國立虎尾科技大學 |

AI-Enabled Modeling and Monitoring of Data-Rich Advanced Manufacturing Systems.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	AI-Enabled Modeling and Monitoring of Data-Rich Advanced Manufacturing Systems./
作者:	Mamun, Abdullah Al.
面頁冊數:	1 online resource (122 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-02, Section: B.
Contained By:	Dissertations Abstracts International85-02B.
標題:	Industrial engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9798380117821

AI-Enabled Modeling and Monitoring of Data-Rich Advanced Manufacturing Systems.
Mamun, Abdullah Al.

AI-Enabled Modeling and Monitoring of Data-Rich Advanced Manufacturing Systems. - 1 online resource (122 pages)

Source: Dissertations Abstracts International, Volume: 85-02, Section: B.

Thesis (Ph.D.)--Mississippi State University, 2023.

Includes bibliographical references

The infrastructure of cyber-physical systems (CPS) is based on a meta-concept of cybermanufacturing systems (CMS) that synchronizes the Industrial Internet of Things (IIoTs), Cloud Computing, Industrial Control Systems (ICSs), and Big Data analytics in manufacturing operations. Artificial Intelligence (AI) can be incorporated to make intelligent decisions in the day-to-day operations of CMS. Cyberattack spaces in AI-based cybermanufacturing operations pose significant challenges, including unauthorized modification of systems, loss of historical data, destructive malware, software malfunctioning, etc. However, a cybersecurity framework can be implemented to prevent unauthorized access, theft, damage, or other harmful attacks on electronic equipment, networks, and sensitive data. The five main cybersecurity framework steps are divided into procedures and countermeasure efforts, including identifying, protecting, detecting, responding, and recovering. Given the major challenges in AI-enabled cybermanufacturing systems, three research objectives are proposed in this dissertation by incorporating cybersecurity frameworks. The first research aims to detect the in-situ additive manufacturing (AM) process authentication problem using high-volume video streaming data. A side-channel monitoring approach based on an in-situ optical imaging system is established, and a tensor-based layer-wise texture descriptor is constructed to describe the observed printing path. Subsequently, multilinear principal component analysis (MPCA) is leveraged to reduce the dimension of the tensor-based texture descriptor, and low-dimensional features can be extracted for detecting attack-induced alterations. The second research work seeks to address the high-volume data stream problems in multi-channel sensor fusion for diverse bearing fault diagnosis. This second approach proposes a new multi-channel sensor fusion method by integrating acoustics and vibration signals with different sampling rates and limited training data. The frequency-domain tensor is decomposed by MPCA, resulting in low-dimensional process features for diverse bearing fault diagnosis by incorporating a Neural Network classifier. By linking the second proposed method, the third research endeavor is aligned to recovery systems of multi-channel sensing signals when a substantial amount of missing data exists due to sensor malfunction or transmission issues. This study has leveraged a fully Bayesian CANDECOMP/PARAFAC (FBCP) factorization method that enables to capture of multi-linear interaction (channels x signals) among latent factors of sensor signals and imputes missing entries based on observed signals.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380117821Subjects--Topical Terms:

679492
Industrial engineering.
Subjects--Index Terms:

Cyber-physical systemIndex Terms--Genre/Form:

554714
Electronic books.

AI-Enabled Modeling and Monitoring of Data-Rich Advanced Manufacturing Systems.
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