TY - JOUR
T1 - Change Detection in Complex Dynamical Systems Using Intrinsic Phase and Amplitude Synchronization
AU - Iquebal, Ashif Sikandar
AU - Bukkapatnam, Satish
AU - Srinivasa, Arun
N1 - Funding Information:
Manuscript received March 11, 2020; revised June 24, 2020; accepted July 20, 2020. Date of publication August 7, 2020; date of current version August 28, 2020. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Sotirios Chatzis. This work was supported by the kind funding from National Science Foundation, under Grant CMMI-1432914, CMMI-1437139, IIP-1543226, IIP-1355765, and ECCS-1547075. (Corresponding author: Ashif Sikandar Iquebal.) Ashif Sikandar Iquebal and Satish Bukkapatnam are with the Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX 77843 USA (e-mail: ashif_22@tamu.edu; satish@tamu.edu).
Publisher Copyright:
© 1991-2012 IEEE.
PY - 2020
Y1 - 2020
N2 - We present an approach for the detection of sharp change points (short-lived and persistent) in nonlinear and nonstationary dynamic systems under high levels of noise by tracking the local phase and amplitude synchronization among the components of a univariate time series signal. The signal components are derived via Intrinsic Time scale Decomposition (ITD)-a nonlinear, non-parametric analysis method. We show that the signatures of sharp change points are retained across multiple ITD components with a significantly higher probability as compared to random signal fluctuations. Theoretical results are presented to show that combining the change point information retained across a specific set of ITD components offers the possibility of detecting sharp transitions with high specificity and sensitivity. Subsequently, we introduce a concept of mutual agreement to identify the set of ITD components that are most likely to capture the information about dynamical changes of interest and define an InSync statistic to capture this local information. Extensive numerical, as well as real-world case studies involving benchmark neurophysiological processes and industrial machine sensor data, suggest that the present method can detect sharp change points, on an average 62% earlier (in terms of average run length) as compared to other contemporary methods tested.
AB - We present an approach for the detection of sharp change points (short-lived and persistent) in nonlinear and nonstationary dynamic systems under high levels of noise by tracking the local phase and amplitude synchronization among the components of a univariate time series signal. The signal components are derived via Intrinsic Time scale Decomposition (ITD)-a nonlinear, non-parametric analysis method. We show that the signatures of sharp change points are retained across multiple ITD components with a significantly higher probability as compared to random signal fluctuations. Theoretical results are presented to show that combining the change point information retained across a specific set of ITD components offers the possibility of detecting sharp transitions with high specificity and sensitivity. Subsequently, we introduce a concept of mutual agreement to identify the set of ITD components that are most likely to capture the information about dynamical changes of interest and define an InSync statistic to capture this local information. Extensive numerical, as well as real-world case studies involving benchmark neurophysiological processes and industrial machine sensor data, suggest that the present method can detect sharp change points, on an average 62% earlier (in terms of average run length) as compared to other contemporary methods tested.
KW - Change detection
KW - nonlinear and nonstationary systems
KW - phase synchronization
KW - signal decomposition
KW - time series
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U2 - 10.1109/TSP.2020.3014423
DO - 10.1109/TSP.2020.3014423
M3 - Article
AN - SCOPUS:85091168223
VL - 68
SP - 4743
EP - 4756
JO - IRE Transactions on Audio
JF - IRE Transactions on Audio
SN - 1053-587X
M1 - 9162536
ER -