|LabVIEW 2018 Sound and Vibration Toolkit Help|
|LabVIEW 2019 Sound and Vibration Toolkit Help|
One of the most common methods for analyzing sound and vibration signals is Fast Fourier Transform (FFT) analysis. The following figure shows an FFT analysis on the vibration signal of a PC fan with seven blades and four coils.
Notice that the overall vibration signal of the PC fan is the superposition of the vibration from the shaft, coils, and blades. The PC fan rotates at 3,300 RPM. The shaft rotates at the same rate as the rotational speed of the PC fan, whereas the rotational speeds of the coils and blades are four and seven times that of the PC fan, respectively. The vibration signal of the shaft has the same frequency as the rotational speed of the PC fan, which is 55 Hz. The coil and blade vibration signals are the fourth and seventh harmonics of the rotational speed of the PC fan, which are 220 Hz and 385 Hz, respectively.
You can use an FFT power spectrum to identify and quantify the frequency components of a sound or vibration signal. The following figure shows the FFT power spectrum of the PC fan.
The FFT power spectrum of the vibration signal shows peaks at the rotational speed and at the fourth and seventh harmonics of the rotational speed. You thus can use the FFT power spectrum for machinery diagnostic purposes by associating certain frequency components with specific mechanical parts.
Many mechanical characteristics of rotating or reciprocating machinery change with speed. You can observe some mechanical faults, such as resonance, only as the rotational speed approaches or passes the critical speed. For this reason, machine sound and vibration tests usually require a run-up or run-down test. However, when the rotational speed changes, the frequency bandwidth of each individual harmonic gets wider. As a result, some frequency components might overlap. The resulting FFT power spectrum can no longer help you identify characteristic vibration components because no obvious peaks appear in the spectrum. The following figure shows the FFT power spectrum of the PC fan when the rotational speed changes from 1,000 to 4,000 revolutions per minute (RPM).
Notice that you cannot identify any obvious peaks associated with particular mechanical parts in the FFT power spectrum plot.
Order analysis techniques enable you to analyze sound and vibration signals when the rotational speed changes over time. An order is the normalization of the rotational speed. The first order is the rotational speed, and order n is n times the rotational speed. Order components thus are the harmonics of the rotational speed. In the case of the PC fan, the shaft vibration is the first order vibration. The coil and blade vibrations are the fourth and seventh order vibrations, respectively. With order analysis, you can uncover information about harmonics buried in the FFT power spectrum due to changing rotational speed.
The order power spectrum is one of the order analysis functions that the NI Sound and Vibration Measurement Suite provides. The following figure shows the order power spectrum of the same signal used to compute the FFT power spectrum plot above.
The Order power spectrum plot shows clearly-defined peaks associated with different mechanical parts. The peak at the first order corresponds to the shaft vibration. The peak at the fourth order corresponds to the vibration of the coils. The peak at the seventh order corresponds to the vibration of the blades.
In general, order analysis techniques relate sound and vibration signals to the rotational speed. Order analysis techniques also reduce these signals to characteristic components, associate the components with mechanical parts, and provide repeatable sound and vibration measurements. You can obtain information about individual mechanical parts as well as the entire machine with order analysis.