The damping factor refers to the damping ratio for each
frequency. It makes it possible to obtain the damping ratio of
the target frequency and affects the selection of materials
together with the natural frequency.
The value of the damping factor is represented in percent.
The larger (smaller) the damping factor is, the quicker (slower)
the frequency dampens. The FFT analyzer makes it easier, for
each resonance frequency, to obtain the damping factor as the
inverse of the ratio of the -3dB frequency width to the
Voltage, sound pressure, etc. are often expressed in dB
(decibels). The dB notation uses the common logarithms (log10).
We learned the following logarithm rules in high school math
and the following logarithmic operation rules:
dB (decibel) is defied as
where P0 is the reference level with which a
voltage of 1V and a sound pressure of 20μPa (Pascal) are used.
dB expresses the ratio of square or power. If P is the same as
the reference level, P2/P02=1
or 0dB because log101=0. With 0dB=1 (P0=1)
in mind, let us calculate 10 times in dB.
10 times corresponds to 20dB. Since it is troublesome to
square the ratio each time, 20log is made using a previous
logarithmic operation rule.
This eliminates the need to square the ratio resulting in
simple calculation. This notation may not be familiar in
mathematics. When the antilogarithm (the number subject to
logarithmic operation, a and b above) is decupled or multiplied
by 10, the result is 20dB (because 10=101). When the
antilogarithm is multiplied by 1/10, the result is -20dB
Energy Spectrum Density
The finite impulse energy (by striking method, etc.),
standardized in terms of energy.
The energy spectrum density
is calculated by multiplying the power spectrum density by the
sampling time (window length, T=1/⊿f).
When time function x (t) defined over variable range -T/2 <=
t <= T/2 is represented by expression (2) using the Fourier
coefficient in expression (1), the function x (t) can be
recognized as a periodic function which repeats the same
waveform outside the variable range as the one inside it
according to the periodicity of the complex exponential
function. Therefore, the periodic function x (t) which repeats
the same waveform with a period of T is represented by the
In order to obtain the limit of expressions (1) and (2) when
the time section length (T) is an infinite, which defines the
time function x (t), the following transformation (expression
(3)) is applied to the two expressions. In this case, the
summation in expression (2) is replaced with integral, resulting
in a transformation pair given by expressions (4) and (5).
Expression (4) denotes the Fourier transform of x (t) and
expression (5) the inverse Fourier transform of X (f). This pair
in expressions is referred to as the Fourier transform pair or
Fourier integral pair. X (f), a function of frequency f, is also
referred to as the complex amplitude. As shown by expressions
(4) and (5), the Fourier transform obtains the corresponding
frequency function from a time function, and the inverse Fourier
transform obtains the corresponding time function from a
The following transformation pair may be used in some cases,
which defines the integral section for the Fourier transform as
a semi-infinite section assuming that the start point of time
function x (t) is the origin of the time axis and that x (t) is
zero in the section with t<0.
When complex function X (f) of the frequency defined by
expression (4) is now represented by expression(8), the
components of frequency f forming time function x (t) are
represented by expression (9) from the definition of expression
That is, the amplitude of the component of frequency f is
equal to 2|X(f) | and its phase is equal to declination ∠X (f)
X (f). Thus, the amplitude and phase of the component of
time function x (t) can be obtained from complex number X (f),
and therefore X (f) is referred to as the frequency spectrum of
The Fourier coefficient obtained by expression (1) has its
value only at frequency n/T and is always zero at other
frequencies, and therefore it is referred to as line spectrum.
On the other hand, X (f) of expression (4) is a continuous
function of the frequency, and therefore it is referred to as
Frequency differential is performed by multiplying the power spectrum and the frequency response function by (jω)n. (j is the imaginary number unit and ω=2πf.)
(Note 1) In the power spectrum, the first-order differential is the square of ω and the second-order differential is the fourth power of ω because they are power values.
(Note 2) Since the frequency response function is a complex arithmetic operation, the imaginary units j are also multiplied.
The integration operation on the frequency axis is performed by dividing by (ω)n for the power spectrum and (jω)n for the frequency response function. (j is an imaginary unit, ω＝2πｆ)
(Note 1) In the power spectrum, the single integration is the square of ω and the double integration is the fourth power of ω, because they are power values.
(Note 2) Since the frequency response function is a complex arithmetic operation, the imaginary units j are also divided.
The frequency resolution is equal to the frequency range divided
by the number of FFT lines (analysis data length HREF_FRAME_LEN/2.56).
At the time of zoom analysis, the frequency resolution is equal
to 1/800 times (stop frequency - start frequency).
Frequency Response Function
The frequency response function denotes the relationship between
the input and the output of an electrical system or structural
vibration transmission system. It is represented by the ratio of
the Fourier spectrum of the input, A (f), to the Fourier
spectrum of the output, B (f).
Therefore, the frequency response function, H(f), can be
This FFT analyzer calculates H (f) by multiplying each of the
denominator and numerator on the right side by A*(f), complex
conjugate of A (f), using the following expression.
The denominator, A (f) x A*(f), is the power spectrum of A (f),
and the numerator, B (f) x A*(f), is the cross spectrum of A (f)
and B (f). Therefore, the frequency response function, H (f),
can be obtained by dividing the cross spectrum of the input and
output by the power spectrum of the input.
The frequency response function can also be estimated by
The transfer function estimated by expression (2) is referred to
as H1 and the one estimated by expression (3) as H2.
output signal b (t) contains much external noise,
random error can be minimized by averaging.
A non-linear system can be linearized by averaging
(approximation with method of least squares) using a
random signal as an input signal.
output signal a (t) contains much external noise,
random error can be minimized by averaging.
If leakage error is assumed at the resonance point,
bias error can be reduced.
When the true frequency response function is Ht(f), if both
input and output contain much noise, the following relationship
results (on the premise that the system is a linear system).
The phase of H1(f),H2(f) is equal to the phase of cross spectrum
The relationship with coherence function HREF_COH is represented
which is the ratio of H1 to H2. When the ratio of the power
spectrum for input and output (transfer characteristic) is |Ha(f)|2,
If you apply logarithm to both sides of expression (6),
Expression (7) indicates that the average of the logarithmic
value of the gain of H1 and H2 is equal to the logarithmic value
of true frequency response function Ha.
The frequency response function can be represented by the gain
and phase characteristics. The gain characteristic indicates how
the amplitude varies when a signal passes through the system.
The X-axis denotes the frequency and the Y-axis decibel based on
20 log10 H (f). The phase characteristic indicates
the phase lead or lag between the input signal and the output
signal. The X-axis denotes the frequency and the Y-axis the
angle in degree or radian