System and method for the dynamic analysis of event data

• US 10,514,977 B2
• Filed: 03/14/2014
• Issued: 12/24/2019
• Est. Priority Date: 03/15/2013
• Status: Active Grant

• Current Assignee: Dwayne LeRoy Mason, Richard B. Jones
• Original Assignee: Dwayne LeRoy Mason, Richard B. Jones
• Inventors: Jones, Richard B., Mason, Dwayne L.
• Primary Examiner(s): Iqbal, Nadeem
• Application Number:
  US14/784,868
• Publication Number:
  US 20160062820A1
• Time in Patent Office: 2,111 Days
• CPC Class Codes: G06F 11/0721 within a central processing unit [CPU]
• CPC Class Codes: G06F 11/079 Root cause analysis, i.e. error or fault diagnosis in a hardware test environment G06F11/22; in a software test environment G06F11/36
• CPC Class Codes: G06F 11/0793 Remedial or corrective actions recovery from an exception in an instruction pipeline G06F9/3861; by retry G06F11/1402; for recovering from a failure of a protocol instance or entity H04L69/40
• CPC Class Codes: G06F 18/2193 based on specific statistical tests
• CPC Class Codes: G07C 3/08 Registering or indicating the production of the machine either with or without registering working or idle time

Abstract

Disclosed is a system and method for the analysis of event data that enables analysts to create user specified datasets in a dynamic fashion. Performance, equipment and system safety, reliability, and significant event analysis utilizes failure or performance data that are composed in part of time-based records. These data identify the temporal occurrence of performance changes that may necessitate scheduled or unscheduled intervention like maintenance events, trades, purchases, or other actions to take advantage of mitigate or compensate for the observed changes. The criteria used to prompt a failure or performance record can range from complete loss of function to subtle changes in performance parameters that are known to be precursors of more severe events. These specific criteria applied to any explicit specific application and this invention is relevant to this type of data taxonomy and can be applied across all areas in which event data may be collected.

Claims

1. A computerized method, the method comprising:

programming, by a computer processor, a computer graphical user interface of a computer device of a user to allow the user to select, in real-time, a plurality of computer entities, displayed on the computer graphical user interface, into at least one ad hoc dynamic grouping;

wherein the plurality of computer visual entities of the computer graphical user interface are representative of a plurality of physical components;

wherein each physical component of the plurality of physical components is associated with at least one value-based data item representative of at least one of;

at least one condition of a respective physical component and at least one event related to the respective physical component;

determining, by the computer processor, a data taxonomy for the at least one ad hoc dynamic grouping;

wherein the data taxonomy is determined without performing data definition conversion for each value-based data item associated with each physical component of the plurality of physical components;

wherein the data taxonomy is configured to classify each value-based data item associated with each physical component of the plurality of physical components;

receiving, by the computer processor, via the computer graphical user interface, a user selection, identifying at least;

1) the at least one ad hoc dynamic grouping and

2) a specified time interval;

obtaining, by the computer processor, component data for the at least one ad hoc dynamic grouping based on the specified time interval, wherein the component data comprises a plurality of value-based data items associated with the plurality of physical components of the at least one ad hoc dynamic grouping;

generating, by the computer processor, based on the data taxonomy, a single dataset file from the component data associated with the plurality of physical components of the at least one ad hoc dynamic grouping;

applying, by the computer processor, at least one statistical trend analysis technique to the single dataset file associated with the plurality of physical components of the at least one ad hoc dynamic grouping to determine at least one trend in the component data;

transforming, by the computer processor, based at least in part on the at least one trend in the component data, the component data into an operational group model for the at least one ad hoc dynamic grouping of the single dataset file;

wherein the operational group model for the at least one ad hoc dynamic grouping of the single dataset file comprises a plurality of alert levels and a plurality of action levels; and

causing, by the computer processor, based at least in part on the operational group model, to generate at least one of;

i) an optimal preventive maintenance schedule for at least one physical component of the plurality of physical components of the at least one ad hoc dynamic grouping;

ii) at least one first alert comprising first data predicting a first time period to a next failure of the at least one physical component of the plurality of physical components of the at least one ad hoc dynamic grouping and, optionally, at least one first decision that suggests or requires at least one first change related to the next failure;

iii) at least one second alert comprising second data predicting a second time period to a next event associated with the at least one physical component of the plurality of physical components of the at least one ad hoc dynamic grouping and, optionally, at least one second decision that suggests or requires at least one second change related to the next event;

iv) at least one third alert comprising third data predicting a third time period between failures of the at least one physical component of the plurality of physical components of the at least one ad hoc dynamic grouping and, optionally, at least one third decision that suggests or requires at least one third change related to the failures;

v) at least one fourth alert comprising fourth data predicting a fourth time period between events associated with the at least one physical component of the plurality of physical components of the at least one ad hoc dynamic grouping, and, optionally, at least one fourth decision that suggests or requires at least one fourth change related to the events; and

vi) any combination thereof.

