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Soft sensor

Soft sensor is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Soft sensor rather than just read about it. In short: 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.

Key takeaways

  • Soft sensor belongs to science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Soft sensor to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Soft sensor from memory before moving on to harder problems.

Reference excerpt

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. 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 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 Estimators of food quality

See also Virtual sensing State observer

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 Kadlec, Petr; Gabrys, Bogdan; Strandt, Sybille (2009), "Data-driven Soft Sensors in the Process Industry" (PDF), Computers and Chemical Engineering, 33 (4): 795–814, doi:10.1016/j.compchemeng.2008.12.012 Karri, Rama Rao; Damaraju, Phaneswara Rao; Venkateswarlu, Chimmiri (2009), "Soft Sensor Based Nonlinear Control of a Chaotic Reactor", Intelligent Control Systems and Signal Processing, 2 (1): 537–543, doi:10.3182/20090921-3-TR-3005.00093 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, CiteSeerX 10.1.1.91.2319, doi:10.1016/S0098-1354(02)00161-8 {{citation}}: Cite uses deprecated parameter |citeseerx= (help)

External links Helsinki University of Technology Soft Sensors for process applications in gas industry

Worked examples

Example 1 — a first encounter with Soft sensor

Start with the simplest possible case. Write down what Soft sensor claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Soft sensor before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Soft sensor ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Soft sensor

In research
Soft sensor appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Soft sensor in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Soft sensor is common in secondary-school and first-year university syllabi. It links to neighbouring topics Linear filters, Nonlinear filters, Sensors, so understanding it makes those chapters shorter.
In everyday life
Look for Soft sensor outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Soft sensor in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Soft sensor means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Soft sensor out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Soft sensor in simple terms?

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.

Why does Soft sensor matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Soft sensor?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Soft sensor.

Tags

  • Linear filters
  • Nonlinear filters
  • Sensors

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