WO1994022025A1

WO1994022025A1 - Plant parameter detection by monitoring of power spectral densities

Info

Publication number: WO1994022025A1
Application number: PCT/US1994/003067
Authority: WO
Inventors: Raymond Donald Bartusiak; Douglas Hugh Nicholson
Original assignee: Exxon Chemical Patents Inc.
Priority date: 1993-03-22
Filing date: 1994-03-22
Publication date: 1994-09-29
Also published as: AU6413394A

Abstract

A method for determining when a process variable output signal is in a steady state or in a non-steady state condition, wherein the signal detection technique is dependent upon power spectral densities. Reference data is established by first sampling a plant's process variable output signal when the plant is operating at steady state. The sampled output signal is then analyzed to derive the reference data that includes an energy content of each of a plurality of its frequency components. A current operational data base is then established by sampling a process variable signal when the plant is in operation. The sampled current output signal is analyzed to derive current data that includes an energy content of each of a plurality of its frequency components. For each common frequency component of the reference data and the current data, the system makes a comparison of the energy contents and issues a non-steady state signal if the comparison indicates that the compared energy contents exceed a predetermined limit.

Description

PLANT PARAMETER DETECTION BY MONITORING OF POWER

SPECTRAL DENSITIES

FIELD OF THE INVENTION

This invention relates to process control systems, and more particularly, to a system for determining whether a process parameter is in a steady or unsteady state through a use of the parameter's power spectral density.

BACKGROUND OF THE INVENTION

In controlling dynamic plant process, it is often necessary to know whether process variables are in a steady state or in an unsteady state. While, many process control systems monitor plant variables and compare them against predetermined set points, it is often more important to know whether deviations of a plant variable, over time, away from a set point are significantly different from normal. It is also important to be able to predict whether a plant parameter output exhibits an incipient condition which may lead to an unsteady state.

The prior art contains many teachings concerning plant parameter monitoring for process control applications. In US-A-4,744,041, the steady state speed of a dc motor is detected through the use of fast Fourier transform analysis by employing a current sensor which measures the current in a test motor and sends the sampled current signal as data to a computer. The computer then samples and stores measured instantaneous current values at a plurality of discrete times and performs a fast Fourier transform on the steady state current to determine its power spectral density. Based on the computed power spectral density, the speed of the motor is determined by detecting the frequency at which maximum power is used.

US-A-4,303,979, discloses a system for monitoring frequency spectrum variations in output signals. The system initially determines a root mean square (RMS) average frequency value for an input signal. In addition, it determines RMS values for each of a plurality of frequency subranges within the input signal. The system then monitors an input test signal and obtains its RMS value and the average frequency of the overall input signal. If the determined reference and test frequencies differ substantially in their RMS and average frequency values, the RMS value and average frequency values, the RMS value and average frequency of an anomalous frequency component peak is calculated. The average of the anomalous frequency component is then compared to boundary frequency values to determine in which frequency range the anomalous value lies. The RMS value of the thus determined frequency range and the average frequency are then employed to determine correction parameters. US-A-4,965,757 discloses a process and device for decoding a received, encoded signal. The received signal is first filtered, sampled and digitized before being stored. Digitized samples of each successive signal block are transposed to the frequency domain by a fast Fourier transform. The thus computed spectra are compared with stored theoretical values for each possible code signal in order to identify the received encoded signal.

US-A-3,883,726 discloses a fast Fourier transform algorithm computer that utilizes an attenuated input data window. An input buffer receives input time samples, a cosine square attenuator superimposes a cosine squared shape on the input data samples, and then the resultant signal is passed to the fast Fourier analysis computer. A delay device, adders and an output buffer are provided for removing the effect of the attenuating input data window.

US-A-4,975,633 discloses a spectrum analyzer that displays spectrum data and power values of a radio frequency (RF) or optical signal. An input signal is directed down one path where it is subjected to a spectrum analysis, and down a second path where its power value is determined. Display means indicates both the spectrum data and the power value.

