scholarly journals Smart Manufacturing PHM

2020 ◽  
Vol 7 (3) ◽  
Author(s):  
Brian A. Weiss ◽  
Philip Freeman ◽  
Jay Lee ◽  
Radu Pavel
Keyword(s):  
Health Monitoring ◽  
Machine Tools ◽  
Manufacturing System ◽  
New Technologies ◽  
Health Management ◽  
Smart Manufacturing ◽  
Prognostics And Health Management ◽  
Monitoring And Control ◽  
Supply Chain Disruptions ◽  
And Control

The age of Smart Manufacturing has arrived where more and more organizations are embracing it to innovate and maintain their competitiveness. Smart Manufacturing blends information technology (IT) with operations technology (OT) to enable greater productivity, efficiency, quality, and customization within factory operations. More specifically, emerging and existing factory-floor level technologies (including robotics, machine tools, additive processes, automation, and sensors) are being fused with networking (both wired and wireless) and analysis technologies to generate more timely, accurate, and appropriate communication. This communication directly enables more intelligent sensing, monitoring, and control of the overall manufacturing system, including its constituent processes and sub-systems. Organizations that are adopting a smart manufacturing approach have become more flexible and adaptive to address changing customer demands, integrate new technologies, mitigate supply chain disruptions, and better utilize their human workforce. Prognostics and Health Management (PHM), in the context of Smart Manufacturing, focuses on the technologies and capabilities that enable health monitoring, diagnostics, and prognostics to promote greater intelligence in maintenance and control activities.

scholarly journals Adaptive Multi-scale Prognostics and Health Management for Smart Manufacturing Systems

2020 ◽  
Vol 7 (3) ◽  
Author(s):  
Benjamin Y. Choo ◽  
Stephen C. Adams ◽  
Brian A. Weiss ◽  
Jeremy A. Marvel ◽  
Peter A. Beling
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing System ◽  
Health Management ◽  
Smart Manufacturing ◽  
Health State ◽  
Prognostics And Health Management ◽  
Multi Scale ◽  
Decision Points ◽  
The Hierarchical Structure ◽  
Smart Manufacturing Systems

The Adaptive Multi-scale Prognostics and Health Management (AM-PHM) is a methodology designed to enable PHM in smart manufacturing systems. In application, PHM information is not yet fully utilized in higher-level decisionmaking in manufacturing systems. AM-PHM leverages and integrates lower-level PHM information such as from a machine or component with hierarchical relationships across the component, machine, work cell and assembly line levels in a manufacturing system. The AM-PHM methodology enables the creation of actionable prognostic and diagnostic intelligence up and down the manufacturing process hierarchy. Decisions are then made with the knowledge of the current and projected health state of the system at decision points along the nodes of the hierarchical structure. To overcome the issue of exponential explosion of complexity associated with describing a large manufacturing system, the AM-PHM methodology takes a hierarchical Markov Decision Process (MDP) approach into describing the system and solving for an optimized policy. A description of the AM-PHM methodology is followed by a simulated industry-inspired example to demonstrate the effectiveness of AM-PHM.

Hierarchical Decomposition of a Manufacturing Work Cell to Promote Monitoring, Diagnostics, and Prognostics

2017 ◽  
Author(s):  
Brian A. Weiss ◽  
Guixiu Qiao
Keyword(s):  
Machine Tools ◽  
Health Management ◽  
Control Strategies ◽  
Industrial Robot ◽  
Work Cell ◽  
Critical Elements ◽  
Functional Relationships ◽  
Manufacturing Operations ◽  
Robot Systems ◽  
And Control

Manufacturing work cell operations are typically complex, especially when considering machine tools or industrial robot systems. The execution of these manufacturing operations require the integration of layers of hardware and software. The integration of monitoring, diagnostic, and prognostic technologies (collectively known as prognostics and health management (PHM)) can aid manufacturers in maintaining the performance of machine tools and robot systems by providing intelligence to enhance maintenance and control strategies. PHM can improve asset availability, product quality, and overall productivity. It is unlikely that a manufacturer has the capability to implement PHM in every element of their system. This limitation makes it imperative that the manufacturer understand the complexity of their system. For example, a typical robot systems include a robot, end-effector(s), and any equipment, devices, or sensors required for the robot to perform its task. Each of these elements is bound, both physically and functionally, to one another and thereby holds a measure of influence. This paper focuses on research to decompose a work cell into a hierarchical structure to understand the physical and functional relationships among the system’s critical elements. These relationships will be leveraged to identify areas of risk, which would drive a manufacturer to implement PHM within specific areas.

