Data analytic engine towards the self-management of complex physical systems

Abstract

Systems and method for modeling system dynamics, including extracting features representative of a temporal evolution of a dynamical system, further including deriving one or more vector trajectories by performing sliding window segmentation of one or more time series; applying a linear test to determine whether the one or more vector trajectories are linear or nonlinear; and performing linear or nonlinear subspace decomposition on the vector trajectory based on the linear test. The system and method may generate a system evolution model from the extracted features of the dynamical system and determine a fitness score of the system evolution model.

Description

RELATED APPLICATION INFORMATION[0001]This application claims priority to provisional application Ser. No. 61 / 713,006, filed on Oct. 12, 2012, and provisional application Ser. No. 61 / 719,425, filed on Oct. 28, 2012, each incorporated herein by reference.BACKGROUND[0002]1. Technical Field[0003]The present invention relates to complex systems, and, in particular, to modeling, analyzing, and managing time series dynamics in complex physical systems.[0004]2. Description of the Related Art[0005]Recently, cyber-physical systems (CPS) have risen to prominence with examples including automobile and intelligent transportation systems, medical devices and healthcare systems, smart grid, industrial monitoring, etc. Since CPS integrates computing, communication, and storage capabilities in the monitoring entities, a significant amount of measurement data can now be collected, which contains useful knowledge to provide an opportunity for system self-management. Modern industries, such as power pl...

AI Technical Summary

Benefits of technology

The patent is about a method and system for modeling the dynamics of a system using time-series data. The system extracts features from the data and creates a model that represents the system's evolution over time. The model is tested to determine if it accurately describes the system's behavior. This invention allows for better understanding and predictive analysis of complex systems, which could be useful in various fields such as science and medicine.

Problems solved by technology

Such methods require extensive human involvement and are domain specific.

Also, it may be difficult to obtain complete domain knowledge with the increasing of system scale and complexity.

However, their solutions need large amount historical data and are computationally expensive.

However, a number of challenges exist to extract knowledge from measurement data.

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