REMOTE DATA ANALYTICS TO PREDICT SYSTEM COMPONENTS OR DEVICE FAILURE

• US 20170269562A1
• Filed: 03/15/2016
• Published: 09/21/2017
• Est. Priority Date: 03/15/2016
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Dutta, Manas, Shetty, Praveen, Koniki, Ramesh Babu, Gurrapu, Praveen, Gellaboina, Mahesh Kumar, Mallavarpu, Sreedhara
• Granted Patent: US 10,139,788 B2
• Time in Patent Office: Days
• CPC Class Codes: G05B 13/048 using a predictor
• CPC Class Codes: G05B 23/0229 knowledge based, e.g. expert systems; genetic algorithms

Abstract

An apparatus includes a memory and one or more processors operably connected to the memory. The one or more processors are configured to receive data collected from a process facility system, detect anomalies for field device or process failures associated with the process facility system that are not monitored by alarms, detect leading indicators for field device or process failures that are monitored by alarms, and monitor the process facility system to detect further anomalies and leading indicators before failures occur.

Claims

1. An apparatus comprising:

a memory; and

one or more processors operably connected to the memory, the one or more processors configured to;

receive data collected from a process facility system;

detect anomalies for field device or process failures associated with the process facility system that are not monitored by alarms;

detect leading indicators for field device or process failures that are monitored by alarms; and

monitor the process facility system to detect further anomalies and leading indicators before failures occur.

2. The apparatus of claim 1, wherein to detect anomalies, the one or more processors are further configured to:

extract time series data from the collected data;

convert the collected data to a regularized time series;

apply a correlation to reduce dimensionality of the collected data converted to a regularized time series;

detect outlier data from the collected data; and

identify a distribution for normal behavior from the collected data after the outlier data is removed.

3. The apparatus of claim 2, wherein to monitor the process facility system to detect further anomalies, the one or more processors are further configured to identify anomalies from the normal behavior distribution.

4. The apparatus of claim 2, wherein the correlation comprises a Pearson correlation, and wherein a principal component analysis is used to detect the outlier data.

5. The apparatus of claim 1, wherein to detect leading indicators, the one or more processors are further configured to:

identify a trigger from alarm data of the process facility system;

determine one or more correlations using parameters related to the identified trigger;

perform dimensionality reduction on the determined one or more correlations; and

create a predictive model for the identified trigger.

6. The apparatus of claim 5, wherein to monitor the process facility system to detect further leading indicators, the one or more processors are further configured to deploy leading indicators for alarms from the predictive model.

7. The apparatus of claim 5, wherein a selection technique for performing dimensionality reduction is one of:

random forest or lasso.

8. A method comprising:

receiving data collected from a process facility system;

detecting anomalies for field device or process failures associated with the process facility system that are not monitored by alarms;

detecting leading indicators for field device or process failures that are monitored by alarms; and

monitoring the process facility system to detect further anomalies and leading indicators before failures occur.

9. The method of claim 8, wherein detecting anomalies further comprises:

extracting time series data from the collected data;

converting the collected data to a regularized time series;

applying a correlation to reduce dimensionality of the collected data converted to a regularized time series;

detecting outlier data from the collected data; and

identifying a distribution for normal behavior from the collected data after the outlier data is removed.

10. The method of claim 9, wherein monitoring the process facility system to detect further anomalies comprises identifying anomalies from the normal behavior distribution.

11. The method of claim 9, wherein the correlation comprises a Pearson correlation, and wherein a principal component analysis is used to detect the outlier data.

12. The method of claim 8, wherein detecting leading indicators comprises:

identifying a trigger from alarm data of the process facility system;

determining one or more correlations using parameters related to the identified trigger;

performing dimensionality reduction on the determined one or more correlations; and

creating a predictive model for the identified trigger.

13. The method of claim 12, wherein monitoring the process facility system to detect further leading indicators comprises deploying leading indicators for alarms from the predictive model.

14. The method of claim 12, wherein a selection technique for performing dimensionality reduction is one of:

random forest or lasso.

15. A non-transitory machine-readable medium encoded with executable instructions that, when executed, cause one or more processors to:

receive data collected from a process facility system;

detect anomalies for field device or process failures associated with the process facility system that are not monitored by alarms;

detect leading indicators for field device or process failures that are monitored by alarms; and

monitor the process facility system to detect further anomalies and leading indicators before failures occur.

16. The non-transitory machine-readable medium of claim 15, wherein the instructions to detect anomalies further comprise instructions to:

extract time series data from the collected data;

convert the collected data to a regularized time series;

apply a correlation to reduce dimensionality of the collected data converted to a regularized time series;

detect outlier data from the collected data; and

identify a distribution for normal behavior from the collected data after the outlier data is removed.

17. The non-transitory machine-readable medium of claim 16, wherein the instructions to monitor the process facility system to detect further anomalies comprise instructions to identify anomalies from the normal behavior distribution.

18. The non-transitory machine-readable medium of claim 16, wherein the correlation comprises a Pearson correlation, and wherein a principal component analysis is used to detect the outlier data.

19. The non-transitory machine-readable medium of claim 15, wherein the instructions to detect leading indicators comprise instructions to:

identify a trigger from alarm data of the process facility system;

determine one or more correlations using parameters related to the identified trigger;

perform dimensionality reduction on the determined one or more correlations;

and create a predictive model for the identified trigger.

20. The non-transitory machine-readable medium of claim 19, wherein the instructions to monitor the process facility system to detect further leading indicators comprise instructions to deploy leading indicators for alarms from the predictive model.