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Artificial Intelligence to Enable Smart Prognostics and Health Management of Manufacturing Systems for Industry 4.0.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Artificial Intelligence to Enable Smart Prognostics and Health Management of Manufacturing Systems for Industry 4.0./
Author:	Sundaram, Sarvesh.
Description:	1 online resource (173 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-02, Section: B.
Contained By:	Dissertations Abstracts International85-02B.
Subject:	Industrial engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798380157124

Artificial Intelligence to Enable Smart Prognostics and Health Management of Manufacturing Systems for Industry 4.0.
Sundaram, Sarvesh.

Artificial Intelligence to Enable Smart Prognostics and Health Management of Manufacturing Systems for Industry 4.0. - 1 online resource (173 pages)

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

Thesis (Ph.D.)--Northeastern University, 2023.

Includes bibliographical references

Modern manufacturing systems are complex and heterogeneous distributed information systems that consist of manufacturing hardware and networking software. Unlike traditional manufacturing environments, modern ones generate large amounts of data in real-time that take the form of sensor measurements from the shopfloor, images from the product inspection, technical text from maintenance records, and many more. The issue of collecting, processing, and analyzing this data is at the core of the Industry 4.0 paradigm's success. What is more important, challenging, and rewarding is that such valuable real-time streaming data can be used for diagnostics, prognostics, and health monitoring of systems and products. This dissertation explores the area of health monitoring of modern manufacturing environments to support the state-of-the-art Industry 4.0 paradigms. We deploy Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) based approaches to assess the performance of systems and products by identifying anomalies and defects, estimating the Remaining Useful Life (RUL), and determining what sort of corrective maintenance actions may be necessary.In the context of the foregoing, advanced manufacturing environments are very intricate and have a vast array of complex problems that can be researched. This dissertation focuses on four important questions: (1) what are the requirements to achieve interoperability in smart manufacturing? (2) how can the next hardware failure, be it of a machine tool or a cutting tool, be predicted before it occurs? (3) how can we identify the products that fail inspection, and how can we improve the overall quality inspection process by considering the factors affecting it? and (4) how can we leverage the resource-rich maintenance logs from manufacturing systems to find the most frequently occurring problems, and more importantly, how can we predict corrective actions for future maintenance problems?This dissertation addresses the above problems by making the following research contributions. In the first work, we discuss interoperability in manufacturing by reviewing semantic, syntactic, factory, and cloud-based interoperability. We then identify key research challenges that impact the implementation of interoperable manufacturing, and review frameworks for modern manufacturing such as Reference Architecture Model for Industry 4.0 (RAMI 4.0), Industrial Internet Reference Architecture (IIRA), and several others. The second part of the research proposes a framework for Smart Prognostics and Health Management (SPHM) to support manufacturing system health. We apply it to a milling machine operation and assess the condition of the milling machine's tool tip by identifying whether it is safe or degraded and estimate its RUL using ML models. To address the third question, we introduce a methodology for Smart Quality Inspection (SQI) of manufacturing products by using a custom Convolutional Neural Network (CNN) architecture. A shopfloor computer application is developed that helps minimize the effects of the factors affecting the visual inspection process. The fourth component of this dissertation introduces a Technical Language Processing (TLP) framework to deal with the unstructured and complex nature of technical text data from maintenance work orders. We demonstrate its capabilities by applying it to maintenance logs of aircraft to predict corrective actions, and link maintenance problems to standardized maintenance codes as prescribed by the industry.The rapid growth of Artificial Intelligence (AI) suggests that its potential to transform health monitoring capabilities is going to improve in the coming years. Future research in health monitoring of manufacturing systems and products can be aimed towards the following areas: development of standardized communication protocols for interoperability, further automation of the inspection process by integrating localized fault detection, and incorporation of TLP with predictive maintenance approaches and quality control to form a prescriptive approach, to suggest a few.

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

Mode of access: World Wide Web

ISBN: 9798380157124Subjects--Topical Terms:

679492
Industrial engineering.
Subjects--Index Terms:

Deep learningIndex Terms--Genre/Form:

554714
Electronic books.

Artificial Intelligence to Enable Smart Prognostics and Health Management of Manufacturing Systems for Industry 4.0.
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