Function Smoothing/ Noise Reduction

In summary, the conversation discusses a problem with a laser sensor being used as a bin level sensor. The spikes in the sensor readings are caused by large rocks passing in front of the laser. The desired outcome is a smooth trend in the readings, with the sampling rate being 2 seconds. The conversation also mentions the use of a binary output and the time span of the graph. The sensor is attached and there is a communication network between the sensor and the graph. The ultimate goal is to remove the large spikes in the readings.
  • #1
Valdima
10
0
Hi,

my problem involves a laser sensor as a bin level sensor. what i want to do is get rid of the noise from the signal obtained. the spikes in the picture below are because the location of the laser sensor has to be where it is and when large rocks move down into the chute they pass infront of the laser giving it temporary high readings. what i want at the end of the day is a smooth trend. if you need more info from me post what you need.

the sampling rate is 2 seconds

http://img855.imageshack.us/img855/7298/functiony.png
 
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  • #2
Do you mean the output is binary where either it is at high level when the rock pass through and low if nothing pass through? The sharp spikes that go even beyond the binary level are the one you want to get rid of and show only high and low?

You said sample rate is 2 second per sample, what is the time span of the graph? How wide are the spikes that you want to filter out?
 
  • #3
it is a laser distance sensor

where the distance detected varies the output, I have attached the sensor in question.

also it is a real time level sensor for the bin so the percentage on the right hand side represents the bin level and 32% is the usual bin level, based on flow rates.

the graph is being updated every 2 seconds. there is a comms network between the sensor and this graph,

the hierarchy is like
sensor-plc-scada

but yeah the aim is to remove the large spikes both up and down.
 

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1. What is function smoothing/noise reduction?

Function smoothing/noise reduction is a technique used to remove random variations or noise from a function or data set. This is done by applying mathematical methods to smooth out the curve and make it more continuous.

2. What are the benefits of function smoothing/noise reduction?

The main benefit of function smoothing/noise reduction is to improve the overall quality and clarity of the data. This can make it easier to analyze and interpret the function, and can also help to identify any underlying patterns or trends that may have been hidden by the noise.

3. What are some common methods for function smoothing/noise reduction?

Some of the most commonly used methods for function smoothing/noise reduction include moving averages, polynomial regression, and the Savitzky-Golay filter. These methods use different mathematical techniques to smooth out the function and reduce any noise present in the data.

4. How do I choose the best method for function smoothing/noise reduction?

The best method for function smoothing/noise reduction will depend on the specific data set and the level of noise present. It is important to consider the trade-off between smoothing and preserving important features of the function. It may be necessary to try out different methods and compare the results to determine the most suitable approach.

5. Are there any limitations to function smoothing/noise reduction?

While function smoothing/noise reduction can greatly improve the quality of data, it is important to note that it is not a perfect solution. In some cases, it may oversmooth the function and remove important features. It is also important to consider the potential biases introduced by the choice of smoothing method. Additionally, the effectiveness of function smoothing/noise reduction may be limited by the amount and type of noise present in the data.

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