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Proceedings Paper

A machine learning framework to understand multiphase flow using acoustic signals (Conference Presentation)
Author(s): Maruti K. Mudunuru; Vamshi Krishna Chillara; Satish Karra; Dipen Sinha

Paper Abstract

Time-series signals are central to understand and identify the state of a dynamical system. They are ubiquitous in many areas related to geosciences, climate, and structural health monitoring. As a result, the theory and techniques for analyzing and modeling time-series have vast applications in many different scientific disciplines. One of the key challenges that the time-series data analysts face is that of information/data overload. Furthermore, the sheer volume of the time-series data generated at the sensor node makes it difficult to transport the data to centralized databases. These aspects pose an obstacle for data analysts in detecting changes in the system response as early as possible. Instead, a workflow for an efficient and automatic reduction of collected data at sensor nodes can enable timely analyses and decrease event detection latency. Such a workflow can be useful for many real-time monitoring and sensing applications. An attractive way to construct a computationally efficient workflow for automated analysis of time-series data is through machine learning. In this paper, we present a machine learning framework to construct models to efficiently reduce the time-series data by means of feature extraction and feature selection. In the first step of the framework, we apply a feature extraction and feature filtering algorithm called “Feature Extraction based on Scalable Hypothesis (FRESH)” for a given time-series data to extract comprehensive time-series signal features and then filter the resulting features. In second step, we quantify the significance of each filtered feature for predicting a set of labels/targets. Third, we construct a machine learning classifier, which takes in important filtered features to classify the time-series signals. The proposed framework is tested and validated against ultrasonic sensing datasets obtained from multiphase flow loop experiments.

Paper Details

Date Published: 14 May 2018
Proc. SPIE 10652, Disruptive Technologies in Information Sciences, 1065213 (14 May 2018); doi: 10.1117/12.2304536
Show Author Affiliations
Maruti K. Mudunuru, Los Alamos National Lab. (United States)
Vamshi Krishna Chillara, Los Alamos National Lab. (United States)
Satish Karra, Los Alamos National Lab. (United States)
Dipen Sinha, Los Alamos National Lab. (United States)

Published in SPIE Proceedings Vol. 10652:
Disruptive Technologies in Information Sciences
Misty Blowers; Russell D. Hall; Venkateswara R. Dasari, Editor(s)

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