Malfunction Detection Method and System Thereof

• US 20130173218A1
• Filed: 05/16/2011
• Published: 07/04/2013
• Est. Priority Date: 09/07/2010
• Status: Abandoned Application

• Current Assignee: Hitachi, Ltd.
• Original Assignee: Hitachi, Ltd.
• Inventors: Maeda, Shunji, Shibuya, Hisae
• Application Number:
  US13/702,531
• Publication Number:
  US 20130173218A1
• Time in Patent Office: Days
• US Class Current: 702/182
• CPC Class Codes: G05B 23/0221 Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods
• 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 17/00 Digital computing or data processing equipment or methods, specially adapted for specific functions information retrieval, database structures or file system structures therefor G06F16/00
• CPC Class Codes: G06Q 10/20 Administration of product repair or maintenance

Abstract

To allow early sensing of anomalies in a manufacturing plant or other infrastructure (plant), provided is a method that acquires data of runtime status of said plant from a plurality of sensors of said plant, makes a model from training data that corresponds to the regular runtime status of said plant, employs the training data thus modeled in computing a anomaly measure of the data acquired from the sensors, and detects anomalies. In computing the anomaly measure, the anomaly is detected by recursively carrying out: a derivation of a residual error from the training data thus modeled acquired from the plurality of sensors, a removal of a signal having a residual error that is greater than a predetermined value, and a computation of the anomaly measure for the data that is acquired from the plurality of sensors whereupon the signal having the large residual error is removed.

Claims

1. An anomaly detection method for detecting an anomaly of a plant or an installation, comprising the steps of:

obtaining data related to an operation state of the plant or the installation from plural sensors installed at the plant or the installation;

modeling learning data corresponding to nearly-normal data in a normal operation state of the plant or the installation;

calculating anomaly measurement of each data obtained from the plural sensors by using the modeled learning data; and

detecting an anomaly of the plant or the installation on the basis of the calculated anomaly measurement,

wherein in the step of calculating anomaly measurement, residual errors from the modeled learning data are obtained for the pieces of data obtained from the plural sensors, a signal having the residual error larger than a predetermined value is removed, and

wherein in the step of detecting an anomaly, anomaly detection is performed by recursively calculating the anomaly measurement for the data obtained from the plural sensors from which the signal having the large residual error is removed.

2. The anomaly detection method according to claim 1, wherein in the step of modeling, signals from the plural sensors are normalized in advance.

3. The anomaly detection method according to claim 1, wherein in the step of modeling, a predetermined weight is given to each sensor signal.

4. The anomaly detection method according to claim 1, wherein in the step of modeling, the learning data is modeled using that similar to the nearly-normal data in a normal operation state of the plant or the installation.

5. The anomaly detection method according to claim 4, wherein in the step of modeling, the learning data is modeled using that closer in distance and/or time to the nearly-normal data in a normal operation state of the plant or the installation.

6. A anomaly detection method for detecting an anomaly of a plant or an installation, comprising the steps of:

obtaining data related to an operation state of the plant or the installation from plural sensors installed at the plant or the installation;

modeling learning data corresponding to nearly-normal data in a normal operation state of the plant or the installation;

calculating anomaly measurement of each data obtained from the plural sensors by using the modeled learning data; and

detecting an anomaly of the plant or the installation on the basis of the calculated anomaly measurement,

wherein in the step of calculating anomaly measurement, residual errors from the modeled learning data are obtained for the pieces of data obtained from the plural sensors, a region to which a signal having the residual error larger than a predetermined value belongs or a signal belonging to the same category in terms of a function is removed, and

wherein in the step of detecting, anomaly detection is performed by recursively calculating the anomaly measurement for the data obtained from the plural sensors from which the signal having the large residual error is removed.

7. The anomaly detection method according to claim 6, wherein in the step of modeling, a sensor signal is normalized in advance.

8. The anomaly detection method according to claim 6, wherein in the step of modeling, a predetermined weight is given to each sensor signal.

9. The anomaly detection method according to claim 6, wherein in the step of modeling, the learning data is modeled using that similar to the nearly-normal data in a normal operation state of the plant or the installation.

10. The anomaly detection method according to claim 9, wherein in the step of modeling, the learning data is modeled using that closer in distance and/or time to the nearly-normal data in a normal operation state of the plant or the installation.

11. An anomaly detection system for detecting an anomaly of a plant or an installation, the system comprising:

a sensor data obtaining unit that obtains data related to an operation state of the plant or the installation from plural sensors installed at the plant or the installation;

a learning data modeling unit that models learning data corresponding to nearly-normal data in a normal operation state of the plant or the installation obtained from the sensor data obtaining unit;

an anomaly measurement calculating unit that calculates the anomaly measurement of each data obtained from the plural sensors using the learning data modeled by the learning data modeling unit; and

an anomaly detecting unit that detects an anomaly of the plant or the installation on the basis of the anomaly measurement calculated by the anomaly measurement calculating unit,

wherein the anomaly measurement calculating unit obtains residual errors from the modeled learning data for the pieces of data from the plural sensors obtained by the sensor data obtaining unit, removes a signal having the residual error larger than a predetermined value, and

wherein the anomaly detecting unit performs anomaly detection by recursively calculating the anomaly measurement for the data obtained from the plural sensors from which the signal having the large residual error is removed.

12. The anomaly detection system according to claim 11, wherein a signal from the sensor is normalized in advance.

13. The anomaly detection system according to claim 11, wherein in the learning data modeling unit, a predetermined weight is given to each sensor signal.

14. The anomaly detection system according to claim 11, wherein the modeling unit models the learning data using that similar to the nearly-normal data in a normal operation state of the plant or the installation.

