Real-Time Regime Shift Detection With Root Cause Analysis
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Solution Overview
Problem
Current systems face inefficiencies in monitoring and identifying regime shifts due to the complexity of data from multiple sources, especially in multivariate data, leading to decreased system efficiency and product quality, as manual analysis and simple statistical techniques are time-consuming and ineffective.
Innovation Solution
A method and system utilizing machine learning techniques to continuously monitor systems, identify regime shifts in real-time based on key performance indicators (KPIs) and relevant features, detect root causes, and recommend rectification actions using optimization techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual analysis of data is used to identify regime shift, then analysis can be performed with simple tools, but it is time consuming and inefficient
Solution Approach 1:
The patent replaces manual mechanical analysis with automated machine learning models that process multivariate data streams. The system uses supervised learning algorithms trained on historical data to automatically detect regime shifts, eliminating the need for time-consuming manual analysis while maintaining or improving accuracy.
Solution Approach 2:
The system implements self-service through automated monitoring and detection mechanisms that continuously analyze data without human intervention. The machine learning models autonomously identify regime shifts and generate alerts, enabling the system to serve itself in detecting critical changes without requiring manual data processing.
2Reliability
If simple statistical techniques are used for identifying regime shift, then the approach is simple to implement, but they are not very effective on large and multivariate data
Solution Approach 1:
The patent transforms the analysis approach by changing from univariate statistical parameters to multivariate parameters that capture complex system behavior. The machine learning models analyze multiple correlated variables simultaneously, detecting patterns and interactions that simple statistical techniques miss, thereby improving reliability on multivariate data.
Solution Approach 2:
The system combines multiple analytical components into a composite solution: feature selection modules, multiple machine learning classifiers, and ensemble methods work together synergistically. This composite approach leverages the strengths of different algorithms and techniques to achieve superior performance on complex multivariate data compared to any single simple statistical method.
3Adaptability or versatility
If existing techniques for identifying regime shift are used, then they work on uni-variate data, but they are not very effective on multivariate data or data from multiple sources
Solution Approach 1:
The patent creates a universal system that handles diverse data types from multiple sources through a unified machine learning framework. The feature selection and preprocessing modules adapt to different data formats and sources, while the core detection algorithms maintain consistent performance across univariate, multivariate, and multi-source data scenarios.
Solution Approach 2:
The system transitions from analyzing single-dimensional univariate data to multi-dimensional multivariate analysis. By incorporating multiple variables and data sources simultaneously, the machine learning models capture complex interactions and patterns across dimensions, significantly improving detection accuracy for regime shifts in complex systems.
Data Source
AI summary
This disclosure relates generally to identification and analysis of regime shift. The identification and analysis of the regime shift includes regime shift identification (RSI), root cause analysis of the identified regime shift and a recommendation unit to rectify the identified regime shift. The disclosure proposes to monitor a system continuously to identify a regime shift at real-time as presence of regime shifts in any system decreases quality of process and products and makes the system less efficient. The regime shift is identified at real-time based on key performance indicators (KPIs), a set of relevant features and real time input data using machine learning techniques. Further the disclosure also proposes techniques for detecting at least one root cause for the identified regime shift and also recommends a rectification action to rectify the identified regime shift based on optimization techniques.