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Identification of statistical patterns in complex systems via symbolic time series analysis
Shalabh Gupta1, Amol Khatkhate, Asok Ray
1The Pennsylvania State University, University Park, PA 16802, USA. szg107@psu.edu
ISA Transactions
|October 27, 2006
Summary
This study introduces an information-theoretic method for identifying statistical patterns in complex systems using symbolic time series analysis. This approach enhances structural integrity and operational reliability in engineered systems.
Area of Science:
- Complex Systems Analysis
- Information Theory
- Materials Science
Background:
- Monitoring complex engineered systems (e.g., power grids, transportation networks) requires identifying statistical patterns in sensor data.
- Existing methods may not fully capture the intricate dynamics crucial for performance and reliability.
Purpose of the Study:
- To present an information-theoretic approach for identifying statistical patterns in spatially distributed sensor time series data.
- To enhance structural integrity and operational reliability in human-engineered complex systems.
Main Methods:
- Utilizing principles from Symbolic Dynamics, Automata Theory, and Information Theory.
- Formulating a symbolic time series analysis method.
- Experimental validation using a dedicated test apparatus for fatigue damage analysis.
Main Results:
- Demonstrated the efficacy of the symbolic time series analysis method.
- Successfully applied the approach to real-time analysis of fatigue damage in polycrystalline alloys.
- Provided a framework for pattern identification to improve system reliability.
Conclusions:
- The proposed information-theoretic approach is effective for identifying critical statistical patterns in complex systems.
- Symbolic dynamics and information theory offer powerful tools for enhancing the monitoring and reliability of engineered systems.
- Experimental validation confirms the method's applicability in materials science for fatigue damage assessment.
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