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Noise

Filtering topics:

 

This page describes the causes of noise. This page is part of the section on
Filtering that is part of
A Guide to Fault Detection and Diagnosis..

Sources of noise

The purpose of filtering is to reduce noise. But what is noise, and where does it come from? Two major categories of noise are sensor noise and process noise.

Sensor noise is caused by errors in the sensors and the transmission of the signals. Sensor noise includes “spikes” as well as high frequency variations. High frequency variations include “hum” at frequencies that are a multiple of the power supply frequency. Spikes in signals occur from many sources and are picked up by the instrumentation and its wiring. Sudden changes in electrical load caused by events such as motor starts and stops will pass spikes through wiring, and may also generate enough electromagnetic energy that nearby sensors and their wiring may pick it up. Arcs in electric motors, or caused by arc welding can similarly generate spikes. Inadequately shielded sensors or their transmission lines, and variables transmitted through wireless links are vulnerable to radio frequency interference, leading to spikes, and pickup of other noise such as electrical hum. Noise can also be introduced through ground loops.

Process noise is rapid variation that originates in the monitored process itself. This might be true variation in variable values, but might be of such a high frequency that it is of no interest for a particular analysis. In the process industries, examples include noise introduced by turbulence, boiling, or even cyclic variations introduced by badly tuned controllers. In the latter case, these variations are not at all random. But, relatively high-frequency flow controller cycling might be of little interest when analyzing the behavior of large equipment. A time-averaged value is of more interest when considering steady state models for diagnosis.

In monitoring computer system or network behavior, some variables such as CPU utilization or network utilization can vary extremely rapidly.

Diagnostic systems looking at any of these kinds of variables need to use filtering to reduce the effects of high frequency noise and spikes.

 

Copyright 2010 - 2013, Greg Stanley

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