Program: Multi-models to detect a change in a real-time series of complex systems
Ph.D. Dissertation by Abdulrahman Shamsan
Three models will be introduced in this poster to detect a small shift time series signal. The novelty in this work is the real-time monitoring which is an important tool for quality control process. The proposed approach has applications in various disciplines such as ultraprecision manufacturing, medical signal monitoring, etc. The poster includes three parts each one explains a model and a comparison between those models will be in the fourth part.
The first model is an Intrinsic multiplex graph model detects incipient process drift in ultraprecision manufacturing. While rare events like process drift and damage in ultraprecision manufacturing are not preventable in most cases, the accurate detection could enable timely corrective actions to substantially reduce the severity of the fallout and the associated treatment cost. Nonetheless, subtle process drift in incipient stage generally nullifies conventional data-driven detection algorithms, which are inadequate to track dynamic behavioral evolution for complex systems. Notably, the inevitable sensor failure in conjunction with heterogeneous sensing characteristics have made it strenuous to synchronize data from multiple sources to monitor process variability. To address those challenges, this paper presents a novel intrinsic multiplex graph approach based on intrinsic timescale decomposition of the signal collected from one single source for the detection of incipient process drift.
The second model is a Multimodal Data Fusion Using Multivariate Empirical Mode Decomposition for Automatic Process Monitoring. The recent leap forward in wireless sensing and communication technologies has brought forward the proliferation of multimodal sensing data and offered a unique viewpoint to study numerous complex systems and processes. Indeed, the mining of such sensing data is poised to promote the paradigm-shift from experience-based to evidence-driven process monitoring, control, prognostics, and optimization. Notably, an integration and effective fusion of such multimodal data has been an impending obstacle to utterly marshal their prowess. Despite the extensive attempt in the literature, multimodal data fusion is still in the embryonic stage, particularly considering the inherent nonlinear, nonstationary, and heterogeneous nature of the sensing data. In this article, we report a novel approach to fuse multimodal data from different channels via nonparametric multivariate empirical mode decomposition (EMD). The EMD decomposes each signal into a series of oscillatory modes called the intrinsic mode functions (IMFs) with a progressively lower frequency. The multivariate EMD guarantees that signals from different channels have the same number of components and are mode aligned. Capitalizing on the recurrence quantification analysis of the intrinsic state space reconstructed from the IMFs, we build a control chart to monitor process dynamics. Applications in ultraprecision machining processes suggest the effectiveness of the proposed algorithm.
The third model is an Intrinsic Recurrence Quantification Analysis of Nonlinear and Nonstationary Short-term Time Series. Recurrence analysis is an effective tool to delineate and quantify the dynamics of complex systems, e.g., laminar, divergent or nonlinear transient behaviors. Oftentimes, the effectiveness of recurrence quantification rests on the accurate reconstruction of state space from univariate time series. Few, if any, previous approaches can quantify the recurrence properties from a short-term univariate time series, which is oftentimes present in rare or extreme events. This paper presents novel intrinsic recurrence quantification analysis to quantify the recurrence behaviors in complex dynamic systems with short-term observations. As opposed to the traditional recurrence analysis, we delineate and quantify recurrence dynamics in intrinsic scales, which captures not only nonlinear but also nonstationary behaviors in short-term time series. The proposed intrinsic recurrence approach is utilized to identify subjects with early stage atrial fibrillation with short-term episode.
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