Symbolic time series analysis of ultrasonic signals for fatigue damage monitoring in polycrystalline alloys

2006 ◽  
Vol 17 (7) ◽  
pp. 1963-1973 ◽  
Author(s):  
Shalabh Gupta ◽  
Asok Ray ◽  
Eric Keller
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Fatigue Damage ◽  
Ultrasonic Signals ◽  
Series Analysis ◽  
Polycrystalline Alloys ◽  
Damage Monitoring ◽  
Symbolic Time Series Analysis

Symbolic Time Series Analysis for Anomaly Detection in Measure-Invariant Ergodic Systems

2020 ◽  
Vol 142 (6) ◽  
Author(s):  
Najah F. Ghalyan ◽  
Asok Ray
Keyword(s):  
Experimental Data ◽  
Time Series ◽  
Time Series Analysis ◽  
Anomaly Detection ◽  
Markov Chains ◽  
Fatigue Damage ◽  
Superior Performance ◽  
Series Analysis ◽  
Time Invariant ◽  
Symbolic Time Series Analysis

Abstract This paper presents a novel framework of symbolic time series analysis (STSA) for anomaly detection in dynamical systems. The core concept is built upon a property of measure-preserving transformation (MPT) sequence, acting on a probability space with ergodic measure, that the eigenfunctions of these transformations would be time-invariant. As a result, unlike a standard STSA that is required to generate time-homogeneous Markov chains, the proposed MPT-based STSA is allowed to have time-inhomogeneous Markov chains, where the (possibly time-varying) state transition probability matrices have time-invariant eigenvectors. Such a time-invariance facilitates analysis of the dynamical system by using short-length time series of measurements. This is particularly important in applications, where the underlying dynamics and process anomalies need fast monitoring and control actions in order to mitigate any potential structural damage and/or to avoid catastrophic failures. The MPT-based STSA has been applied for low-delay detection of fatigue damage, which is a common source of failures in mechanical structures and which is known to have uncertain dynamical characteristics. The underlying algorithm has been validated with experimental data generated from a laboratory apparatus that uses ultrasonic sensors to detect fatigue damage in polycrystalline–alloy specimens. The performance of the proposed MPT-based STSA is evaluated by comparison with those of a standard STSA and a hidden Markov model (HMM) on the same experimental data. The results consistently show superior performance of the MPT-based STSA.

Characterizing the dynamics of coupled pendulums via symbolic time series analysis

2013 ◽  
Vol 222 (2) ◽  
pp. 501-510 ◽  
Author(s):  
G. De Polsi ◽  
C. Cabeza ◽  
A.C. Marti ◽  
C. Masoller
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Series Analysis ◽  
Symbolic Time Series Analysis

scholarly journals An Independence Test Based on Symbolic Time Series

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Wiston Adrián Risso
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Time Series Analysis ◽  
Monte Carlo Simulations ◽  
Degrees Of Freedom ◽  
Chaotic Systems ◽  
Small Samples ◽  
Independence Test ◽  
Series Analysis ◽  
Symbolic Time Series Analysis

An independence test based on symbolic time series analysis (STSA) is developed. Considering an independent symbolic time series there is a statistic asymptotically distributed as a CHI-2 with n-1 degrees of freedom. Size and power experiments for small samples were conducted applying Monte Carlo simulations and comparing the results with BDS and runs test. The introduced test shows a good performance detecting independence in nonlinear and chaotic systems.

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