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INTELLIGENT CONTROL AND AUTOMATIC DETECTION/PREDICTION IN SENSOR BASED SYSTEMS
by
Shuping Liu
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(ELECTRICAL ENGINEERING)
December 2011
Copyright 2011 Shuping Liu
Object Description
| Title | Intelligent control and automatic detection/prediction in sensor based systems |
| Author | Liu, Shuping |
| Author email | lius@usc.edu;bensile@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Electrical Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2011-09-30 |
| Date submitted | 2011-10-04 |
| Date approved | 2011-10-04 |
| Restricted until | 2011-10-04 |
| Date published | 2011-10-04 |
| Advisor (committee chair) | Raghavendra, Cauligi S. |
| Advisor (committee member) |
Silvester, John A. Ershaghi, Iraj Talukder, Ashit Panangadan, Anand |
| Abstract | Sensors are increasingly used for collecting data from the field for monitoring and detecting anomalous behavior. In this thesis a network of sensors are used for data collection, analysis, and detecting abnormal situations in two domains of patient health monitoring and failures in rod pump systems in an oilfield. In these application domains, there are two challenging problems: intelligent control for wireless sensor operations to make decisions on sampling to optimize life time of a sensor network and accurate anomaly detection and prediction using the collected data. ❧ For the health monitoring application domain, a new policy-based framework of Markov Decision Processes (MDP) is formulated for energy efficient optimization problem. The optimal global policy obtained from MDP formulation can be used by distributed sensors to achieve adaptive sampling for optimal and intelligent control of both energy consumption (system lifetime) and detection accuracy. The size of MDP policy may be large with increasing number of sensors having limited memory and discretization granularity of the problem. A decision tree-based learning algorithm is applied for a compact policy representation. Computational complexity is also exponential to the number of sensors and proportional to the discretization granularity of the problem, which causes the computational scalability problem and limits the application of MDP framework on large state space cases. In order to overcome computational scalability problem, three computationally efficient learning algorithms are developed based on approaches to learn local policies for each sensor: RLAA Learning Algorithm, AMRL Learning Algorithm and COL Learning Algorithm. We successfully applied our approaches to healthcare monitoring system, and compared the performance with other methods. The results show that all three learning algorithms are scalable to sensor networks with large state space. ❧ For the oil field domain, learning-based automatic anomaly detection and prediction algorithms are developed for artificial lift rod pump systems which fail due to various reasons and fixing them can be costly and difficult because most parts are underground. Currently, failures in such systems are detected by field experts, which take time and incur labor costs. Our approach is supervised learning-based anomaly detection techniques from field data and we developed a novel combination of two supervised learning algorithms, AdaBNet and AdaDT for this problem. These techniques are successfully applied to detecting and predicting failures in rod pump systems with real data from oilfields. Our automated anomaly detection and prediction approach can allow automated surveillance of large number of wells in an oil field to reduce cost while monitoring wells remotely. |
| Keyword | intelligent control; energy efficiency; accuracy; automatic; detection and prediction; sensor based systems; machine learning; data mining |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m |
| Contributing entity | University of Southern California |
| Rights | Liu, Shuping |
| Physical access | The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given. |
| Repository name | University of Southern California Digital Library |
| Repository address | USC Digital Library, University of Southern California, University Park Campus MC 7002, 106 University Village, Los Angeles, California 90089-7002, USA |
| Repository email | cisadmin@lib.usc.edu |
| Archival file | uscthesesreloadpub_Volume6/etd-LiuShuping-316.pdf |
Description
| Title | Page 1 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | INTELLIGENT CONTROL AND AUTOMATIC DETECTION/PREDICTION IN SENSOR BASED SYSTEMS by Shuping Liu A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (ELECTRICAL ENGINEERING) December 2011 Copyright 2011 Shuping Liu |
