Data processing: measuring – calibrating – or testing – Measurement system – Measured signal processing
Reexamination Certificate
1998-07-01
2001-03-27
Heckler, Thomas M. (Department: 2787)
Data processing: measuring, calibrating, or testing
Measurement system
Measured signal processing
C700S108000, C702S182000
Reexamination Certificate
active
06208949
ABSTRACT:
FIELD OF THE INVENTION
This invention relates to the monitoring, testing and control of dynamical systems.
INTRODUCTION AND BACKGROUND ART
In dynamical systems, at least one property of the system varies with time in response to external disturbances. Examples include acoustical systems where the fluid pressure or velocity varies, mechanical systems where stresses or displacement varies, electrical system where the voltage or current varies and optical systems where the intensity of light varies. The analysis of dynamical systems is important in a great many areas, including monitoring, testing and control. There are usually two primary objectives (1) characterization of the dynamic disturbance of the system and (2) characterization of a dynamic response model which predicts how the system will respond to external disturbances. For example, in a control system, the disturbance must be characterized to determine if control action is required and the dynamic response model must be known in order to determine the appropriate control signal to apply.
Disturbance Characterization
Disturbances can be classified as being either broadband or narrowband. An example of broadband noise is wind noise heard in an automobile cabin. Examples of narrowband noise include the hum produced by a power transformer or the repetitive vibration of a rotating machine. Stationary, broadband signals, such as that which results from a recording of the noise from an air-conditioning vent, are usually characterized mathematically by a power spectrum, such as obtained by a third-octave spectral analysis, while transient broadband signals, such as impacts, may be characterized by time-frequency analysis, such as the short-term Fourier transform and the spectrogram or a wavelet transform.
This invention relates to the analysis of transient, broadband and narrowband disturbances.
Narrowband disturbances are so called because the majority of the power in the disturbance is concentrated in narrow frequency bands. The position of the frequency bands is determined by the external source of the disturbance and can therefore change when the source changes. Narrowband disturbances are often characterized by order analysis. In order analysis, the power of the disturbance in each of the narrow frequency bands is estimated, this contrasts to Fourier analysis in which the frequency bands are fixed and are not related to the source. Order analysis is used in many areas, for example: noise and vibration analysis, condition based monitoring of rotating machines, active noise and vibration control, higher harmonic control, machinery balancing and alignment. Order analysis systems typically use a synchronization signal. The analysis is performed either by a bank of tracking filters, which separate the signal into narrow frequency bands and then compute the power in each band, or by synchronous sampling in which the sampling rate is varied so that the frequencies of a discrete Fourier transform coincide with the frequencies of the source. Tracking filters have a major disadvantage in that there is a fundamental trade-off between the bandwidth of the filter (which should be narrow to reject noise and nearby tonal components) and the ability to track changing signals (which requires a broader filter to reduce delay). An example of such a system is shown in
FIG. 1. A
sensor
2
is used to sense the disturbance of a dynamic system
1
and produce a signal
3
. A synchronizing signal
100
, derived from a tachometer for example, is indicative of the frequency or phase of the system. The synchronizing signal is passed to tone generators
101
,
101
′,
101
″ which each generate complex (in-phase and quadrature) signals at one of the harmonics of the fundamental frequency of the disturbance. The signal
3
is multiplied at
102
,
102
′,
102
″ by each of the complex signals and passed through low pass filters
103
,
103
′,
103
″ (which may be integrators) to produce estimates of the complex amplitudes at each harmonic frequency, as indicated
104
,
104
′,
104
″. These estimated signals provide an indication of the amplitude of the disturbance at the harmonic frequency. This process is known as heterodyning. Synchronous sampling techniques are better at separating the harmonic components, but require more expensive electronic hardware to perform the synchronous sampling and cannot be used simultaneously for broadband analysis. The use of tracking filters in a system for active control is described in U.S. Pat. No. 5,469,087 (Eatwell).
Neither system is very effective when multiple disturbance sources at different frequencies are present. In ‘Multi Axle Order Tracking with the Vold-Kalman Tracking filter’, H. Vold et al, Sound & Vibration Magazine, May 1997, pp30-34, a system for tracking multiple sources is described. This system estimates the complex envelopes of the signal components subject to the constraint that the envelopes can be represented locally by low-order polynomials. The resulting process is not well suited to implementation in ‘real-time’, and would result in considerable processing delay because of the nature of the constraints. This makes it unsuitable for application to real-time control systems. In addition, the process is numerically intensive. The measurement of disturbances experienced by rotating or reciprocating machinery often requires the use of multiple sensors. The data from these sensors is transmitted to a computer system for processing and analysis. The combination of multiple sensors and moderate frequency bandwidths will results in high data transfer rates. Considerable benefit would result if the data could be compressed before transmission or storage.
Characterization of Dynamic Response Model
System identification is the process of building a mathematical model of a dynamical system based on measurements of response of the system to known disturbances. This is usually done by applying the known disturbances to a mathematical model and then adjusting the parameters of the model until the output of the model is as close as possible to the measured output from the real system. This model is referred to as the system model or the dynamic response model.
System identification is a central part of modal analysis and control systems. In modal analysis, the dynamic response model of the system is parametrized by the frequency, damping and shape of a number of resonant modes. In order to conduct a modal analysis of a system it is usually necessary to cease the operation of the system. This means that modal analysis cannot be part of an on-line condition monitoring system.
System identification is also a central part of an adaptive control system, such as used for active noise and vibration control. In most control systems, a mathematical model of the physical system is assumed to be known from prior measurements or from numerical or analytical modelling. Once this information is known, the state of the system (usually current and prior conditions) can be estimated using known techniques. For example, if the statistics of the disturbance are known, an optimal ‘observer’ may be used to estimate the current system state.
An example of a control system with on-line system identification is shown in FIG.
2
. Test signal
4
is added at
75
to the output
74
of control system
114
to produce an actuator drive signal
76
. The actuator
77
excites the dynamic system
1
. The response of the dynamic system is measured by sensor
2
to produce sensor signal
3
. The component of the sensor signal that is due to the test signal is estimated by passing the test signal
4
through adaptive filter
110
to produce an estimated response signal
111
. This is subtracted from the sensor signal at
112
to give error signal
113
, which is in turn used to adapt the coefficients of the filter
110
. The control system
114
is responsive to the sensor signal
3
and, optionally, a reference signal
4
. The system model is provided by the filter
110
. Examples of s
Adaptive Audio, Inc.
Angeli Michael de
Heckler Thomas M.
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