Soft sensor
Soft sensor or virtual sensor is a common name for software where several measurements are processed together. There may be dozens or even hundreds of measurements. The interaction of the signals can be used for calculating new quantities that need not be measured. Soft sensors are especially useful in data fusion, where measurements of different characteristics and dynamics are combined. It can be used for fault diagnosis as well as control applications.
Well-known software algorithms that can be seen as soft sensors include e.g. Kalman filters. More recent implementations of soft sensors use neural networks or fuzzy computing.
Examples of soft sensor applications
- Kalman filters for estimating the location
- velocity estimators in electric motors
- estimating process data using self-organizing neural networks
- fuzzy computing in process control
References
- Fortuna, Luigi; Graziani, Salvatore; Rizzo, Alessandro; Xibilia, M. Gabriella (2007), Soft Sensors for Monitoring and Control of Industrial Processes, Springer-Verlag, ISBN 978-1-84628-479-3
- Angelov, Plamen; Kordon, Arthur (2010), "Adaptive Inferential Sensors based on Evolving Fuzzy Models: An Industrial Case Study", IEEE Transactions on Systems, Man and Cybernetics - part B, 40 (2): 529–539
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- Kadlec, Petr; Gabrys, Sybille; Strandt (2010), "Data-driven Soft Sensors in the Process Industry", Computers and Chemical Engineering, 33 (4): 795–814
- Macias-Hernandez, Jose J; Angelov, Plamen; Zhou, Xiaowei (2007), "Soft Sensor for Predciting Crude Oil Distillation Side Streams using Takagi Sugeno Evolving Fuzzy Models", Proc. 2007 IEEE International Conference on Systems, Man, and Cybernetics: 3305–3310
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- Angelov, Plamen; Kordon, Arthur; Zhou, Xiaowei (2008), "Evolving Fuzzy Inferential Sensors for Process Industry", 3rd International Workshop on Genetic and Evolving Fuzzy Systems: 41–46
- Venkatasubramanian, V.; Rengaswamy, R.; Yin, S.; Kavuri (2003), "A review of process fault detection and diagnosis, three Parts", Computers and chemical engineering, 27 (3): 293–326, doi:10.1016/S0098-1354(02)00161-8