東華大學圖書館 |

Intelligent Maintenance and Monitoring Strategy for Smart Manufacturing Systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Intelligent Maintenance and Monitoring Strategy for Smart Manufacturing Systems./
作者:	Ye, Honghan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	188 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:	Dissertations Abstracts International83-03B.
標題:	Industrial engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28717440
ISBN:	9798538113873

Intelligent Maintenance and Monitoring Strategy for Smart Manufacturing Systems.
Ye, Honghan.

Intelligent Maintenance and Monitoring Strategy for Smart Manufacturing Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 188 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2021.

This item must not be sold to any third party vendors.

In smart manufacturing systems, production scheduling, maintenance decision-making, and process monitoring are three key, closely interconnected components, which play significant roles in the system performance, quality control, and overall cost. Due to the rapid advancement of in-process measurements and sensor technology, massive data frequently appear in modern industries. While such a data-rich environment has the potential to better reveal real-time details of the underlying system and make better decisions for the system improvement, it also presents significant challenges in the following perspectives: (i) how to effectively leverage the acquired knowledge to balance trade-offs between conflicting objectives, (ii) how to optimally design the monitoring system given the practical resources constraint, and (iii) how to efficiently handle the high-dimensional heterogeneous information with different acquisition rates, distributions, and characteristics.This thesis concentrates on production and maintenance scheduling, and process monitoring to develop systematic analytics methodologies for quality control, cost reduction, and performance improvement in smart manufacturing systems. By incorporating engineering domain knowledge with advanced statistical techniques, the proposed methodologies facilitate (i) the real-time decisions that improve the system production and maintenance scheduling, (ii) the effective monitoring of system status, (iii) the informative and intelligent decisions on balancing between exploration and exploitation given the limited monitoring resources, and (iv) the asynchronous process monitoring with different data acquisition rates.The first chapter introduces the background and challenges in production and maintenance scheduling, and monitoring in smart manufacturing systems, and establishes the major research objective of the thesis. Chapter 2 addresses a joint scheduling problem that considers corrective maintenance (CM) due to unexpected breakdowns and scheduled preventive maintenance (PM) in a generic M-machine flow shop. The objective is to find the optimal job sequence and PM schedule such that the total of tardiness cost, PM cost, and CM cost is minimized. To address this critical research issue, our novel idea is to dynamically update the PM interval based on real-time machine age, such that maintenance activity coordinates with job scheduling to the maximum extent, which results in an overall cost saving. With the rapid development of sensor technology, real-time observations from the sensors can be used to describe the machine status more accurately and achieve early anomaly detection. In Chapter 3, we propose a nonparametric monitoring and sampling algorithm integrated with Thompson sampling to quickly detect abnormalities occurring in heterogeneous data streams. In particular, a Bayesian approach is incorporated with an antirank-based cumulative sum (CUSUM) procedure to collectively estimate the underlying status of all data streams based on the partially observed data. Furthermore, an intelligent sampling strategy based on Thompson sampling (TS) algorithm is proposed to dynamically observe the informative data streams and balance between exploration and exploitation to facilitate quick anomaly detection. While the proposed method in Chapter 3 shows good performance in monitoring heterogeneous data streams, it heavily relies on the assumption that full historical in-control observations of all data streams are available offline, which does not always hold in practice. To address this issue, Chapter 4 further proposes a generic online nonparametric monitoring and sampling scheme occurring in high-dimensional heterogeneous processes when only partial observations are available. Specifically, we integrate the TS algorithm with a quantile-based nonparametric CUSUM procedure to construct local statistics of all data streams based on the partially observed data. Further, we develop a global monitoring scheme by using the sum of top-r local statistics to screen out the most suspicious data streams. Chapter 5 proposes a generic top-r based asynchronous monitoring (TRAM) framework to online monitor high-dimensional heterogeneous and asynchronous processes, where measurements of each data stream follow arbitrary distributions and are collected at different sampling intervals. In particular, we first adopt a quantile-based nonparametric CUSUM scheme to monitor each data stream locally. Then, an effective compensation strategy is proposed for unsampled data streams at the local statistics level to alleviate severe detection delay when mean shifts occur to long-sampling-interval data streams. Furthermore, we develop a global monitoring scheme using the sum of top-r local statistics, which is able to quickly detect a wide range of possible mean shifts in all directions. Chapter 6 then summarizes the contribution of the thesis.In summary, this thesis contributes to developing systematic analytics methodologies for quality control, cost reduction, and performance improvement in smart manufacturing systems. The developed methods are generic and can also be applied to other applications such as healthcare, energy and climate research, which will lead to improved maintenance scheduling, efficient resource allocation, and significant overall cost savings.

