 based on qualitative trend analysis, e.g. system evolution
• CPC Class Codes: G06F 18/21375 involving differential geometry, e.g. embedding of pattern manifold
• CPC Class Codes: G06F 2218/12 Classification; Matching

Abstract

A method and system for detecting an anomaly or a fault in equipment such as a plant. A method of representing the state of the equipment is offered. Output signals from multidimensional sensors are treated as subjects. (1) Normal learning data is created. (2) An anomaly measurement is calculated by a subspace classifier or other method. (3) Trajectories of motions of observational data and learning data are evaluated and their errors are calculated by a linear prediction method or the like. (4) The state of the equipment is represented using the anomaly measurement and the trajectories of the motions. (5) A decision is made regarding an anomaly. A case-based reasoning anomaly detection consists of modeling the learning data by the subspace classifier and detecting candidate anomalies based on the distance relationship between the observational data and the subspace. The trajectories of the motions are based on modeling using a linear prediction method.

Claims

1. An anomaly detection method for early detecting an anomaly or a fault in a plant or equipment, said method comprising the steps of:

acquiring data from a plurality of sensors;

modeling learning data consisting mostly of normal data;

calculating an anomaly measurement of the acquired data using the modeled learning data;

modeling time-sequential behavior of the acquired data by linear prediction;

calculating prediction errors from the models; and

detecting whether there is an anomaly or a fault using both the anomaly measurement and the prediction errors.

2. The anomaly detection method according to claim 1, said method further comprising the steps of:

calculating an anomaly measurement of the acquired data as a vector using the modeled learning data;

calculating prediction errors from the models as a prediction error vector; and

detecting whether there is an anomaly or a fault using a combination of the anomaly measurement vector and the prediction error vector.

3. An anomaly detection method for early detecting an anomaly or a fault in a plant or equipment, said method comprising the steps of:

acquiring data from a plurality of sensors;

preparing given orders or determining orders based on distances between data sets whenever data is acquired;

modeling the acquired data by linear prediction;

calculating a prediction error from the model; and

detecting whether there is an anomaly or a fault.

4-10. -10. (canceled)

11. An anomaly detection system for early detecting an anomaly or a fault in a plant or equipment, wherein

data is acquired from a plurality of sensors;

learning data consisting mostly of normal data is modeled;

an anomaly measurement of the acquired data is calculated using the modeled learning data;

time-sequential behavior of the acquired data is modeled by linear prediction;

prediction errors from the models are calculated; and

is detected as to whether there is an anomaly or a fault, using both anomaly measurement and prediction errors.

12. The anomaly detection system according to claim 11, wherein

an anomaly measurement of the acquired data is calculated as a vector using the modeled learning data,

prediction errors from the models are calculated as a prediction error vector, and

it is detected as to whether there is an anomaly or a fault, using a combination of the anomaly measurement vector and the prediction error vector.

13-15. -15. (canceled)