Root Cause Analysis Using Granger Causality for Failure Onset
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Solution Overview
Problem
Conventional root cause analysis of mechanical system failures often fails to accurately identify the onset of failures due to the propagation of malfunctions, requiring human expertise and being inefficient in determining Granger causalities between time series data variables.
Innovation Solution
A system employing a greedy hill climbing process to perform a polynomial number of conditional independence tests to determine Granger causality between variables from time series data, facilitating autonomous root cause analysis and onset detection in mechanical systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional analyses are used to identify root cause, then human expertise can fix the system after failure, but it is impossible to accurately identify when the root cause initiated
Solution Approach 1:
The patent applies preliminary action by performing Granger causality analysis on time series data to identify potential root causes and their onset times before actual system failure occurs. The system continuously monitors variables and detects causal relationships that precede failures, enabling early intervention rather than post-failure analysis by human experts.
Solution Approach 2:
The patent replaces the mechanical system of human expert analysis with an automated computational system that uses Granger causality tests and greedy hill climbing algorithms to identify root causes and their onset times. This substitution enables precise temporal identification of root causes that was previously impossible for human experts.
2Reliability
If exhaustive causal discovery algorithms are used to determine Granger causality, then complete causal relationships can be identified, but the computational complexity becomes exponential
Solution Approach 1:
The patent applies segmentation by dividing the causal discovery process into manageable components: (1) selecting candidate variables, (2) performing greedy hill climbing to identify parent variables, (3) conducting conditional independence tests, and (4) determining Granger causality. This segmentation reduces the problem from exponential complexity to polynomial complexity while maintaining reliability.
Solution Approach 2:
The patent uses partial action by implementing a greedy hill climbing approach that performs conditional independence tests only for necessary variable pairs rather than all possible combinations. This partial testing strategy reduces computational complexity from exponential to polynomial while still identifying the essential causal relationships needed for root cause analysis.
Data Source
AI summary
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.