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710	2		_aSpringerLink (Online service) _9106996
999			_c110815 _d110815 _x1
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003			ES-MaUEC
005			20230102113439.0
008			190228s2019 gw a o |||| 0|eng d
020			_a9783030056452
024	7		_a10.1007/978-3-030-05645-2 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aTS192 _b2019 EB
245	0	0	_aPredictive Maintenance in Dynamic systems : _bAdvanced Methods, Decision Support Tools and Real-World Applications _cedited by Edwin Lughofer, Moamar Sayed-Mouchaweh
264		1	_aCham _bSpringer International Publishing : _bImprint: Springer _c2019
300			_a1 recurso en línea (XIII, 567 páginas) _b200 ilustraciones, 144 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aEngineering (Springer-11647)
505	0		_aIntroduction -- Predictive Maintenance and (Early) FDD in Dynamic Systems -- Beyond State-of-the-Art -- Early Fault Detection and Diagnosis Approaches -- Prognostics and Forecasting -- Self-Reaction and Self-Healing Techniques -- Applications of Predictive Maintenance with emphasize on Industry 4.0 challenges -- Conclusion.
520	3		_aThis book provides a complete picture of several decision support tools for predictive maintenance. These include embedding early anomaly/fault detection, diagnosis and reasoning, remaining useful life prediction (fault prognostics), quality prediction and self-reaction, as well as optimization, control and self-healing techniques. It shows recent applications of these techniques within various types of industrial (production/utilities/equipment/plants/smart devices, etc.) systems addressing several challenges in Industry 4.0 and different tasks dealing with Big Data Streams, Internet of Things, specific infrastructures and tools, high system dynamics and non-stationary environments . Applications discussed include production and manufacturing systems, renewable energy production and management, maritime systems, power plants and turbines, conditioning systems, compressor valves, induction motors, flight simulators, railway infrastructures, mobile robots, cyber security and Internet of Things. The contributors go beyond state of the art by placing a specific focus on dynamic systems, where it is of utmost importance to update system and maintenance models on the fly to maintain their predictive power. .
988			_aPrimersemestre_2019_Engineering
650		7	_2embne _aMantenimiento industrial _9140848
650		7	_2embne _9154786 _aMantenimiento productivo total
700	1		_aLughofer, Edwin. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aSayed-Mouchaweh, Moamar. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _997837
776	0	8	_iPrinted edition: _z9783030056445
776	0	8	_iPrinted edition: _z9783030056469
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-05645-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _cm _dz _feng _ggw _h0 _b09/2019 _eel _zSI