2. The method of claim 1, wherein the plurality of physical components and the at least one event are selected from the group consisting of:

1) physical components and events associated with at least one manufacturing plant,

2) physical components and events associated with at least one of the following industries;

medical, airline, social media, telecommunications, oil and gas, chemicals, hydrocarbon processing, pharmaceutical, and biotechnology, and

3) physical components and events associated with at least one securities market,

4) events associated with weather, and

5) physical components and events associated with at least one of housing and commercial real estate markets.

3. The method of claim 1, wherein the at least one value-based data item is representative of component ID data.

4. The method of claim 1, wherein the optimal preventive maintenance schedule comprises a preventive maintenance interval.

5. The method of claim 1, wherein at least one of the at least one first change, the at least one second change, the at least one third change, and the at least one fourth change comprises a trigger to buy or sell.

6. A computer system, comprising:

a computer program stored on a non-transitory storage subsystem of the computer system, comprising instructions that, when executed, cause a processor to execute the steps of;

receiving, a computer graphical user interface of a computer device of a user, a first user selection of a plurality of computer entities, displayed on the computer graphical user interface, into at least one ad hoc dynamic grouping;

wherein the plurality of computer visual entities of the computer graphical user interface are representative of a plurality of physical components;

wherein each physical component of the plurality of physical components is associated with at least one value-based data item representative of at least one of;

at least one condition of a respective physical component and at least one event related to the respective physical component;

determining a data taxonomy for the at least one ad hoc dynamic grouping;

wherein the data taxonomy is determined without performing data definition conversion for each value-based data item associated with each physical component of the plurality of physical components;

wherein the data taxonomy is configured to classify each value-based data item associated with each physical component of the plurality of physical components;

receiving, via the computer graphical user interface, a second user selection, identifying at least;

1) the at least one ad hoc dynamic grouping and

2) a specified time interval;

obtaining component data for the at least one ad hoc dynamic grouping based on the specified time interval, wherein the component data comprises a plurality of value-based data items associated with the plurality of physical components of the at least one ad hoc dynamic grouping;

generating, based on the data taxonomy, a single dataset file from the component data associated with the plurality of physical components of the at least one ad hoc dynamic grouping;

applying at least one statistical trend analysis technique to the single dataset file associated with the plurality of physical components of the at least one ad hoc dynamic grouping to determine at least one trend in the component data;

transforming, based at least in part on the at least one trend in the component data, the component data into an operational group model for the at least one ad hoc dynamic grouping of the single dataset file;

wherein the operational group model for the at least one ad hoc dynamic grouping of the single dataset file comprises a plurality of alert levels and a plurality of action levels; and

causing, based at least in part on the operational group model, to generate at least one of;

i) an optimal preventive maintenance schedule for at least one physical component of the plurality of physical components of the at least one ad hoc dynamic grouping;

ii) at least one first alert comprising first data predicting a first time period to a next failure of the at least one physical component of the plurality of physical components of the at least one ad hoc dynamic grouping and, optionally, at least one first decision that suggests or requires at least one first change related to the next failure;

iii) at least one second alert comprising second data predicting a second time period to a next event associated with the at least one physical component of the plurality of physical components of the at least one ad hoc dynamic grouping and, optionally, at least one second decision that suggests or requires at least one second change related to the next event;

iv) at least one third alert comprising third data predicting a third time period between failures of the at least one physical component of the plurality of physical components of the at least one ad hoc dynamic grouping and, optionally, at least one third decision that suggests or requires at least one third change related to the failures;

v) at least one fourth alert comprising fourth data predicting a fourth time period between events associated with the at least one physical component of the plurality of physical components of the at least one ad hoc dynamic grouping, and, optionally, at least one fourth decision that suggests or requires at least one fourth change related to the events; and

vi) any combination thereof.

7. The system of claim 6, wherein the plurality of physical components and the at least one event are selected from the group consisting of:

1) physical components and events associated with at least one manufacturing plant,

2) physical components and events associated with at least one of the following industries;

medical, airline, social media, telecommunications, oil and gas, chemicals, hydrocarbon processing, pharmaceutical, and biotechnology, and

3) physical components and events associated with at least one securities market,

4) events associated with weather, and

5) physical components and events associated with at least one of housing and commercial real estate markets.

8. The system of claim 6, wherein the at least one value-based data item is representative of component ID data.

9. The system of claim 6, wherein the optimal preventive maintenance schedule comprises a preventive maintenance interval.

10. The system of claim 6, wherein at least one of the at least one first change, the at least one second change, the at least one third change, and the at least one fourth change comprises a trigger to buy or sell.