03437nam a22003734u 450000100220000000300060002200500170002800700150004500800410006002000300010102400310013104100080016224501470017025000060031726000160032326400160033930000120035533600260036733700260039333800360041934700210045534700080047652017840048465000380226865000350230665000320234165302870237370000420266077600360270285600640273885601020280285601420290499900170304610.1002/9781119825920Wiley20251030120313.0cr nn 008mam|a240126t2024||||xxu    fs|   |0|| 0|eng||  a9781119825920qelectronic7 a10.1002/97811198259202doi  aeng00aArtificial Intelligence in Process Fault DiagnosisbMethods for Plant Surveillanceh[electronic resource]cEdited by Richard J. Fickelscherer.  a1  bWileyc2024 1bWileyc2024  a432pp.;  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext file2rdaft  bPDF  aArtificial 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 experts Guidance for creating programs that can prevent catastrophic operating disasters, reduce downtime after emergency process shutdowns, and more Comprehensive 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. 4aComputer-Aided Engineering966003 4aArtificial Intelligence958441 4aIndustrial Chemistry96961100aArtificial Intelligence; process fault diagnosis; Machine learning; data science; data analysis; process control; process monitoring; Fuzzy logic; emergency process shutdowns; diagnostic strategy; petroleum processing; chemical processing; nuclear processing; operations engineering1 aFickelscherer, Richard J.4edt99188408iPrinted edition:z978111982589040uhttps://doi.org/10.1002/9781119825920aWiley Online Library40uhttps://onlinelibrary.wiley.com/action/showBook?doi=10.1002%2F9781119825920aWiley Online Library423Cover Imageuhttps://onlinelibrary.wiley.com/cms/asset/03eb8af9-fc7e-41a7-ab11-7c559a60bca7/9781119825920.cover.gifaWiley Online Library  c67131d67131