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Artificial Intelligence in Process Fault Diagnosis : Methods for Plant Surveillance

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Τίτλος: Artificial Intelligence in Process Fault Diagnosis : Methods for Plant Surveillance
Περιγραφή: Artificial Intelligence in Process Fault Diagnosis A comprehensive guide to the future of process fault diagnosis Automation has revolutionized every aspect of industrial production, from the accumulation of raw materials to quality control inspections. Even process analysis itself has become subject to automated efficiencies, in the form of process fault analyzers, i.e., computer programs capable of analyzing process plant operations to identify faults, improve safety, and enhance productivity. Prohibitive cost and challenges of application have prevented widespread industry adoption of this technology, but recent advances in artificial intelligence promise to place these programs at the center of manufacturing process analysis. Artificial Intelligence in Process Fault Diagnosis brings together insights from data science and machine learning to deliver an effective introduction to these advances and their potential applications. Balancing theory and practice, it walks readers through the process of choosing an ideal diagnostic methodology and the creation of intelligent computer programs. The result promises to place readers at the forefront of this revolution in manufacturing. Artificial Intelligence in Process Fault Diagnosis readers will also find: Coverage of various AI-based diagnostic methodologies elaborated by leading expertsGuidance for creating programs that can prevent catastrophic operating disasters, reduce downtime after emergency process shutdowns, and moreComprehensive overview of optimized best practices Artificial Intelligence in Process Fault Diagnosis is ideal for process control engineers, operating engineers working with processing industrial plants, and plant managers and operators throughout the various process industries.
Συγγραφείς: Richard J. Fickelscherer
Resource Type: eBook.
Θέματα: Chemical process control--Data processing, Fault location (Engineering)--Data processing, Artificial intelligence--Industrial applications
Categories: SCIENCE / Chemistry / Industrial & Technical
Βάση Δεδομένων: eBook Index
FullText Text:
  Availability: 0
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  Data: Artificial Intelligence in Process Fault Diagnosis A comprehensive guide to the future of process fault diagnosis Automation has revolutionized every aspect of industrial production, from the accumulation of raw materials to quality control inspections. Even process analysis itself has become subject to automated efficiencies, in the form of process fault analyzers, i.e., computer programs capable of analyzing process plant operations to identify faults, improve safety, and enhance productivity. Prohibitive cost and challenges of application have prevented widespread industry adoption of this technology, but recent advances in artificial intelligence promise to place these programs at the center of manufacturing process analysis. Artificial Intelligence in Process Fault Diagnosis brings together insights from data science and machine learning to deliver an effective introduction to these advances and their potential applications. Balancing theory and practice, it walks readers through the process of choosing an ideal diagnostic methodology and the creation of intelligent computer programs. The result promises to place readers at the forefront of this revolution in manufacturing. Artificial Intelligence in Process Fault Diagnosis readers will also find: Coverage of various AI-based diagnostic methodologies elaborated by leading expertsGuidance for creating programs that can prevent catastrophic operating disasters, reduce downtime after emergency process shutdowns, and moreComprehensive overview of optimized best practices Artificial Intelligence in Process Fault Diagnosis is ideal for process control engineers, operating engineers working with processing industrial plants, and plant managers and operators throughout the various process industries.
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      – Code: 670.427
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        Type: prePub
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      – Code: eng
        Text: English
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      – SubjectFull: Fault location (Engineering)--Data processing
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              Type: published
              Y: 2024
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              M: 11
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              Y: 2025
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