000			02088nam a22002177a 4500
999			_c4520 _d4520
005			20230208113727.0
008			230208b ||||| |||| 00| 0 eng d
020			_a9780128193655
082			_a629.895 _bHAR
100			_aHarrou, Fouzi _910534
245			_aStatistical process monitoring using advanced data-driven and deep learning approaches: _btheory and practical applications
260			_bElsevier _aCambridge _c2021
300			_axii, 315 p.
365			_aUSD _b150.00
504			_aTable of content 1. Introduction 2. Linear Latent Variable Regression (LVR)-Based Process Monitoring 3. Fault Isolation 4. Nonlinear latent variable regression methods 5. Multiscale latent variable regression-based process monitoring methods 6. Unsupervised deep learning-based process monitoring methods 7. Unsupervised recurrent deep learning schemes for process monitoring 8. Case studies 9. Conclusions and future perspectives
520			_aStatistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches tackles multivariate challenges in process monitoring by merging the advantages of univariate and traditional multivariate techniques to enhance their performance and widen their practical applicability. The book proceeds with merging the desirable properties of shallow learning approaches – such as a one-class support vector machine and k-nearest neighbours and unsupervised deep learning approaches – to develop more sophisticated and efficient monitoring techniques. Finally, the developed approaches are applied to monitor many processes, such as waste-water treatment plants, detection of obstacles in driving environments for autonomous robots and vehicles, robot swarm, chemical processes (continuous stirred tank reactor, plug flow rector, and distillation columns), ozone pollution, road traffic congestion, and solar photovoltaic systems.
650			_aMachine learning _92343
650			_aMultivariate analysis--Data processing _911791
650			_aProcess control--Statistical methods _95810
942			_2ddc _cBK