METHOD FOR ANOMALY DETECTION/DIAGNOSIS, SYSTEM FOR ANOMALY DETECTION/DIAGNOSIS, AND PROGRAM FOR ANOMALY DETECTION/DIAGNOSIS

• US 20130073260A1
• Filed: 04/05/2011
• Published: 03/21/2013
• Est. Priority Date: 04/20/2010
• Status: Abandoned Application

• Current Assignee: Hitachi, Ltd.
• Original Assignee: Hitachi, Ltd.
• Inventors: Maeda, Shunji, Shibuya, Hisae, Magara, Hiroyuki
• Application Number:
  US13/641,886
• Publication Number:
  US 20130073260A1
• Time in Patent Office: Days
• US Class Current: 702/183
• CPC Class Codes: G05B 23/0224 Process history based detection method, e.g. whereby history implies the availability of large amounts of data
• CPC Class Codes: G06F 15/00 Digital computers in general details G06F1/00 – G06F13/00; Data processing equipment in general
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Abstract

In order to provide a method and system for anomaly detection/diagnosis that can detect anomalies quickly and with high sensitivity in a machinery such as a plant, in the present disclosures, sets of maintenance record information comprising operational records, replacement part information and other past cases are associated with each other on a keyword basis, anomalies are detected on the basis of anomaly detection that targets the output signal of a multidimensional sensor attached to the machinery, and by means of connecting the detected anomaly with the associated maintenance record information, the required diagnosis/measures with respect to the anomaly that has arisen are elucidated.

Claims

1. A method for anomaly detection/diagnosis that detects an anomaly of a plant or machinery, or a sign of an anomaly to diagnose the plant or the machinery, the method comprising the steps of:

detecting an anomaly of the plant or the machinery by using data obtained from plural sensors;

extracting a keyword from maintenance record information of the plant or the machinery;

generating a diagnosis model of the plant or the machinery by using the extracted keyword; and

diagnosing the plant or the machinery by using the generated diagnosis model.

2. The method for anomaly detection/diagnosis according to claim 1,

Wherein in the step of extracting, the maintenance record information includes any of on-call data, an operational report, an adjustment/replacement part code, image information, and sound information;

wherein in the step of generating, the frequency of appearance of the keyword set on the basis of the maintenance record information is calculated to obtain the pattern of the frequency of appearance, and

wherein in the step of diagnosing, the obtained pattern of the frequency of appearance is used as the diagnosis model, and the plant or the machinery is diagnosed using similarity between the pattern of the frequency of appearance of the diagnosis model and a keyword related to a newly-detected anomaly of the plant or the machinery.

3. The method for anomaly detection/diagnosis according to claim 1,

wherein in the step of extracting, a phenomenon diagnosis is made or a phenomenon is classified to express a relation between the sensors using the data obtained from the plural sensors, the frequency of appearance of a keyword that appears as a result is calculated, and

wherein in the step of diagnosing, similarity between the calculated frequency of appearance of the keyword and the pattern of the frequency of appearance of the keyword in the diagnosis model is calculated, and the plant or the machinery is diagnosed using the calculated similarity.

4. The method for anomaly detection/diagnosis according to claim 1,

wherein in the step of detecting, data is obtained from the plural sensors;

wherein in the step of generating, learning data mainly composed of normal data is modeled; and

wherein in the step of diagnosing, the anomaly measurement of the obtained data is calculated as a vector using the modeled learning data, and an anomaly is detected on the basis of the trajectory of the vector of the anomaly measurement over time.

5. A system for anomaly detection/diagnosis that detects an anomaly of a plant or machinery, or a sign of an anomaly to diagnose the plant or the machinery, the system comprising:

an anomaly detecting unit that detects an anomaly of the plant or the machinery using data obtained from plural sensors;

a database unit that accumulates maintenance record information of the plant or the machinery;

a diagnosis model generating unit that generates a diagnosis model of the plant or the machinery using a keyword extracted from the maintenance record information of the plant or the machinery accumulated in the database unit; and

a diagnosing unit that diagnoses the plant or the machinery by checking a newly-detected anomaly against the diagnosis model.

