Intelligent Soft Sensors

• Other Authors: Tomažič, Simon (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: MDPI - Multidisciplinary Digital Publishing Institute 2023
• Subjects: Technology: general issues; History of engineering & technology; extended Kalman filter; state estimation; sensor selection; observability; non-linear models; prognostic and health management; extreme learning machine; soft sensors; computerized adaptive testing (CAT); soft-sensor based diagnosis; executive functions; neurodevelopmental disorders; robust observer; bioprocess monitoring; nonlinear systems; general anesthesia; total intravenous anesthesia; target-controlled infusion; propofol; BIS index; depth of hypnosis; improved mathematical model; population-data-based model; residual model; early fire warning; hybrid feature fusion; intelligent building system; D-S evidence theory; affective computing; EDA; stress detection; physiological signals; frequency analysis; shape memory coil; joule heating effect; self-sensing actuation; variable stiffness actuation; electrical resistance; support vector machine regression model; nonlinear regression model; multi-source data fusion; sintering quality prediction; image feature extraction; keyframe extraction; soft sensor; improved particle swarm algorithm; least squares support vector machine; transfer learning; Pichia pastoris; spectroscopy; Raman; modelling; variable selection; outliers; simulator; kinetic model; n/a
• Online Access: DOAB: download the publication; DOAB: description of the publication

This Special Issue deals with the field of intelligent soft sensors that enable the online estimation of nonmeasurable process variables. Soft sensors or virtual sensors are common names for software algorithms in which multiple measurements are processed together. Typically, soft sensors are based on control theory and are also referred to as state observers. There may be dozens or even hundreds of measurements from hard sensors (big data). The interaction of signals can be used to compute new quantities that cannot be measured directly online or are difficult and expensive to measure. Soft sensors are particularly useful in data fusion, combining measurements of different characteristics and dynamics. They can be used for fault diagnosis (self-analysis, self-calibration, and self-maintenance) as well as for control applications. Well-known software algorithms that can be seen as soft sensors include, for example, Kalman filters. More recent implementations of soft sensors use neural networks, fuzzy logic, models based on evolving clustering, partial least squares, etc. In the digitized factories of the future, intelligent sensors represent one of the core building blocks for automating and optimizing production, as they make production more efficient in every respect.