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Vibration Information for rotating machinery applications.

 

Vibration Measurements: Recognizing Bad Data

Monday, June 06, 2011

When taking vibration measurements it is important to verify that the data does not contain errors. Bad data can be caused by several factors including: poor cable connections, faulty sensors, mechanical and thermal shocks to the sensors, improper mounting conditions, and settling time.  Most data collection instrumentation provides a readout of the vibration signals and a visual check of the data can usually determine if there are any measurement errors. Errors usually cause specific, recognizable patterns in the time waveform and spectral data.

Bad data can cause the DC offset of the time waveform to vary, even though the waveform amplitude is unchanged. Figure 1 shows an example of a waveform signal that is “settling”.

vibration measurement

Collecting erroneous data can be prevented by understanding the underlying cause of these errors. Modern vibration sensors have built-in amplifiers and integrated circuits that require a short period of time to “settle” after powering them; if data is collected immediately after powering the sensor it will result in the errors illustrated in Figures 1 and 2.

Another source of bad data is when sensors receive a thermal shock; this may occur when transferring sensors from hot to cold surfaces, and vice versa. The sudden temperature change affects the circuitry performance and it is important to let the sensor temperature stabilize.

A mechanical shock can also cause measurement errors; this usually occurs when a sensor is bumped or knocked. It is always a good idea to allow sensors to settle before taking measurements.

Sensor overloading can occur due to extremely high vibration amplitudes, which saturate the sensor and lead to false results. Saturation can be caused by excessive vibration from adjacent machines or by extremely high frequency vibration sources such as cavitation.

Loose mounting can result in unexpected harmonics in the spectral data. This typically occurs when a sensor is not attached firmly to a machine. Poor mounting can also prevent the sensor from detecting certain frequencies, although this may not be obvious from observing the data.

There are several causes of erroneous data and it is recommended that you be aware of the possible sources or errors. Data analysis can only be reliable if the data is collected in a proper manner.

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