6. The system for anomaly detection/diagnosis according to claim 5,

wherein the database unit accumulates the maintenance record information including any of on-call data, an operational report, an adjustment/replacement part code, image information, and sound information;

wherein the diagnosis model generating unit calculates the frequency of appearance of the keyword set on the basis of the maintenance record information to obtain the pattern of the frequency of appearance, and uses the same as the diagnosis model; and

wherein the diagnosing unit diagnoses the machinery using similarity of the pattern of the frequency of appearance for a newly-detected anomaly.

7. The system for anomaly detection/diagnosis according to claim 5, further comprising:

a phenomenon diagnosing unit that expresses a relation between the sensors using the data obtained from the plural sensors or classifies a phenomenon;

wherein the diagnosing unit calculates the frequency of appearance of a keyword that appears through the phenomenon diagnosing unit to calculate similarity with the pattern of the frequency of appearance; and

the plant or the machinery is diagnosed using the calculated similarity.

8. The system for anomaly detection/diagnosis according to claim 5,

wherein the diagnosis model generating unit obtains data from the plural sensors to model learning data mainly composed of normal data;

and

wherein the diagnosing unit calculates the anomaly measurement of the obtained data as a vector using the modeled learning data, and detects an anomaly on the basis of the trajectory of the vector of the anomaly measurement over time.

9. A program for anomaly detection/diagnosis that quickly detects an anomaly of a plant or machinery, or a sign of an anomaly to make a diagnosis, the program comprising:

a processing step of detecting an anomaly using data obtained from plural sensors;

a processing step of generating a diagnosis model using the frequency of appearance of a keyword obtained from maintenance record information; and

a diagnosis processing step of diagnosing the plant or the machinery using the diagnosis model generated in the processing step of generating the diagnosis model.

10. The program for anomaly detection/diagnosis according to claim 9,

wherein in the processing step of detecting the anomaly, an anomaly is detected using the data obtained from the plural sensors;

wherein in the processing step of generating the diagnosis model, the diagnosis model is generated using the frequency of appearance of the keyword obtained from the maintenance record information; and

wherein in the step of diagnosis processing step, a pattern or a keyword is extracted through anomaly detection or a phenomenon diagnosis when the machinery is diagnosed using the diagnosis model generated in the diagnosis processing step, and the extracted pattern or keyword is used for a diagnosis.

11. A system for corporate asset management/machinery asset management comprising:

a database that stores maintenance record information composed of an operational report, replacement part information, and the like;

detecting means that allows a classifier such as a subspace method to detect an anomaly or a sign of an anomaly using signal information obtained from a multidimensional sensor attached to the machinery; and

diagnosing means that makes a diagnosis on the basis of the frequency pattern of a keyword focusing on a replacement part or an adjustment, wherein

the system performs anomaly/sign detection and a diagnosis triggered by the anomaly/sign detection.

12. The system for corporate asset management/machinery asset management according to claim 11, further including phenomenon classifying means which classify the detected anomaly or sign into phenomena.

13. The system for corporate asset management/machinery asset management according to claim 12, wherein the phenomenon classifying means that classifies the detected anomaly or sign into phenomena can edit the phenomena.

14. The system for corporate asset management/machinery asset management according to claim 11, wherein the phenomenon classifying means edit each item of the frequency pattern of the keyword.

15. The system for corporate asset management/machinery asset management according to claim 11, further comprising a display which displays the frequency pattern of the keyword and the displayed keywords can be edited as context of the machinery and a maintenance operation.

16. The system for corporate asset management/machinery asset management according to claim 11, wherein the diagnosing means groups each item of the frequency pattern of the keyword or selects them by time.

17. The system for corporate asset management/machinery asset management according to claim 11, wherein in the diagnosing means, the keyword is a word, a symbol, or a code set in the system, or a symbol output in a process of anomaly detection or the like.

18. The system for corporate asset management/machinery asset management according to claim 11, wherein in the diagnosing means, the frequency of appearance of the keyword is recorded as a pattern, and the maintenance record information can be reused by utilizing the pattern.