Granger Causality Root Cause Analysis for Failure Onset Detection
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Solution Overview
Problem
Conventional root cause analysis of mechanical system failures relies heavily on human expertise, often failing to accurately identify when the root cause initiated, leading to delayed detection and increased financial losses.
Innovation Solution
An autonomous root cause analysis system using Granger causality and a greedy hill climbing process to perform a polynomial number of conditional independence tests on time series data, enabling efficient identification of the onset and root cause of mechanical system failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional analyses are used by subject matter experts, then human expertise can identify the root cause, but it is impossible to accurately identify when the root cause initiated
Solution Approach 1:
The patent replaces human expert analysis with an automated computational system that uses Granger causality tests and machine learning algorithms to analyze time series data. This substitution enables precise temporal identification of root cause onset by detecting changes in causal relationships in the data, overcoming the limitation of human experts who cannot accurately determine when the root cause initiated.
2Reliability
If exhaustive causal discovery algorithms are used, then complete causal relationships can be identified, but the run time increases exponentially
Solution Approach 1:
The patent segments the causal discovery process into manageable components: (1) performing Granger causality tests to identify potential causal relationships, (2) using conditional independence tests to filter spurious relationships, and (3) applying machine learning to rank and prioritize root causes. This segmentation reduces the computational complexity from exponential to polynomial time while maintaining reliable causal analysis.
Solution Approach 2:
The patent changes the parameters of the causal discovery algorithm by using Granger causality metrics and conditional independence thresholds to control the search space. By adjusting these parameters, the system achieves a balance between completeness of causal analysis and computational efficiency, avoiding exhaustive search while maintaining reliability.
3Reliability
If traditional failure analysis methods are used, then system failures can be detected, but the need for human interaction increases and false discovery rate increases
Solution Approach 1:
The patent implements a self-service automated system that performs complete root cause analysis without human intervention. The system automatically collects time series data, performs Granger causality tests, applies conditional independence filtering, and generates root cause reports. This automation eliminates human interaction requirements while reducing false discoveries through systematic statistical testing and machine learning validation.
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.