Automated safety KPI enhancement

• US 10,572,796 B2
• Filed: 05/06/2015
• Issued: 02/25/2020
• Est. Priority Date: 05/06/2015
• Status: Active Grant

• Current Assignee: Saudi Arabian Oil Company (Government of Saudi Arabia)
• Original Assignee: Saudi Arabian Oil Company (Government of Saudi Arabia)
• Inventors: AlSaud, Zakarya Abu, Alkhabbaz, Fouad, Almadi, Soloman M., Abdullatif, Abduladhim
• Primary Examiner(s): Zhen, Li B.
• Assistant Examiner(s): Coughlan, Peter D
• Application Number:
  US14/705,650
• Publication Number:
  US 20160328641A1
• Time in Patent Office: 1,756 Days
• CPC Class Codes: G06N 3/04 Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/043 based on fuzzy logic, fuzzy membership or fuzzy inference, e.g. adaptive neuro-fuzzy inference systems [ANFIS]
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/06393 Score-carding, benchmarking or key performance indicator [KPI] analysis

Abstract

The present disclosure describes methods and systems, including computer-implemented methods, computer-program products, and computer systems, for automating a proactive Safety KPI analysis. Correlated data related to a safety key performance indicator (KPI) is obtained from a correlation engine. A safety KPI prediction related to safety incidents, future safety trends, or future safety KPIs is generated based on the received correlated data and at least one safety KPI prediction model. The generated safety KPI prediction is transmitted to a proactive monitoring and alerting engine and a safety KPI alert is generated based on the safety KPI prediction, at least one alert threshold, and the at least one safety KPI prediction model. Transmission of the generated safety KPI alert is then initiated.

Claims

1. A computer-implemented method executed by one or more processors, the method comprising:

obtaining, by the one or more processors, an operational status of machinery and employee information of persons operating the machinery, wherein the employee information comprises medical records, health conditions, corporate human resources records, education level, and training history of persons operating the machinery;

obtaining, by the one or more processors, correlated data related to a safety key performance indicator (KPI) of the machinery from a correlation engine, wherein the correlated data comprises a correlation of the operational status of machinery with one or more potential safety incidents and a correlation of the employee information with one or more potential safety incidents, the correlation correlating machinery use to discrete entities including specific employees and groups of employees, including correlating a distance of travel to a place of employment by an employee and transportation-related accidents;

determining, by the one or more processors, deviations between current operational status data and historical operational status data to identify one or more safety deviation factors;

generating, by the one or more processors, a safety KPI prediction related to the one or more potential safety incidents, future safety trends, or future safety KPIs based on the correlated data and at least one safety KPI prediction model, wherein the safety KPI prediction is generated based on the one or more safety deviation factors and one or more remediation factors given the operational status of machinery;

transmitting, by the one or more processors, the generated safety KPI prediction to a proactive monitoring and alerting engine;

generating, by the one or more processors, a safety KPI alert based on the safety KPI prediction, at least one alert threshold, and the at least one safety KPI prediction model;

automatically transmitting, by the one or more processors, the generated safety KPI alert; and

in response to automatically transmitting, updating, by the one or more processors, a feature or an operational method of the machinery associated with the KPI alert to proactively mitigate the one or more potential safety incidents.

2. The method of claim 1, wherein the correlation engine is communicably coupled with at least one database and at least one correlation model selected from the group consisting of a Work Dynamics model, a Statistical and Reporting model, and an Employee Behavioral model, wherein:

the Work Dynamics model comprises one or more models related to employee commutes or machinery that employees use; and

the Employee Behavioral model comprises one or more models related to employee health, employee jobs, employee age, hours worked, or medicines.

3. The method of claim 2, wherein the proactive monitoring and alerting engine can update an internal alert threshold based on data from the at least one database and the at least one correlation model.

4. The method of claim 1, wherein the generation is performed by at least one of a prediction engine selected from the group consisting of a neural network, fuzzy logic, probability logic, and an artificial intelligence engine.

5. The method of claim 4, wherein the prediction engine is self-learning.

6. The method of claim 4, wherein the at least one safety KPI prediction model is generated or updated by the at least one prediction engine based on the correlated data, the at least one safety KPI prediction model used by the proactive monitoring and alerting engine to generate the safety KPI alerts.

7. The method of claim 6, wherein the proactive monitoring and alerting engine receives data from the at least one safety KPI prediction model.

