Anomaly Detection/Diagnostic Method and Anomaly Detection/Diagnostic System

• US 20140195184A1
• Filed: 05/30/2012
• Published: 07/10/2014
• Est. Priority Date: 08/17/2011
• Status: Abandoned Application

• Current Assignee: Hitachi, Ltd.
• Original Assignee: Hitachi, Ltd.
• Inventors: Maeda, Shunji, Shibuya, Hisae
• Application Number:
  US14/239,114
• Publication Number:
  US 20140195184A1
• Time in Patent Office: Days
• US Class Current: 702/85
• CPC Class Codes: G01D 18/006 Intermittent recalibration
• CPC Class Codes: G01M 99/00 Subject matter not provided for in other groups of this subclass
• CPC Class Codes: G05B 21/02 electric
• CPC Class Codes: G05B 23/0235 based on a comparison with predetermined threshold or range, e.g. "classical methods", carried out during normal operation; threshold adaptation or choice; when or how to compare with the threshold
• CPC Class Codes: G05B 23/0283 Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL] preventive maintenance, i.e. planning maintenance according to the available resources without monitoring the system G06Q10/06

Abstract

Provided are an anomaly detection/diagnostic method and an anomaly detection/diagnostic system whereby it is possible, in equipment such as a plant, to detect anomalies promptly and with high sensitivity, wherein anomaly detection is carried out using operating information such as the operating time of the equipment and output signals from a plurality of sensors appended to the equipment, and wherein maintenance logs such as written procedure reports comprising procedure logs and instances of past countermeasures such as replacement part information are targeted to make associations between detected anomalies and countermeasures, and create links between anomaly detection and past maintenance logs, making reference to equipment records as well, while classifying and presenting anomalies that require action, thereby improving diagnostic accuracy.

Claims

1. An anomaly detection/diagnostic method used for detecting an anomaly of a plant or equipment or detecting an anomaly sign of said plant or said equipment and used for diagnosing said plant or said equipment, said anomaly detection/diagnostic method comprising:

detecting an anomaly of said plant or said equipment or detecting an anomaly sign of said plant or said equipment by taking sensor data acquired from a plurality of sensors installed in said plant or said equipment and/or operating data such as operation times and operating times as an object;

associating said anomaly of said plant or said equipment or said anomaly sign of said plant or said equipment with past countermeasures by making use of maintenance-history information of said plant or said equipment; and

classifying and presenting said anomaly requiring a countermeasure or said anomaly sign requiring a countermeasure on the basis of results of said association.

2. The anomaly detection/diagnostic method according to claim 1, wherein:

said maintenance-history information includes at least some of on-call data, work reports, adjustments/replacement part codes, video information and audio information;

an appearance frequency of a keyword determined from said maintenance-history information, the number of combinations with other keywords and a combination frequency are computed in order to obtain a pattern of a high appearance frequency;

said obtained pattern of said high appearance frequency is taken as a category;

said sensor data and said operating data of said anomaly detected at said plant or said equipment or said anomaly sign detected at said plant or said equipment are classified; and

on the basis of results of said classification, said anomaly requiring a countermeasure or said anomaly sign requiring a countermeasure is classified and presented.

3. The anomaly detection/diagnostic method according to claim 1, wherein:

operating data of said plant or operating data of said equipment is acquired;

sensor data is acquired from said sensors;

data included in said acquired sensor data and/or said acquired operating data as data composed of almost normal data is modeled as learning data;

said modeled learning data is used to compute an anomaly measure of said acquired sensor data and/or said acquired operating data as a vector; and

an anomaly of said plant or said equipment is detected on the basis of the magnitude of said computed anomaly measure vector or the angle of said vector.

4. The anomaly detection/diagnostic method according to claim 1, wherein:

said operating data is used to calibrate said acquired sensor data;

data included in said calibrated sensor data as data composed of almost normal data is modeled as learning data;

said modeled learning data is used to compute anomaly measure of said calibrated sensor data as a vector; and

an anomaly of said plant or said equipment is detected on the basis of the magnitude of said computed anomaly measure vector or the angle of said vector.

5. The anomaly detection/diagnostic method according to claim 1, further comprising

computing the success rate for a requested countermeasure proposal on the basis of a result of a countermeasure,

wherein sensitivity for an anomaly sign can be adjusted on the basis of said computed success rate.

6. The anomaly detection/diagnostic method according to claim 1, further comprising

generating and outputting equipment records.

