000			03483nam a22003854u 4500
001			10.1002/9781119825920
003			Wiley
005			20251030120313.0
007			cr nn 008mam|a
008			240126t2024||||xxu fs| |0|| 0|eng||
020			_a9781119825920 _qelectronic
024	7		_a10.1002/9781119825920 _2doi
041			_aeng
245	0	0	_aArtificial Intelligence in Process Fault Diagnosis _bMethods for Plant Surveillance _h[electronic resource] _cEdited by Richard J. Fickelscherer.
250			_a1
260			_bWiley _c2024
264		1	_bWiley _c2024
300			_a432pp.;
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _2rdaft
347			_bPDF
520			_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.
650		4	_aComputer-Aided Engineering _966003
650		4	_aArtificial Intelligence _958441
650		4	_aIndustrial Chemistry _969611
653	0	0	_aArtificial 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 engineering
700	1		_aFickelscherer, Richard J. _4edt _991884
776	0	8	_iPrinted edition: _z9781119825890
856	4	0	_uhttps://doi.org/10.1002/9781119825920 _aWiley Online Library
856	4	0	_uhttps://onlinelibrary.wiley.com/action/showBook?doi=10.1002%2F9781119825920 _aWiley Online Library
856	4	2	_3Cover Image _uhttps://onlinelibrary.wiley.com/cms/asset/03eb8af9-fc7e-41a7-ab11-7c559a60bca7/9781119825920.cover.gif _aWiley Online Library
999			_c67131 _d67131