International business machines corporation (20240160694). ROOT CAUSE ANALYSIS USING GRANGER CAUSALITY simplified abstract
Contents
- 1 ROOT CAUSE ANALYSIS USING GRANGER CAUSALITY
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ROOT CAUSE ANALYSIS USING GRANGER CAUSALITY - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
ROOT CAUSE ANALYSIS USING GRANGER CAUSALITY
Organization Name
international business machines corporation
Inventor(s)
Karthikeyan Shanmugam of Elmsford NY (US)
Bhanukiran Vinzamuri of Elmsford NY (US)
ROOT CAUSE ANALYSIS USING GRANGER CAUSALITY - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240160694 titled 'ROOT CAUSE ANALYSIS USING GRANGER CAUSALITY
Simplified Explanation
The patent application abstract describes techniques for root cause analyses based on time series data, utilizing a greedy hill climbing process to determine causality between variables in a mechanical system.
- The system described in the patent application includes a memory for storing computer executable components and a processor for executing these components.
- The computer executable components include a maintenance component that detects failure causes in a mechanical system by conducting conditional independence tests to establish Granger causality between variables from time series data.
Potential Applications
This technology can be applied in various industries such as manufacturing, automotive, aerospace, and healthcare for predictive maintenance and fault detection in complex systems.
Problems Solved
This technology helps in identifying the root causes of failures in mechanical systems, enabling proactive maintenance and reducing downtime and costly repairs.
Benefits
The benefits of this technology include improved system reliability, increased operational efficiency, cost savings through preventative maintenance, and enhanced safety by addressing issues before they escalate.
Potential Commercial Applications
Potential commercial applications of this technology include developing predictive maintenance software, integrating it into existing monitoring systems, and offering consulting services for implementing predictive maintenance strategies.
Possible Prior Art
One possible prior art could be traditional root cause analysis methods that rely on manual inspection and historical data rather than automated algorithms analyzing time series data for causality.
Unanswered Questions
How does this technology compare to other predictive maintenance solutions on the market?
This article does not provide a direct comparison with other predictive maintenance solutions, leaving the reader to wonder about the specific advantages and disadvantages of this technology in comparison to existing options.
What are the limitations of using time series data for root cause analyses in mechanical systems?
The article does not address the potential challenges or constraints of employing time series data for root cause analyses, leaving room for further exploration into the limitations of this approach.
Original Abstract Submitted
techniques regarding root cause analyses based on time series data are provided. for example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. the system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. the computer executable components can comprise maintenance component that can detect a cause of failure for a mechanical system by employing a greedy hill climbing process to perform a polynomial number of conditional independence tests to determine a granger causality between variables from time series data of the mechanical system given a conditioning set.
