Soft sensor or
virtual sensor is a common name for software where several measurements are processed together. Commonly soft sensors are based on control theory and also receive the name of
state observer In control theory, a state observer or state estimator is a system that provides an estimate of the internal state of a given real system, from measurements of the input and output of the real system. It is typically computer-implemented, and pr ...
. 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
Data fusion is the process of integrating multiple data sources to produce more consistent, accurate, and useful information than that provided by any individual data source.
Data fusion processes are often categorized as low, intermediate, or hig ...
, where measurements of different characteristics and dynamics are combined. It can be used for fault
diagnosis
Diagnosis is the identification of the nature and cause of a certain phenomenon. Diagnosis is used in many different disciplines, with variations in the use of logic, analytics, and experience, to determine " cause and effect". In systems engin ...
as well as control applications.
Well-known software algorithms that can be seen as soft sensors include e.g.
Kalman filter
For statistics and control theory, Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, including statistical noise and other inaccuracies, and produces estimat ...
s. More recent implementations of soft sensors use
neural network
A neural network is a network or circuit of biological neurons, or, in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus, a neural network is either a biological neural network, made up of biological ...
s or
fuzzy computing.
Examples of soft sensor applications:
*
Kalman filter
For statistics and control theory, Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, including statistical noise and other inaccuracies, and produces estimat ...
s for estimating the
location
In geography, location or place are used to denote a region (point, line, or area) on Earth's surface or elsewhere. The term ''location'' generally implies a higher degree of certainty than ''place'', the latter often indicating an entity with an ...
* Velocity estimators in
electric motor
An electric motor is an Electric machine, electrical machine that converts electrical energy into mechanical energy. Most electric motors operate through the interaction between the motor's magnetic field and electric current in a Electromagneti ...
s
* Estimating process data using
self-organizing
Self-organization, also called spontaneous order in the social sciences, is a process where some form of overall order arises from local interactions between parts of an initially disordered system. The process can be spontaneous when suff ...
neural networks
* Fuzzy computing in
process control
An industrial process control in continuous production processes is a discipline that uses industrial control systems to achieve a production level of consistency, economy and safety which could not be achieved purely by human manual control. I ...
* Estimators of food quality
See also
*
Virtual sensing Virtual sensing techniques, also called soft sensing, proxy sensing, inferential sensing, or surrogate sensing, are used to provide feasible and economical alternatives to costly or impractical physical measurement instrument. A virtual sensing sy ...
*
State observer In control theory, a state observer or state estimator is a system that provides an estimate of the internal state of a given real system, from measurements of the input and output of the real system. It is typically computer-implemented, and pr ...
References
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* {{citation , doi= 10.1016/S0098-1354(02)00161-8 , last1= Venkatasubramanian , first1=V. , last2= Rengaswamy , first2=R. , last3= Yin , first3=S. , last4= Kavuri , title=A review of process fault detection and diagnosis, three Parts , journal=Computers and Chemical Engineering , volume=27 , issue=3 , year=2003 , pages=293–326 , citeseerx = 10.1.1.91.2319
External links
Helsinki University of Technology
Sensors
Nonlinear filters
Linear filters