In signal technology, the sampling frequency or sampling rate describes the frequency used to sample a time-continuous analog signal at discrete points in order to generate a time-discrete digital signal.
The IfTA AD4 family module allows sampling rates up to 204,8 kHz.
The sampling theorem, namely the Nyquist-Shannon sampling theorem, is a fundamental theorem in telecommunications, signal processing and information theory. It deals with the question to what extent an analog output signal can be reconstructed without any errors from a time-discrete digitized signal. In order to be able to measure a signal, the sampling frequency must be at least twice the maximum frequency of the analog signal to be measured. Otherwise, the so called aliasing occurs.
In signal processing, anti-aliasing means the active prevention or reduction of aliasing effects. Aliasing effects occur mainly when the sampled signal has higher frequencies than half the sampling frequency. This effect is expressed by the fact that frequencies higher than half the sampling frequency are wrongly interpreted as lower frequencies. To prevent aliasing in technical applications, low-pass filters are used to filter out frequencies above the half sampling frequency before sampling.
At high sampling rates and many channels, large amounts of data accumulate within a short period of time which can hardly be stored for longer periods of time nor analyzed. For example, only the maximum amplitude within one minute can be stored, or only the maximum amplitude across all channels can be stored at one time instead of storing each channel.
The IfTA DataHub realizes this data aggregation and allows to make large amounts of data visible by means of small compact overview files.
The Fast Fourier Transformation is a particularly fast and efficient algorithm for calculating the discrete Fourier Transformation (DFT). Digitized time-discrete signals can be converted into their individual frequency components by the Fourier transform for further analysis.
What is the advantage of combining online & offline data analysis?
By combining the analysis of offline and online data in one program it is possible to compare currently measured vibration parameters of the monitored machine with already existing and released measurement data. In addition to the dynamic data of the rotor dynamics, slower process variables can also be displayed in the same plot. Thus complex scenarios can be analyzed more efficiently. On the hardware side, both slower measurement cards and modules for bus connection are available.
The IfTA ArgusOMDS sets standards for the detailed and complete analysis of the vibration behavior of rotors.
An order in rotating machines is defined as a multiple of the rotational speed (1st order = 1 x rotational speed, 2nd order = 2 x rotational speed, ..., n x rotational speed). The order analysis examines order-dependent phenomena such as unbalance or friction over a changed rotational speed.
For order tracking, a signal with discrete values depending on the angular position of the wave is calculated using resampling techniques. This signal is transformed via FFT into the order domain instead of the frequency domain. Therefore, there is no smearing of order-dependent phenomena and they can be identified and resolved more easily.
IfTA TrendViewer offers order tracking as an optional calculation.
In resampling, a discrete signal is rewritten to other time-discrete data points. It is mainly used as a preparation for order tracking, where a signal is resampled depending on the angular position of the wave.
A trigger is defined as a condition that initiates further actions in the measuring system. The condition can be defined both within the measuring system or externally. An example for this is the storage of data as soon as a limit value is exceeded. Such limit values can usually be defined for known events. If, however, in particular sporadic events for which no trigger can yet be defined, continuous storage is preferable, otherwise the events will not be recorded if the limit values are too high. Further application examples are the manual triggering to take measuring points, the storage of transient operating points with higher resolution than constant operating points (RunUp, RunDown vs. Steady State for rotors), as well as the storage of unexpected events such as reliefs or emergency shutdowns of machines.