7. An anomaly detection/diagnostic method used for detecting an anomaly of a plant or equipment or detecting an anomaly sign of said plant or said equipment and used for diagnosing said plant or said equipment, said anomaly detection/diagnostic method comprising:

detecting an anomaly of said plant or said equipment or detecting an anomaly sign of said plant or said equipment by taking sensor data acquired from a plurality of sensors installed in said plant or said equipment and/or operating data such as operation times and operating times as an object; and

carrying out state monitoring by making use of an image obtained from an image taking operation as an object.

8. An anomaly detection/diagnostic system used for detecting an anomaly of a plant or equipment or detecting an anomaly sign of said plant or said equipment and used for diagnosing said plant or said equipment, said anomaly detection/diagnostic system comprising:

an anomaly detection section for detecting an anomaly of said plant or said equipment or an anomaly sign of said plant or said equipment by taking sensor data acquired from a plurality of sensors installed in said plant or said equipment and/or operating data such as operation times and operating times as an object;

a database section for storing maintenance-history information comprising information such as countermeasures for said plant or said equipment; and

a diagnostic section for associating an anomaly detected by said anomaly detection section as an anomaly of said plant or said equipment or an anomaly sign detected by said anomaly detection section as an anomaly sign of said plant or said equipment with past countermeasures by making use of information stored in said database section to serve as maintenance-history information of said plant or said equipment and for classifying and presenting an anomaly requiring a countermeasure or an anomaly sign requiring a countermeasure on the basis of results of said association.

9. The anomaly detection/diagnostic system according to claim 8, wherein:

said maintenance-history information stored in said database section includes at least some of on-call data, work reports, adjustments/replacement part codes, video information and audio information;

said diagnosis-model generation section computes an appearance frequency of a keyword determined from said maintenance-history information, the number of combinations with other keywords and a combination frequency in order to obtain a pattern of a high appearance frequency;

said obtained pattern of said high appearance frequency is taken as a category;

said sensor data and said operating data of said anomaly detected at said plant or said equipment or said anomaly sign detected at said plant or said equipment are classified; and

on the basis of results of said classification, said anomaly requiring a countermeasure or said anomaly sign requiring a countermeasure is classified and presented.

10. The anomaly detection/diagnostic system according to claim 8, wherein said diagnosis-model generation section:

acquires operating data of said plant or operating data of said equipment and sensor data from said sensors installed in said plant or said equipment;

models data included in said acquired sensor data and/or said acquired operating data as data composed of almost normal data as learning data;

makes use of said modeled learning data in order to compute an anomaly measure of said sensor data acquired from said sensors or an anomaly measure of said operating data of said plant or said equipment as a vector; and

detects an anomaly of said plant or said equipment on the basis of the magnitude of said computed anomaly measure vector or the angle of said vector.

11. The anomaly detection/diagnostic system according to claim 8, wherein said diagnosis-model generation section:

makes use of said operating data to calibrate said acquired sensor data;

models data included in said calibrated sensor data as data composed of almost normal data as learning data;

makes use of said modeled learning data to compute an anomaly measure of said calibrated sensor data as a vector; and

detects an anomaly of said plant or said equipment on the basis of the magnitude of said computed anomaly measure vector or the angle of said vector.

12. The anomaly detection/diagnostic system according to claim 11, wherein said diagnosis-model generation section:

makes use of said operating data to calibrate said acquired sensor data;

models a data group comprising data included in said calibrated sensor data and data of other plants and other equipment as data composed of almost normal data as learning data;

makes use of said modeled learning data to compute an anomaly measure of said calibrated sensor data as a vector; and

detects an anomaly of said plant or said equipment on the basis of the magnitude of said computed anomaly measure vector or the angle of said vector.

13. The anomaly detection/diagnostic system according to claim 8, further comprising:

a countermeasure-proposal presenting section for presenting a countermeasure proposal; and

an success rate evaluation section for computing the success rate of said presented countermeasure proposal on the basis of a countermeasure result,

wherein sensitivity for an anomaly sign can be adjusted on the basis of a success rate computed by said success rate evaluation section.

14. An anomaly detection/diagnostic system used for detecting an anomaly of a plant or equipment or detecting an anomaly sign of said plant or said equipment and used for diagnosing said plant or said equipment, said anomaly detection/diagnostic system comprising:

an anomaly detection section for detecting an anomaly of said plant or said equipment or an anomaly sign of said plant or said equipment by taking sensor data acquired from a plurality of sensors installed in said plant or said equipment and/or operating data such as operation times and operating times as an object;

a diagnostic section for associating an anomaly of said plant or said equipment or an anomaly sign of said plant or said equipment with past countermeasures by making use of maintenance-history information of said plant or said equipment and for classifying and presenting an anomaly requiring a countermeasure or an anomaly sign requiring a countermeasure on the basis of results of said association; and

a record generation section for generating records of said equipment.