Data processing: measuring – calibrating – or testing – Measurement system – Performance or efficiency evaluation
Reexamination Certificate
2000-04-19
2001-12-18
Hoff, Marc S. (Department: 2857)
Data processing: measuring, calibrating, or testing
Measurement system
Performance or efficiency evaluation
C702S182000, C702S076000, C702S147000, C073S660000, C340S683000
Reexamination Certificate
active
06332116
ABSTRACT:
FIELD OF THE INVENTION
The invention relates generally to signal analysis 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 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 to 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 space in time, to angle-samples, i.e. samples of the physical signal 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.
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 array of coefficients which depend on time and frequency, (c) select first coefficients from the array which correspond to a first subset of one or more order components of interest in the input signal, (d) generate a time domain signal from the first coefficients, e.g., by performing an inverse joint time-frequency transform on the first 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 invertible joint time-frequency transform operates to transform the samples of the input signal to produce an array of coefficients which depend on time and frequency. The joint time-frequency transform is invertible, meaning that an inverse transform may be applied to the array of time-frequency coefficients to reproduce the original (or approximately the original) time domain input signal. The invertible joint time-frequency transform is preferably the Gabor transform, but may instead be a wavelet transform, or the Gabor spectrogram.
The input signal may comprise a plurality of order components. According to various embodiments of the invention, various order components of the input signal may be selectively extracted (or removed) from the array of time-frequency coefficients (the joint time-frequency representation), and then the inverse joint time-frequency transform may be applied to produce a time domain signal containing only the selected order components (or produce the original input signal minus the removed order components).
The first subset of order components which the user desires to analyze may be selected in order to select the first coefficients from the array. For example, the user may directly select the one or more order components which the user desires to analyze, and the coefficients corresponding to these selected order components may then be used to generate the time domain signal. Alternatively, a second subset of order components in the input signal may be selected for masking or removal, wherein the remaining order components are the first subset of order components desired to be analyzed by the user. Thus the first subset of order components may correspond to order components of the input signal which are not included in the second subset of one or more order components. Thus the first subset of order components may be “selected” by selecting non-members of this first subset for masking or removal.
Various methods may be used in selecting the order components of interest. For example, the signal analysis system may display a visual representation of the array of coefficients, wherein the various order components are visible in the visual representation as time-frequency curves. The user may select one or more points in the visual representation to select one or more order components. For example, the user may position a “cross-hairs” on the selected order components in the visual representation. The signal analysis system may then determine one or more time-frequency curves corresponding to the selected points, wherein the determined time-frequency curves correspond to the selected order components. The signal analysis system may select the first coefficients of the array as those coefficients which re
Qian Shie
Shao Hui
Conley Rose & Tayon PC
Hoff Marc S.
Hood Jeffrey C.
National Instruments Corporation
Tsai Carol S.
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