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020			_a9783030795191
024	7		_a10.1007/978-3-030-79519-1 _2doi
040			_aTR-AnTOB _beng _cTR-AnTOB _erda
041			_aeng
050		4	_aTK2313
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072		7	_aTEC046000 _2bisacsh
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090			_aTK2313EBK
245	1	0	_aSmart Monitoring of Rotating Machinery for Industry 4.0 _h[electronic resource] / _cedited by Fakher Chaari, Xavier Chiementin, Radoslaw Zimroz, Fabrice Bolaers, Mohamed Haddar.
250			_a1st ed. 2022.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aApplied Condition Monitoring, _x2363-6998 ; _v19
505	0		_aVulnerabilities and fruits of smart monitoring -- A tutorial on Canonical Variate Analysis for diagnosis and prognosis -- A structured approach to machine learning for condition monitoring -- A structured approach to machine learning for condition monitoring: a case study -- Dynamic Reliability Assessment of Structures and Machines Using the Probability Density Evolution Method -- Rotating machinery condition monitoring methods for applications with different kinds of available prior knowledge -- Model Based Fault Diagnosis in Bevel Gearbox -- Investigating the electro-mechanical interaction between helicoidal gears andan asynchronous geared motor -- Algebraic estimator of damping failure for au-tomotive Shock Absorber -- On the use of jerk for condition monitoring of gearboxes in non-stationary operations -- Dynamic remaining useful life estimation for a shaft bearings system. .
520			_aThis book offers an overview of current methods for the intelligent monitoring of rotating machines. It describes the foundations of smart monitoring, guiding readers to develop appropriate machine learning and statistical models for answering important challenges, such as the management and analysis of a large volume of data. It also discusses real-world case studies, highlighting some practical issues and proposing solutions to them. The book offers extensive information on research trends, and innovative strategies to solve emerging, practical issues. It addresses both academics and professionals dealing with condition monitoring, and mechanical and production engineering issues, in the era of industry 4.0.
650		0	_aMachinery.
650		0	_aSignal processing.
650		0	_aDynamics.
650		0	_aNonlinear theories.
650	1	4	_aMachinery and Machine Elements.
650	2	4	_aDigital and Analog Signal Processing.
650	2	4	_aApplied Dynamical Systems.
653		0	_aElectric machinery -- Monitoring
653		0	_aMachine learning
653		0	_aIndustry 4.0
700	1		_aChaari, Fakher. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aChiementin, Xavier. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aZimroz, Radoslaw. _eeditor. _0(orcid)0000-0003-4781-9972 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aBolaers, Fabrice. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aHaddar, Mohamed. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service)
830		0	_aApplied Condition Monitoring, _x2363-6998 ; _v19
856	4	0	_uhttps://doi.org/10.1007/978-3-030-79519-1 _3Springer eBooks _zOnline access link to the resource
942			_2lcc _cEBK