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001 AAI28028871
005 20201109125230.5
006 m o d
007 cr mn ---uuuuu
008 201109s2020 miu sbm 000 0 eng d
020 9798662568846
035 (MiAaPQ)AAI28028871
040 MiAaPQ|beng|cMiAaPQ|dNTU
100 1 Song, Changyue
245 10 Internet of Things-Enabled Degradation Modeling, Inference,
and Prognosis
264 0 |c2020
300 1 online resource (171 pages)
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
500 Source: Dissertations Abstracts International, Volume: 82-
02, Section: B
500 Advisor: Liu, Kaibo
502 Thesis (Ph.D.)--The University of Wisconsin - Madison,
2020
504 Includes bibliographical references
520 Degradation is common in a variety of engineering systems,
which can lead to system failures. Enabled by the Internet
of Things technology, sensors have been widely used to
monitor the degradation process of engineering systems. By
analyzing the collected sensor signals, the failure time
of an engineering system can be predicted, and appropriate
maintenance can be scheduled to avoid unexpected failures.
This brings an unprecedented opportunity for developing
advanced methodologies that enable and assist (i) the
efficient handling of the rich and diverse sensor
measurements, (ii) the estimation and inference of the
unobserved degradation status, and (iii) the exploitation
of the acquired knowledge for more enhanced prognosis of
the future dynamics and decision-making for predictive
maintenance. This thesis focuses on Internet of Things-
enabled degradation modeling, inference, and prognosis to
develop data analytics methodologies by effectively
combining advanced statistics, machine learning and
engineering domain knowledge. The proposed methodologies
enable (i) the proper and robust modeling of the
degradation process and the inter-relations of the sensors,
(ii) an accurate estimation of the unknown degradation
status, (iii) an accurate prediction of the future
behaviors and failure time, and (iv) the enactment of
decisions for predictive maintenance. The first chapter
introduces the background and elaborates the challenges in
degradation modeling, inference, and prediction enabled by
Internet of Things. The objective of this thesis is also
highlighted. Chapter 2 focuses on health index methods for
degradation modeling and prognostics with multiple sensor
signals. While a health index is constructed by combining
the multiple sensor signals to characterize the
degradation process, existing health index methods are
limited to linear fusion function. In this chapter, we
propose a novel health index method that extends the
linear fusion function to nonlinear functions by
incorporating the kernel methods. Chapter 3 focuses on a
more fundamental issue regarding the theoretical
justification of the health index methods. Existing health
index methods are heuristic, and the prognostic
performance of the constructed health index cannot be
guaranteed. To address this issue, we propose to use
indirect supervised learning, where the failure time
information is used as an indirect indicator of the
underlying degradation status to guide the construction of
the health index. In this way, the constructed health
index is theoretically guaranteed to characterize the true
degradation process. Chapter 4 further proposes a generic
framework for multisensor degradation modeling, where a
novel concept called failure surface is proposed to define
system failure based on multiple sensor signals, and a new
method is proposed to estimate the failure surface by
transforming the degradation modeling problem into a
classification problem. As a result, the proposed method
is flexible to explore complicated relations of sensor
signals, is capable of handling asynchronous signals, and
can automatically screen out non-informative sensors.
Chapter 5 proposes a systematic method for degradation
modeling and prognosis that can be widely used in
different scenarios. After extracting features for each
sensor signal, local linear models are adopted to
establish the relation between the extracted features and
failure time. A goodness-of-fit measure is further
proposed to assess the adequacy of the local linear model.
If a unit is monitored by multiple sensors, decision-level
fusion and feature-level fusion are further used to fuse
the information from the sensors. Chapter 6 then
summarizes the contributions of the thesis. In summary,
this thesis contributes to the Internet of Things-enabled
degradation modeling, inference, and prognosis by
developing systematic data-driven analytics methodologies.
The research possesses a great potential for applications
in manufacturing, health care, and energy facilities, etc.,
where Internet of Things technology has been rapidly
adopted
533 Electronic reproduction.|bAnn Arbor, Mich. :|cProQuest,
|d2020
538 Mode of access: World Wide Web
650 4 Industrial engineering
650 4 Artificial intelligence
653 Internet of Things-enabled
653 Degradation modeling
653 Inference
653 Prognosis
655 7 Electronic books.|2local
690 0546
690 0800
710 2 ProQuest Information and Learning Co
710 2 The University of Wisconsin - Madison.|bIndustrial
Engineering
773 0 |tDissertations Abstracts International|g82-02B
856 40 |uhttp://pqdd.sinica.edu.tw/twdaoapp/servlet/
advanced?query=28028871|zclick for full text (PQDT)
912 PQDT