8. A non-transitory, computer-readable medium storing computer-readable instructions, the instructions executable by a computer and configured to:

obtain an operational status of machinery and employee information of persons operating the machinery, wherein the employee information comprises medical records, health conditions, corporate records, education level, and training history human resource of persons operating the machinery;

obtain correlated data related to a safety key performance indicator (KPI) of the machinery from a correlation engine, wherein the correlated data comprises a correlation of the operational status of machinery with one or more potential safety incidents and a correlation of the employee information with one or more potential safety incidents, the correlation correlating machinery use to discrete entities including specific employees and groups of employees, including correlating a distance of travel to a place of employment by an employee and transportation-related accidents;

determine deviations between current operational status data and historical operational status data to identify one or more safety deviation factors;

generate a safety KPI prediction related to the one or more potential safety incidents, future safety trends, or future safety KPIs based on the correlated data and at least one safety KPI prediction model, wherein the safety KPI prediction is generated based on the one or more safety deviation factors and one or more remediation factors given the operational status of machinery;

transmit the generated safety KPI prediction to a proactive monitoring and alerting engine;

generate a safety KPI alert based on the safety KPI prediction, at least one alert threshold, and the at least one safety KPI prediction model;

automatically transmit the generated safety KPI alert; and

in response to automatically transmitting, update a feature or an operational method of the machinery associated with the KPI alert to proactively mitigate the one or more potential safety incidents.

9. The medium of claim 8, wherein the correlation engine is communicably coupled with at least one database and at least one correlation model selected from the group consisting of a Work Dynamics model, a Statistical and Reporting model, and an Employee Behavioral model, wherein:

the Work Dynamics model comprises one or more models related to employee commutes or machinery that employees use; and

the Employee Behavioral model comprises one or more models related to employee health, employee jobs, employee age, hours worked, or medicines.

10. The medium of claim 9, wherein the proactive monitoring and alerting engine can update an internal alert threshold based on data from the at least one database and the at least one correlation model.

11. The medium of claim 8, wherein the generation is performed by at least one of a prediction engine selected from the group consisting of a neural network, fuzzy logic, probability logic, and an artificial intelligence engine.

12. The medium of claim 11, wherein the prediction engine is self-learning.

13. The medium of claim 11, wherein the at least one safety KPI prediction model is generated or updated by the at least one prediction engine based on the correlated data, the at least one safety KPI prediction model used by the proactive monitoring and alerting engine to generate the safety KPI alerts.

14. The medium of claim 13, wherein the proactive monitoring and alerting engine receives data from the at least one safety KPI prediction model.

15. A system, comprising:

a memory;

at least one hardware processor interoperably coupled with the memory and configured to;

obtain an operational status of machinery and employee information of persons operating the machinery, wherein the employee information comprises medical records, health conditions, corporate human resources records, education level, and training history of persons operating the machinery;

obtain correlated data related to a safety key performance indicator (KPI) of the machinery from a correlation engine, wherein the correlated data comprises a correlation of the operational status of machinery with one or more potential safety incidents and a correlation of the employee information with one or more potential safety incidents, the correlation correlating machinery use to discrete entities including specific employees and groups of employees, including correlating a distance of travel to a place of employment by an employee and transportation-related accidents;

determine deviations between current operational status data and historical operational status data to identify one or more safety deviation factors;

generate a safety KPI prediction related to the one or more potential safety incidents, future safety trends, or future safety KPIs based on the correlated data and at least one safety KPI prediction model, wherein the safety KPI prediction is generated based on the one or more safety deviation factors and one or more remediation factors given the operational status of machinery;

transmit the generated safety KPI prediction to a proactive monitoring and alerting engine;

generate a safety KPI alert based on the safety KPI prediction, at least one alert threshold, and the at least one safety KPI prediction model;

automatically transmit the generated safety KPI alert; and

in response to automatically transmitting, update a feature or an operational method of the machinery associated with the KPI alert to proactively mitigate the one or more potential safety incident.

16. The system of claim 15, wherein the correlation engine is communicably coupled with at least one database and at least one correlation model selected from the group consisting of a Work Dynamics model, a Statistical and Reporting model, and an Employee Behavioral model, wherein:

the Work Dynamics model comprises one or more models related to employee commutes or machinery that employees use; and

the Employee Behavioral model comprises one or more models related to employee health, employee jobs, employee age, hours worked, or medicines.

17. The system of claim 16, wherein the proactive monitoring and alerting engine can update an internal alert threshold based on data from the at least one database and the at least one correlation model.

18. The system of claim 15, wherein the generation is performed by at least one of a prediction engine selected from the group consisting of a neural network, fuzzy logic, probability logic, and an artificial intelligence engine.

19. The system of claim 18, wherein the prediction engine is self-learning.

20. The system of claim 18, wherein the at least one safety KPI prediction model is generated or updated by the at least one prediction engine based on the correlated data, the at least one safety KPI prediction model used by the proactive monitoring and alerting engine to generate the safety KPI alerts.

21. The system of claim 20, wherein the proactive monitoring and alerting engine receives data from the at least one safety KPI prediction model.