scholarly journals Towards smart biomanufacturing: a perspective on recent developments in industrial measurement and monitoring technologies for bio-based production processes

2020 ◽  
Vol 47 (11) ◽  
pp. 947-964 ◽  
Author(s):  
Carina L. Gargalo ◽  
Isuru Udugama ◽  
Katrin Pontius ◽  
Pau C. Lopez ◽  
Rasmus F. Nielsen ◽  
...  
Keyword(s):  
Industrial Revolution ◽  
Control Strategies ◽  
Smart Manufacturing ◽  
Monitoring And Control ◽  
Large Sets ◽  
Production Processes ◽  
Recent Developments ◽  
Monitoring Technologies ◽  
And Control ◽  
Industrial Measurement

AbstractThe biomanufacturing industry has now the opportunity to upgrade its production processes to be in harmony with the latest industrial revolution. Technology creates capabilities that enable smart manufacturing while still complying with unfolding regulations. However, many biomanufacturing companies, especially in the biopharma sector, still have a long way to go to fully benefit from smart manufacturing as they first need to transition their current operations to an information-driven future. One of the most significant obstacles towards the implementation of smart biomanufacturing is the collection of large sets of relevant data. Therefore, in this work, we both summarize the advances that have been made to date with regards to the monitoring and control of bioprocesses, and highlight some of the key technologies that have the potential to contribute to gathering big data. Empowering the current biomanufacturing industry to transition to Industry 4.0 operations allows for improved productivity through information-driven automation, not only by developing infrastructure, but also by introducing more advanced monitoring and control strategies.

Tool-Wear Monitoring and Control

2005 ◽  
Author(s):  
J. Srinivas ◽  
Rao Dukkipati ◽  
V. Sreebalaji ◽  
K. Ramakotaih
Keyword(s):  
Tool Wear ◽  
Machine Tools ◽  
Flank Wear ◽  
Cutting Parameters ◽  
Monitoring And Control ◽  
Tool Wear Monitoring ◽  
Wear Monitoring ◽  
Trained Neural Network ◽  
And Control ◽  
Control Methodology

This paper presents, a control methodology based on experimental data of the tool wear as a function of cutting variables. In automatic machine tools there is strong need to control the tool wear by adjustment of the cutting parameters. In this connection, a control system, which can adjust the cutting parameters for a desired wear rate, is necessary. A regression relation is also established between the flank-wear and the cutting parameters. An inversely trained neural network model, which supplies the modified values of the cutting parameters, is used as a controller. The results are shown in the form of tables and graphs.

scholarly journals A Review of Problem Structuring Methods for Consideration in Prognostics and Smart Manufacturing

2020 ◽  
Vol 7 (3) ◽  
Author(s):  
Patrick T. Hester ◽  
Andrew J. Collins ◽  
Barry Ezell ◽  
John Horst
Keyword(s):  
Operations Research ◽  
Health Management ◽  
Development Stage ◽  
Problem Structuring Methods ◽  
Smart Manufacturing ◽  
Prognostics And Health Management ◽  
Problem Structuring ◽  
Research Techniques ◽  
Future System ◽  
The Cost

Successful use of prognostics involves the prediction of future system behaviors in an effort to maintain system availability and reduce the cost of maintenance and repairs. Recent work by the National Institute of Standards and Technology indicates that the field of prognostics and health management is vital for remaining competitive in today’s manufacturing environment. While prognostics-based maintenance involves many traditional operations researchcentric challenges for successful deployment such as limited availability of information and concerns regarding computational efficiency, the authors argue in this paper that the field of prognostics and health management, still in its embryonic development stage, could benefit greatly from considering soft operations research techniques as well. Specifically, the authors propose the use of qualitative problem structuring techniques that aid in problem understanding and scoping. This paper provides an overview of these soft methods and discusses and demonstrates how manufacturers might use them. An approach combining problem structuring methods with traditional operations research techniques would help accelerate the development of the prognostics field.

