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Automating Errors and Conflicts Prognostics and Prevention

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Springer Handbook of Automation

Part of the book series: Springer Handbooks ((SHB))

Abstract

Errors and conflicts exist in many systems. A fundamental question from industries is How can errors and conflicts in systems be eliminated by automation, or can we at least use automation to minimize their damage? The purpose of this chapter is to illustrate a theoretical background and applications of how to automatically prevent errors and conflicts with various devices, technologies, methods, and systems. Eight key functions to prevent errors and conflicts are identified and their theoretical background and applications in both production and service are explained with examples. As systems and networks become larger and more complex, such as global enterprises and the Internet, error and conflict prognostics and prevention become more important and challenging; the focus is shifting from passive response to proactive prognostics and prevention. Additional theoretical developments and implementation efforts are needed to advance the prognostics and prevention of errors and conflicts in many real-world applications.

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Abbreviations

CCM:

CORBA component model

CDMS:

conflict detection and management system

CED:

concurrent error detection

CEDA:

conflict and error detection agent

CEDM:

conflict and error detection management

CEDM:

conflict and error detection model

CEDP:

conflict and error detection protocol

CEDP:

conflict and error diagnostics and prognostics

CORBA:

common object request broker architecture

DES:

discrete-event system

DSS:

decision support system

EPC:

engineering, procurement, and contsruction

FDL:

facility design language

FIS:

fuzzy inference system

FSM:

finite-state machine

HAD:

heterogeneous, autonomous, and distributed

IGS:

intended goal structure

JPL:

Jet Propulsion Laboratory

MAS:

multiagent system

MDP:

Markov decision process

Mcr:

multi-approach to conflict resolution

NASA:

National Aeronautics and Space Administration

NEFUSER:

neural-fuzzy system for error recovery

NEFUSER:

neuro-fuzzy systems for error recovery

OR:

operating room

OR:

operation research

PERT:

project evaluation and review technique

RT:

radiotherapy

RT:

register transfer

TEAMS:

testability engineering and maintenance system

WFMS:

workflow management system

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Authors and Affiliations

  1. PRISM Center and School of Industrial Engineering, Purdue University, 315 N. Grant Street, 47907, West Lafayette, IN, USA

    Xin W. Chen MSc

  2. PRISM Center and School of Industrial Engineering, Purdue University, 47907, West Lafayette, IN, USA

    Shimon Y. Nof Prof

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  1. Xin W. Chen MSc
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  2. Shimon Y. Nof Prof
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Correspondence to Xin W. Chen MSc or Shimon Y. Nof Prof .

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  1. PRISM Center, and School of Industrial Engineering, Purdue University, 315 N. Grant Street, 47907, West Lafayette, IN, USA

    Shimon Y. Nof

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Chen, X.W., Nof, S.Y. (2009). Automating Errors and Conflicts Prognostics and Prevention. In: Nof, S. (eds) Springer Handbook of Automation. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78831-7_30

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