03/17/2021
By Bhavana Maradani

Ph.D. Candidate: Bhavana Maradani
Time: Monday, March 29, 2021, 7 a.m.
Location: This will be a virtual defense via Zoom. Those interested in attending please contact Committee Advisor haim_levkowitz@uml.edu and bhavana_maradani@student.uml.edu at least 24 hours prior to the defense to request access to the meeting.

Committee Members:

  • Haim Levkowitz (advisor), Associate Professor, Computer Science Department, University of Massachusetts Lowell
  • Jonatha Mwaura, Assistant Teaching Professor, Computer Science Department, University of Massachusetts Lowell
  • Sashikala, Professor, Department of Operations and IT, ICFAI Business School, Hyderabad

Abstract:

One of the biggest challenges faced by different industries today is the rapid accumulation of data and the laborious task of sifting through, analyzing, documenting, and evaluating all the available data gleaned from various operations. It is very critical to manage and understand this vast body of data to tease actionable insights in the shortest possible time and with less human intervention. This dissertation aims at addressing the challenges of providing solutions with less expertise intervention and early detection of life threatening instances.

In this dissertation, we focus on the following two problems: 

  1. Analyzing the performance of a subject while performing physical activity during Tele-Rehabilitation;
  2. Estimating the health status and remaining useful “life” (RUL) of an aircraft engine.

Telerehabilitation has evolved as an alternative approach to face-to-face rehabilitation to facilitate rehabilitation for patients remotely; eliminating the time and distance barrier, thereby, reducing the cost and, during these times, minimizing unnecessary physical proximity. Continuous data monitoring enables caregivers to provide proper care/intervention and evaluate a subject. However, the data collected from the body sensor network needs to be presented in an understandable format to aid the caregiver to understand the movement of the subject. Most of the current research has developed models to estimate joint angles. However, research on visualizing the movement data has been limited and little has been done concerning the long-term performance of a subject. To fill this gap we propose an interactive visualization to evaluate subjects’ upper limb movements using data acquired while performing various rehabilitation exercises over a period of time. The main contribution of this task presents interactive visualizations to demonstrate the amount of deviations from the normal range of motion and the time taken to finish a task.

For the second task, we have considered the problem of detecting the health status and estimating the remaining useful life of an aircraft engine (RUL). Considering the high cost associated with engine failure, monitoring the degradation trend of an engine from operational and historical maintenance data can reduce operational expenses and, more important, the likelihood of — possibly catastrophic — failure during use, thus, potentially saving lives. To address the issue of detecting the health status of an engine, we propose an ensemble model to determine the state of an engine. The main contribution of this work is the automatic detection of the optimal number of health states and the development of a classifier to perform health diagnosis of an engine.

Future work: We will be developing a model which determines the health Index of a machine and further map the index to RUL.