Block-Wise Parallel Semisupervised Linear Dynamical System for Massive and Inconsecutive Time-Series Data With Application to Soft Sensing

Linear dynamical system (LDS) has established itself as a powerful tool for soft sensing dynamic industrial processes, which, however, still faces some practically pivotal issues. First, traditional LDS-based soft sensors require training samples to be labeled and ignore time delays (TDs) between ex...

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Veröffentlicht in:	IEEE transactions on instrumentation and measurement 2022, Vol.71, p.1-14
Hauptverfasser:	Shao, Weiming, Li, Yougao, Han, Wenxue, Zhao, Dongya
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Communications systems Data communication Data models Dynamic soft sensor Dynamical systems Finite impulse response filters Heuristic algorithms Impact prediction linear dynamical system (LDS) Machine learning Malfunctions massive and inconsecutive time-series data Model accuracy parallel computing Performance degradation Performance evaluation Performance prediction Predictive models Semisupervised learning Sensors Soft sensors Training variable time delay (TD)
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