Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Mechanical measurement system
Reexamination Certificate
1998-08-17
2001-03-06
Hoff, Marc S. (Department: 2857)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Mechanical measurement system
C702S033000, C702S041000, C702S066000, C702S109000, C702S113000, C073S862193, C073S862381
Reexamination Certificate
active
06199019
ABSTRACT:
BACKGROUND OF THE INVENTION
The present invention relates to a unsteady signal analyzer for analyzing an unsteady signal generated by a monitored object, such as a mechanical system, a process or the like and, more specifically, to an unsteady signal analyzer for analyzing an unsteady signal generated by an elevator, and a recording medium storing an analysis program for analyzing an unsteady signal generated by a monitored object using of a computer.
DISCUSSION OF THE BACKGROUND
There have been proposed various diagnostic systems which measure a signal generated by a monitored object, such as a mechanical system, a process or the like, by a measuring instrument, analyze data represented by a measured signal to detect a defect in the monitored object, and, if any defect is detected, warn the operator or the user of the defect.
Generally, most of these known diagnostic systems convert the data represented by the signal generated by the monitored object into a spectrum by Fourier transform and monitor the spectrum or estimate a characteristic model by a system identification method on the basis of data given to and provided by the monitored object. However, it has been impossible to obtain a spectrum through the Fourier transform of an unsteady signal representing data measured while the monitored object is in an unsteady state in which the operating state of the monitored object varies sharply or to determine a characteristic model through system identification.
A wavelet analytical method which uses wavelet transform has attracted attention as a method of detecting a defect in a monitored object through the analysis of an unsteady signal. The wavelet analytical method will be explained hereinafter.
Fourier transform of a signal x(t) provided by a monitored object is expressed by:
F
(
jω
)
=
∫
-
∞
∞
x
(
t
)
ⅇ
-
j
ω
t
ⅆ
t
(
1
)
Wavelet transform of the same signal x(t) is expressed by:
wt
(
a
,
b
)
=
∫
-
∞
∞
1
&LeftBracketingBar;
a
&RightBracketingBar;
φ
(
t
-
b
a
)
x
(
t
)
ⅆ
t
(
2
)
where ¢(·) is a basis function called mother wavelet for transform. Fourier transform is equivalent to wavelet transform in which the basis function &phgr;(t)=e
−jt
, b=0 and a=&ohgr;
−1
and the basis function is a function of time continuous from a past point of infinity to a future point of infinity as shown in
FIG. 2
a
. Therefore, a spectrum obtained through Fourier transform is a function of one variable, i.e., frequency, as shown in
FIG. 2
b
by way of example and it is impossible to determine time dependence of the spectrum, i.e., the feature of which part of observed data is represented by the spectrum. In this article, wavelet transform uses a Gabor function expressed by:
&phgr;(
t
)=
e
−(t/T)
2
e
−jt
(3)
as a basis function, which is localized with respect to time as shown in
FIG. 3
a
. Thus, the spectrum obtained by wavelet transform is a function of two variables, i.e., frequency and time. The time-dependence of the frequency components of the signal can be determined on the basis of a function of two variables as shown in
FIG. 3
b
by way of example.
As mentioned above, wavelet transform is able to extract the spectral distribution of observed data at every moment. Therefore, wavelet transform is considered to be as an effective means for analyzing an unsteady signal representing the operating condition of a monitored object varying with time.
The foregoing conventional diagnostic system, however, simply subjects the unsteady signal generated by the monitored object to wavelet transform, and hence the result of analysis indicates only the time-dependence of the frequency spectrum. Therefore, the conventional diagnostic system is an imperfect analytical technique for diagnosing the state of the monitored object.
For example, it is impossible to understand how the result of analysis made by the conventional diagnostic system, as shown in
FIG. 3
b
, is related with the variation of the state of the monitored object.
SUMMARY OF THE INVENTION
With these problems in mind, therefore, it is the object of the present invention to provide an unsteady signal analyzer which can diagnose an unsteady state of a monitored object accurately by analyzing an unsteady signal generated by the monitored object.
An unsteady signal analyzer according to a first aspect of the present invention for analyzing an unsteady signal generated by a monitored object comprises: wavelet transform calculating means for producing a wavelet spectrum data through a wavelet transform of the unsteady signal; state variation function setting means for setting a state variation function representing a variation of a specific state variable of the monitored object with time; and time coordinate nonlinear transformation means for transforming a time coordinate of the wavelet spectrum data nonlinearly into a coordinate of the specific state variable by using an inverse function of the state variation function set by the state variation function setting means.
An unsteady signal analyzer according to a second aspect of the present invention for analyzing an unsteady signal representing an acceleration of a cab of an elevator as a monitored object comprises: wavelet transform calculating means for producing a wavelet spectrum data through a wavelet transform of the unsteady signal representing a measured acceleration of the cab; state variation function setting means for setting a state variation function representing a variation of vertical position or vertical speed as a state variable of the cab with time; and time coordinate nonlinear transformation means for transforming a time coordinate of the wavelet spectrum data nonlinearly into a coordinate of the vertical position or the vertical speed by using an inverse function of the state variation function set by the state variation function setting means.
In the unsteady signal analyzer according to the first or the second aspect of the present invention, the time coordinate nonlinear transformation means transforms the time coordinate of the wavelet spectrum data nonlinearly into the coordinate of the specific state variable by using the following expression:
wt
(
a
,
b
)
=
∫
z
(
-
∞
)
z
(
∞
)
1
&LeftBracketingBar;
a
&RightBracketingBar;
φ
(
t
(
z
-
b
)
a
)
x
(
t
(
z
)
)
ⅆ
t
(
z
)
ⅆ
z
ⅆ
z
which is expressing an extended wavelet transform.
In any one of the foregoing unsteady signal analyzers according to the present invention, the time coordinate nonlinear transformation means divides the wavelet spectrum data with respect to time into data segments, rearranges the data segments in order of magnitude of the state variable on the basis of a data table showing the relation between time and the state variable, or the state variation function, and estimates intermediate values of the data segments by interpolation and smoothing techniques, so as to transform the time coordinate of the wavelet spectrum data nonlinearly into the coordinate of the specific state variable.
Any one of the foregoing unsteady signal analyzers according to present invention further comprises a response data measuring means for measuring the unsteady signal.
In any one of the foregoing unsteady signal analyzers according to the present invention, the state variation function setting means may estimate the state variation function on the basis of measured data on a state variable of the monitored object other than the specific state variable.
The measured data on the state variable of the monitored object other than the specific state variable may be measured data on the unsteady signal.
In any one of the foregoing unsteady signal analyzers according to the present invention, the state variation function setting means may estimate the state variation fun
Iino Yutaka
Yukitomo Masanori
Hoff Marc S.
Kabushiki Kaisha Toshiba
Oblon & Spivak, McClelland, Maier & Neustadt P.C.
Vo Hien
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