Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Mechanical measurement system
Reexamination Certificate
2001-10-09
2002-11-05
Hoff, Marc S. (Department: 2857)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Mechanical measurement system
C702S010000, C702S033000, C702S036000, C702S044000, C702S054000, C702S056000
Reexamination Certificate
active
06477472
ABSTRACT:
FIELD OF THE INVENTION
The invention relates generally to signal analysis systems or test and measurement systems, and more particularly to a system and method for analyzing order components of a signal generated by a physical system (e.g., a mechanical system containing one or more rotating elements).
DESCRIPTION OF THE RELATED ART
Scientists and engineers often use test and measurement systems and data acquisition systems to perform a variety of functions, including laboratory research, process monitoring and control, data logging, analytical chemistry, test and analysis of physical phenomena and analysis or control of mechanical or electrical machinery, to name a few examples. One example of hardware to implement such measuring systems is a computer-based measurement system or data acquisition (DAQ) system. Another example of a measurement system is a dedicated instrument, such as a dedicated oscilloscope or signal analyzer.
A measurement system typically may include transducers for measuring and/or providing electrical signals, signal conditioning hardware which may perform amplification, isolation and/or filtering, and measurement or DAQ hardware for receiving digital and analog signals and providing them to a processing system, such as a processor or personal computer. The computer-based measurement system or dedicated instrument may further include analysis hardware and software for analyzing and appropriately displaying the measured data.
One example where measurement and data acquisition systems are used is in the field of rotating machinery analysis. This involves the analysis of physical signals such as vibration or acoustic signals from a rotating machine. A physical signal acquired from a rotating machine may be sampled or digitized. Typically, samples of the physical signal are equidistant in time. However, rotating machines generate signals which are periodic with respect to shaft rotation, i.e., rotation angle of an underlying rotating element (e.g. a crank shaft of an engine). These rotation-periodic signals are referred to herein as order components. When the rotation rate changes in time, the order components change correspondingly in frequency. For example, when the rotation rate increases, the order components increase in frequency. Thus, a traditional analysis method such as the Discrete Fourier Transform (DFT), when applied to the physical signal, displays a frequency smearing of order components. The frequency smearing makes it very difficult to derive meaningful information about the order components. Thus, traditional signal analysis methods such as the Fourier Transform of the time domain input signal are not well suited for analyzing order components generated by rotating machines.
In order to better analyze the performance and characteristics of rotating machines, certain prior art systems convert the time-samples, i.e., the samples of the physical signal which are equally spaced in time, to angle-samples, i.e. samples which are equally spaced in shaft angle. For example, U.S. Pat. No. 4,912,661 assigned to Hewlett-Packard discloses an interpolation method for estimating angle-samples from time-samples. The method disclosed in U.S. Pat. No. 4,912,661 performs an interpolation of the time domain signal, followed by a decimation, in order to produce samples equally spaced with respect to shaft angle. The order components may then be analyzed by performing a traditional analysis method such as the Discrete Fourier Transform on the angle-samples. However, this method is expensive in terms of computational resources and may not be very accurate.
One prior art system known as the Vold-Kalman filter, developed by Bruel and Kjaer, allows the user to track the frequency of an order component given a sufficiently accurate model, i.e., a stochastic model, for the physical signal. The Vold-Kalman filter performance may be strongly sensitive to model accuracy. In other words, the tracking performance is likely to be degraded when an inaccurate signal model is supplied to the filter. Furthermore, the Vold-Kalman filter provides no mechanism for the user to evaluate the accuracy of the frequency tracking for an order component.
Therefore, there exists a need for a system and method which could more accurately and robustly analyze order components of a physical signal, and reconstruct desired order components in the time-domain.
SUMMARY OF THE INVENTION
One embodiment of the present invention comprises a signal analysis system (or measurement system) and method for analyzing an input signal acquired from a physical or mechanical system. The mechanical system may include at least one rotating apparatus. The signal analysis system may be configured to: (a) receive samples of the input signal, (b) perform an invertible joint time-frequency transform (e.g., a Gabor transform) on the samples of the input signal to produce an initial array of coefficients which depend on time and frequency, (c) generate a modified array of coefficients from the initial array of coefficients, (d) generate a time domain signal from the modified array of coefficients, e.g., by performing an inverse joint time-frequency transform on the modified array of coefficients, and (e) present the time domain signal to a user on a presentation device. The input signal is preferably a time domain input signal, i.e., the samples are sampled in time, preferably uniformly in time.
The joint time-frequency transform is invertible, meaning that any time-domain input signal (or an approximation thereto) may recovered from its transform array by applying an inverse transform to the transform array. The invertible joint time-frequency transform is preferably the Gabor transform, but may instead be a wavelet transform, or the Gabor spectrogram.
The operation (c) of generating a modified array of coefficients may comprise (1) determining (i.e. selecting) a subset of coefficient positions (i.e. time-frequency index positions) from the initial array which correspond to a desired subset of one or more order components in the input signal, and (2) setting coefficients of the modified array equal to zero at coefficient positions other than on the determined subset. The coefficients of the modified array at the subset of coefficient positions may be set equal to the corresponding coefficients of the initial array.
The input signal may comprise a plurality of order components. According to one embodiment of the present invention, various order components of the input signal may be selectively extracted (or removed) from the input signal by appropriate selection of the subset of coefficient positions from the initial array of time-frequency coefficients (i.e. the joint time-frequency representation). The inverse joint time-frequency transform may be applied to the modified array of coefficients to produce a time domain signal containing only the selected order components (or the original input signal minus the selected order components).
The subset of order components which the user desires to analyze (i.e. desires to extract from the input signal for presentation to the user) may be selected in a direct or indirect fashion. For example, the user may directly select the desired subset of one or more order components. In this case, the subset of coefficient positions corresponds to this directly-selected subset of order components. Alternatively, the user may select a second subset of one or more order components for removal from the input signal. In this case, the desired subset of order components is the complement of the second subset. The subset of coefficient positions still corresponds to the desired subset of order components. However, the desired subset has been indirectly selected by selecting the second subset of order components for removal.
Various methods may be used to select the order components of interest. For example, the signal analysis system may display a visual representation of the initial array of coefficients, where the various order components are visible in the visual representation as time-f
Jin Wei
Qian Shie
Shao Hui
Conley Rose & Tayon
Hoff Marc S.
Hood Jeffrey C.
National Instruments Corporation
Tsai Carol S.
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