15. The anomaly detection system according to claim 14, wherein the modeling unit models the learning data using that closer in distance and/or time to the nearly-normal data in a normal operation state of the plant or the installation.

16. An anomaly detection system for detecting an anomaly of a plant or an installation, the system comprising:

a sensor data obtaining unit that obtains data related to an operation state of the plant or the installation from plural sensors installed at the plant or the installation;

a learning data modeling unit that models learning data corresponding to nearly-normal data in a normal operation state of the plant or the installation obtained from the sensor data obtaining unit;

an anomaly measurement calculating unit that calculates the anomaly measurement of each data obtained from the plural sensors using the learning data modeled by the learning data modeling unit; and

an anomaly detecting unit that detects an anomaly of the plant or the installation on the basis of the anomaly measurement calculated by the anomaly measurement calculating unit,

wherein the anomaly measurement calculating unit obtains residual errors from the model for the pieces of data from the plural sensors obtained by the sensor data obtaining unit, removes a region to which a signal having the residual error larger than a predetermined value belongs or a signal belonging to the same category in terms of a function, and

wherein the anomaly detection unit performs anomaly detection by recursively calculating the anomaly measurement for the data obtained from the plural sensors from which the signal having the large residual error is removed.

17. The anomaly detection system according to claim 16, wherein in the anomaly measurement calculating unit, a sensor signal is normalized in advance.

18. The anomaly detection system according to claim 16, wherein in the anomaly measurement calculating unit, a predetermined weight is given to each sensor signal.

19. The anomaly detection system according to claim 16, wherein the modeling unit models the learning data using that similar to the nearly-normal data in a normal operation state of the plant or the installation.

20. The anomaly detection system according to claim 19, wherein the modeling unit models the learning data using that closer in distance and/or time to the nearly-normal data in a normal operation state of the plant or the installation.

21. A anomaly detection method, comprising the steps of:

setting targets of observed data with an attribute of time related to an operation state of a plant or an installation from plural sensors installed at the plant or the installation and learning data corresponding to nearly-normal data in a normal operation state of the plant or the installation;

expressing a motion of the observed data by a motion vector in a feature space;

selecting a learning data closer in distance to the observed data;

expressing a motion of the selected learning data by a motion vector; and

comparing an angle formed by the motion vector of the observed data and the motion vector of the learning data to a predetermined value to detect an anomaly.

22. An anomaly detection method, comprising the steps of:

setting a target of observed data with an attribute of time related to an operation state of a plant or an installation from plural sensors installed at the plant or the installation and learning data corresponding to nearly-normal data in a normal operation state of the plant or the installation;

selecting selected learning data from the learning data, one closer in distance to the observed data and the other closer in time to the one closer in distance in a feature space;

modeling the selected learning data;

selecting data closer in time to the observed data, modeling the observed data and the selected data closer in time, and calculating similarity among the modeled learning data, the modeled observed data, and the selected data closer in time; and

detecting anomaly on the basis of the calculated similarity.

23. An anomaly detection method, comprising the steps of:

setting targets of observed data with an attribute of time related to an operation state of a plant or an installation from plural sensors installed at the plant or the installation and learning data corresponding to nearly-normal data in a normal operation state of the plant or the installation;

selecting selected learning data from the learning data, one closer in distance to the observed data and the other closer in time to the one closer in distance in a feature space;

modeling the selected learning data in a low-dimensional subspace;

selecting data closer in time to the observed data and modeling the observed data and the selected data closer in time in the low-dimensional subspace;

calculating similarity among the subspaces of the modeled learning data, the modeled observed data, and the modeled selected-data closer in time; and

detecting anomaly using information of the calculated similarity of the subspaces.

24. An anomaly detection system, comprising:

input means that inputs observed data with an attribute of time related to an operation state of a plant or an installation from plural sensors installed at the plant or the installation;

learning data selecting means that, of learning data corresponding to nearly-normal data in a normal operation state of the plant or the installation, selects one closer in distance to the observed data;

vectorization means that expresses the motion of the observed data input by the input means using a motion vector in a feature space and the motion of the learning data selected by the learning data selecting means using a vector; and

anomaly detecting means that detects an anomaly by comparing an angle formed by the motion vector of the observed data vectorized by the vectorization means and that of the learning data with a predetermined value.

25. An anomaly detection system, comprising:

input means that inputs observed data with an attribute of time related to an operation state of a plant or an installation from plural sensors installed at the plant or the installation;

learning data selecting means that, of learning data corresponding to nearly-normal data in a normal operation state of the plant or the installation, selects one closer in distance to the observed data and the other closer in time to the one closer in distance;

modeling means that models the learning data selected by the learning data selecting means and selects data closer in time to the observed data input by the input means to model the observed data and the selected data closer in time; and

similarity calculating means that calculates similarity among the learning data, the observed data, and the selected data closer in time all of which are modeled by the modeling means, wherein

anomaly detection is performed on the basis of the similarity calculated by the similarity calculating means.

26. An anomaly detection system, comprising:

input means that inputs observed data with an attribute of time related to an operation state of a plant or an installation from plural sensors installed at the plant or the installation;

learning data selecting means that, of learning data corresponding to nearly-normal data in a normal operation state of the plant or the installation, selects one closer in distance to the observed data and the other closer in time to the one closer in distance;

modeling means that models the learning data selected by the learning data selecting means in a low-dimensional subspace and selects data closer in time to the observed data input by the input means to model the observed data and the selected data closer in time in the low-dimensional subspace;

subspace similarity calculating means that calculates similarity between the subspaces formed by the learning data modeled by the modeling means and the subspace formed by the observed data and the selected data closer in time; and

anomaly detecting means that detects an anomaly using the information of the similarity of the subspaces calculated by the subspace similarity calculating means.