scholarly journals Designing the Model of the Management of Mental Health Services

2020 ◽  
Vol 12 (4) ◽  
pp. 297-304
Author(s):  
Shahrooz Rahbari ◽  
Leila Riahi ◽  
Jamaleddin Tabibi
Keyword(s):  
Mental Health ◽  
Mental Health Services ◽  
Health Services ◽  
Health Management ◽  
Psychological Services ◽  
Monitoring And Control ◽  
Current State ◽  
Proposed Model ◽  
Control Community ◽  
And Control

Introduction: Having mental health is necessary for the growth and prosperity of humans and as a result of the growth of societies.Objectives: The purpose of this study was to design a mental health management model in Iran.Methods: In this exploratory study, a review study was first performed to analyze the current state of mental health services in Iran and the world. Countries were selected to compare mental health management with Iran in 6 domains. 311 faculty members with mental health were completed by completing a questionnaire with 50 items in the study. Using the factor analysis, the final model was explained. Results: The effective domains in Iranian mental health services management were named in 8 areas: Mental Health in Particular, Key Centers and Task-Shifting, Human Resources and Specialists Training, Psychological Services for Children and Adolescents, Financial Resources and Hospital Services, Mental Health in PHC and Primary medical services, Policy-Making and Human Rights, Monitoring and Control, Community-Based Services. Conclusions: The proposed model of mental health services management in Iran consists of 8 domains, which is a comprehensive and multidimensional concept. Paying attention to its factors can lead to the successful management of mental health services in Iran.

scholarly journals An Axiomatic Categorisation Framework for the Dynamic Alignment of Disparate Functions in Cyber-physical Systems

2019 ◽  
Vol 1 (1) ◽  
pp. 3581-3590
Author(s):  
Thomas J Byrne ◽  
Aleksandr Doikin ◽  
Felician Campean ◽  
Daniel Neagu
Keyword(s):  
Knowledge Integration ◽  
New Technologies ◽  
Health Management ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Communication Devices ◽  
New Generation ◽  
And Control ◽  
Functional Transformations ◽  
Dynamic Alignment

AbstractAdvancing Industry 4.0 concepts by mapping the product of the automotive industry on the spectrum of Cyber Physical Systems, we immediately recognise the convoluted processes involved in the design of new generation vehicles. New technologies developed around the communication core (IoT) enable novel interactions with data. Our framework employs previously untapped data from vehicles in the field for intelligent vehicle health management and knowledge integration into design. Firstly, the concept of an inter-disciplinary artefact is introduced to support the dynamic alignment of disparate functions, so that cyber variables change when physical variables change. Secondly, the axiomatic categorisation (AC) framework simulates functional transformations from artefact to artefact, to monitor and control automotive systems rather than components. Herein, an artefact is defined as a triad of the physical and engineered component, the information processing entity, and communication devices at their interface. Variable changes are modelled using AC, in conjunction with the artefacts, to aggregate functional transformations within the conceptual boundary of a physical system of systems.

Enhancing the Resiliency of Smart Grid Monitoring and Control

2018 ◽  
pp. 3056-3065
Author(s):  
Wenbing Zhao
Keyword(s):  
Fault Tolerance ◽  
Electric Power ◽  
Health Monitoring ◽  
Power Grid ◽  
Byzantine Fault Tolerance ◽  
Monitoring And Control ◽  
Byzantine Fault ◽  
Electric Power Grid ◽  
Grid Monitoring ◽  
And Control

In this chapter, we present the justification and a feasibility study of applying the Byzantine fault tolerance (BFT) technology to electric power grid health monitoring. We propose a set of BFT mechanisms needed to handle the PMU data reporting and control commands issuing to the IEDs. We report an empirical study to assess the feasibility of using the BFT technology for reliable and secure electric power grid health monitoring and control. We show that under the LAN environment, the overhead and jitter introduced by the BFT mechanisms are negligible, and consequently, Byzantine fault tolerance could readily be used to improve the security and reliability of electric power grid monitoring and control while meeting the stringent real-time communication requirement for SCADA operations.

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