The analysis of time waveform data is not a new technique. In the early days of vibration
analysis time waveform data was viewed on oscilloscopes and frequency components
calculated by hand.

Today we have instruments which performs both the recording adn the calculation of frequency components. [FFT-analyse]

In most situations time waveform data is best utilized by applying the principles of
pattern recognition and if necessary calculating the frequency components of the major
events in the waveform pattern.

Time waveform can be used effectively to enhance spectral information in the following
applications:

• Low speed applications (less than 100 RPM)
• Indication of true amplitude in situations where impacts occur such as assessment of rolling element bearing defect severity
• Gears
• Sleeve bearing machines with X-Y probes (2 channel orbit analysis)
• Looseness
• Rubs
• Beats

Time waveform can be applied to any vibration problem. In some situations normal
spectral and phase data provide better indications as to the source of the problem
without the added complexity of time waveform data. Examples include:

• Unbalance on normal speed machines
• Misalignment on normal speed machines

An algorithm for computing the Fourier transform of a set of descrete datavalues. Given a finite set of data points, for example a periodic sampling taken from a real-world signal, the FFT expresses the data in terms oft its component frequencies.

It also solves the essentially identical inverse problem of reconstructing a signal from the frequency data.

The FFT is an effective method when analysing frequencies in a waveform signal. Especially when working with frequency analysis on rotating machinery the FFT becomes as an helping tool and is useful when detecting wearing, damages and poor running conditions.

Mechanical degradation can be controlled periodically!

Advantages:

• High operational reliability and the possibility of condition classification
• Maintenance only need to be performed in terms of real requirement
• Reduced Off-Hire and sparepart cost

FFT is also a useful algoritm when it comes to acoustic noise. By analysing the content of frequency you kan substantiate which of the frequencies who has largest contribution in the frequency spectra. This is useful when locating source of noise or when covering resonance phenomenon.

This kind of technique is best suited for frequency analysis on bearings and gears.

The bearing frequencies are present throughout the spectrum (the 1/T line spectrum), but obscured at lower frequencies by other vibrations.

However, there is a technique that makes it possible to extract the bearing frequencies from the part of the vibration spectrum where the 1/T line spectrum is dominant, that is, amplitude demodulation: A band-pass filter, with centre frequency fc, filters out the selected part of the spectrum, the output is shifted (heterodyned) to low frequency (fc → DC) and subjected to envelope detection.

If the band-pass filter encompasses a range where the 1/T line spectrum is dominant,
the resulting time history will be dominated by the envelope of the original pulse train. This envelope time history can now be subjected to FFT analysis for easy identification of Bearing Frequencies.

The top trace in the figure to the left [enlarge] is a gear box rolling element bearing vibration spectrum.

Directly below are three envelope spectra of the vibration signal with different frequencies of the envelope detector filter. The first envelope spectrum was measured in the third octave frequency band with a center frequency coinciding with the third order gear mesh frequency component. No harmonic components are present in this envelope spectrum, so we might conclude that there are no defects in the bearing or gear mesh. However, the envelope spectrum measured in the frequency band between the third and fourth orders contains shock pulses. The shock pulses are not at the rotating frequency of the bearing on which the vibration signal was obtained but rather at the rotating frequency of the meshing gear.

The envelope spectrum also contains a series of orders of the BPFO. This indicates cavities (pitting) on the outer race. The enveloped vibration spectrum recorded on the other bearing of the same shaft contains only multiples of the meshing shaft rotating frequency. Thus, both bearings are influenced by shock loads transmitted from faulted teeth located on the meshing gear. This analysis of the spectra presented in the figure, leads to the following conclusion:

Shock loads induced by mesh defects can be detected at all bearings associated with a mesh by choosing the correct frequency band for the envelope detector. In this example, the mesh shock loads do not interfere with the detection of bearing rolling surface flaws.