Change Detection in Complex Dynamical Systems Using Intrinsic Phase and Amplitude Synchronization

  title={Change Detection in Complex Dynamical Systems Using Intrinsic Phase and Amplitude Synchronization},
  author={Ashif Sikandar Iquebal and Satish T. S. Bukkapatnam and Arun R. Srinivasa},
  journal={IEEE Transactions on Signal Processing},
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… 
General Cauchy Conjugate Gradient Algorithms Based on Multiple Random Fourier Features
Monte Carlo simulations on the prediction of synthetic and real-world time-series and the identification of nonlinear system confirm the superiorities of the proposed MRFGCG algorithms in fixed networks, which have lower computational complexity and higher filtering accuracy than sparsification KAFs in non-Gaussian environments.
Tensor-Based ECG Anomaly Detection toward Cardiac Monitoring in the Internet of Health Things
A new sensor-based unsupervised framework that achieves superior performances in detecting abnormal ECG patterns induced by various types of cardiac disease and has a great potential to be implemented in IoHT-enabled cardiac monitoring and smart management of cardiac health.
Acoustic Emission Characterization of Natural Fiber Reinforced Plastic Composite Machining Using a Random Forest Machine Learning Model
Natural fiber reinforced plastic (NFRP) composites are eliciting an increased interest across industrial sectors, as they combine a high degree of biodegradability and recyclability with unique


Sequential Change-Point Detection Methods for Nonstationary Time Series
Two new spectral-based methods for detection of changes in autocorrelation structure in a continuous-valued time series in an online process monitoring setting are presented and it is found that they can provide reliable and timely detection ofChanges in covariance structure in anOnline monitoring framework.
A Dirichlet Process Gaussian State Machine Model for Change Detection in Transient Processes
Experimental investigations suggest that the DPGSM approach can consistently detect incipient, critical changes in intermittent signals some 50–2000 ms ahead of competing methods in benchmark test cases as well as a variety of real-world applications, such as in alternation patterns in a music piece and in the vibration signals capturing the initiation of product defects in an ultraprecision manufacturing process.
Time series forecasting for nonlinear and non-stationary processes: a review and comparative study
This article presents a review of advancements in nonlinear and non-stationary time series forecasting models and a comparison of their performances in certain real-world manufacturing and health informatics applications.
Intrinsic time-scale decomposition: time–frequency–energy analysis and real-time filtering of non-stationary signals
  • M. Frei, I. Osorio
  • Engineering
    Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
  • 2006
We introduce a new algorithm, the intrinsic time-scale decomposition (ITD), for efficient and precise time–frequency–energy (TFE) analysis of signals. The ITD method overcomes many of the limitations
Defining a trend for time series using the intrinsic time-scale decomposition
In this study, the first steps towards a probabilistic model of the ITD analysis of random time series are taken, concerning the universality and scaling properties of the components of the decomposition.
On the structures and quantification of recurrence plots
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
  • N. Huang, Zheng Shen, Henry H. Liu
  • Mathematics
    Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences
  • 1998
A new method for analysing nonlinear and non-stationary data has been developed. The key part of the method is the ‘empirical mode decomposition’ method with which any complicated data set can be
Multiscale monitoring of autocorrelated processes using wavelets analysis
This article proposes a new method to develop multiscale monitoring control charts for an autocorrelated process that has an underlying unknown ARMA(2, 1) model structure. The Haar wavelet transform
Detection of abrupt changes: theory and application
A unified framework for the design and the performance analysis of the algorithms for solving change detection problems and links with the analytical redundancy approach to fault detection in linear systems are established.