ISBN: 9798538113873Subjects--Topical Terms:

526216
Industrial engineering.
Subjects--Index Terms:

Adaptive maintenance

Intelligent Maintenance and Monitoring Strategy for Smart Manufacturing Systems.
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245 1 0 $a Intelligent Maintenance and Monitoring Strategy for Smart Manufacturing Systems.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 188 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
500 $a Advisor: Liu, Kaibo.
502 $a Thesis (Ph.D.)--The University of Wisconsin - Madison, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a In smart manufacturing systems, production scheduling, maintenance decision-making, and process monitoring are three key, closely interconnected components, which play significant roles in the system performance, quality control, and overall cost. Due to the rapid advancement of in-process measurements and sensor technology, massive data frequently appear in modern industries. While such a data-rich environment has the potential to better reveal real-time details of the underlying system and make better decisions for the system improvement, it also presents significant challenges in the following perspectives: (i) how to effectively leverage the acquired knowledge to balance trade-offs between conflicting objectives, (ii) how to optimally design the monitoring system given the practical resources constraint, and (iii) how to efficiently handle the high-dimensional heterogeneous information with different acquisition rates, distributions, and characteristics.This thesis concentrates on production and maintenance scheduling, and process monitoring to develop systematic analytics methodologies for quality control, cost reduction, and performance improvement in smart manufacturing systems. By incorporating engineering domain knowledge with advanced statistical techniques, the proposed methodologies facilitate (i) the real-time decisions that improve the system production and maintenance scheduling, (ii) the effective monitoring of system status, (iii) the informative and intelligent decisions on balancing between exploration and exploitation given the limited monitoring resources, and (iv) the asynchronous process monitoring with different data acquisition rates.The first chapter introduces the background and challenges in production and maintenance scheduling, and monitoring in smart manufacturing systems, and establishes the major research objective of the thesis. Chapter 2 addresses a joint scheduling problem that considers corrective maintenance (CM) due to unexpected breakdowns and scheduled preventive maintenance (PM) in a generic M-machine flow shop. The objective is to find the optimal job sequence and PM schedule such that the total of tardiness cost, PM cost, and CM cost is minimized. To address this critical research issue, our novel idea is to dynamically update the PM interval based on real-time machine age, such that maintenance activity coordinates with job scheduling to the maximum extent, which results in an overall cost saving. With the rapid development of sensor technology, real-time observations from the sensors can be used to describe the machine status more accurately and achieve early anomaly detection. In Chapter 3, we propose a nonparametric monitoring and sampling algorithm integrated with Thompson sampling to quickly detect abnormalities occurring in heterogeneous data streams. In particular, a Bayesian approach is incorporated with an antirank-based cumulative sum (CUSUM) procedure to collectively estimate the underlying status of all data streams based on the partially observed data. Furthermore, an intelligent sampling strategy based on Thompson sampling (TS) algorithm is proposed to dynamically observe the informative data streams and balance between exploration and exploitation to facilitate quick anomaly detection. While the proposed method in Chapter 3 shows good performance in monitoring heterogeneous data streams, it heavily relies on the assumption that full historical in-control observations of all data streams are available offline, which does not always hold in practice. To address this issue, Chapter 4 further proposes a generic online nonparametric monitoring and sampling scheme occurring in high-dimensional heterogeneous processes when only partial observations are available. Specifically, we integrate the TS algorithm with a quantile-based nonparametric CUSUM procedure to construct local statistics of all data streams based on the partially observed data. Further, we develop a global monitoring scheme by using the sum of top-r local statistics to screen out the most suspicious data streams. Chapter 5 proposes a generic top-r based asynchronous monitoring (TRAM) framework to online monitor high-dimensional heterogeneous and asynchronous processes, where measurements of each data stream follow arbitrary distributions and are collected at different sampling intervals. In particular, we first adopt a quantile-based nonparametric CUSUM scheme to monitor each data stream locally. Then, an effective compensation strategy is proposed for unsampled data streams at the local statistics level to alleviate severe detection delay when mean shifts occur to long-sampling-interval data streams. Furthermore, we develop a global monitoring scheme using the sum of top-r local statistics, which is able to quickly detect a wide range of possible mean shifts in all directions. Chapter 6 then summarizes the contribution of the thesis.In summary, this thesis contributes to developing systematic analytics methodologies for quality control, cost reduction, and performance improvement in smart manufacturing systems. The developed methods are generic and can also be applied to other applications such as healthcare, energy and climate research, which will lead to improved maintenance scheduling, efficient resource allocation, and significant overall cost savings.
590 $a School code: 0262.
650 4 $a Industrial engineering. $3 526216
650 4 $a Systems science. $3 3168411
650 4 $a Manufacturing. $3 3389707
650 4 $a Monitoring systems. $3 3543995
650 4 $a Performance evaluation. $3 3562292
650 4 $a Schedules. $3 3564128
650 4 $a Scheduling. $3 750729
650 4 $a Simulation. $3 644748
650 4 $a Decision making. $3 517204
650 4 $a Algorithms. $3 536374
650 4 $a Statistical methods. $3 731085
653 $a Adaptive maintenance
653 $a Big data analytics
653 $a High-dimensional monitoring
653 $a Intelligent sampling
690 $a 0546
690 $a 0790
710 2 $a The University of Wisconsin - Madison. $b Industrial Engineering. $3 2093809
773 0 $t Dissertations Abstracts International $g 83-03B.
790 $a 0262
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28717440