US-A-5,087,873 discloses apparatus for detecting a corrosion state of buried metallic pipe. In Figs. 4-7, a spectrum analyzer receives input signals from a pair of magnetometers. The magnetic field, as a function of frequency, is determined by conducting a fast Fourier transform on the received signals, with the resulting spectrum indicating the amplitude and phase of the magnetic field. Those values are used to determine the condition of the buried pipe.

US-A-4,824,016 discloses an acoustical process for monitoring the operating state of a feed nozzle injecting a mixture of liquid and gas into a process vessel. The system initially determines a reference power spectrum for the nozzle when it is performing in a standard condition. Then, a second determination is made at a later time, a current power spectrum, when it is suspected that performance has deteriorated. Based on the difference between the reference and current power spectras action may be taken, which may include adjustment of the flow rates to the nozzle, so that the nozzle operates under the original reference power spectra. The analysis methods for examination of the power spectra include a human observer, a simple computer pattern recognition algorithm, or time variation of the signal. US-A-5,201,292 describes a system for detecting vibration patterns and indicates the derivation of spectral components and energy levels. Its spectral components are combined into a single value (by auto correlation) and are not individually utilized (see col. 3, lines 55-61). The single value is compared with immediately preceding threshold spectra which has been dynamically adapted in accordance with changed system conditions. (See col. 5, lines 33-42). Substantial lengths are taken to adapt the reference threshold to take into account the most recent changes in the monitored plant data.

Nowhere in the prior art is there any teaching of the analysis of the individual frequency components of a reference power spectra to determine steady state energies and a subsequent storage thereof for later use as comparison data. Furthermore, there is no description in the prior art of the analysis of a power spectra and the comparison thereof, on a frequency-by- frequency basis, with stored reference data.

SUMMARY OF THE INVENTION

According to the present invention, there is provided a method for determining a current state of a plant process variable output signal and whether the output signal is within acceptable limits, the method comprising the steps of:

(a) establishing reference steady state data for a process variable by sampling its output signal over a period of time when the plant is operating at steady state; (b) analyzing the sampled output signal to derive reference data therefrom, including an energy content of each of a plurality of frequency components of the sampled output signal;

(c) storing the derived reference data;

(d) establishing current operational data for that same process variable by sampling a current output signal for the process variable over a period of time when the plant is in operation;

(e) analyzing the sampled current output signal to derive current data therefrom, including an energy content of each of a plurality of frequency components of the sampled current output signal; and

(f) for each common frequency component of the reference data and current data, comparing derived energy content threshold therefor and issuing a signal if the comparing indicates that an energy content of any frequency component of the current data exceeds an energy content threshold of a common frequency component of the reference data, the threshold having a greater value than energy content of the common frequency component of the reference data.

Preferably, step (f) further indicates the common frequency for which the signal is issued. In addition, or in the alternative, the signal in step (f) is issued after a ratio of energies of each common frequency is determined and it is determined that at least one ratio exceeds a predetermined factor. Steps (b) and/or (e) may perform the method as recited above using

Fourier analyses of the sampled output signals to determine the frequency components and energy content of each thereof.

The method of the invention may be performed in plants which utilize process control systems, such as in refineries or petrochemical plants. DESCRIPTION OF THE DRAWINGS

Fig. 1 is a block diagram of a system for performing the method of the invention; Figs. 2a and 2b are high level flow diagrams illustrating the method of the invention;

Fig. 3 is a plot of a variation in flow over time in an exemplary plant;

Fig. 4 is a semi-log plot of energy versus frequency derived from the signal shown in Fig. 3; Fig. 5 is a plot of flow vs. time showing a negative-going ramp disturbance;

Fig. 6 is a semi-log plot of energy vs. frequency of the plot of Fig. 5 indicating [the] that the ramp disturbance produces the most severe violation of plotted energy thresholds in the low frequency range; Fig. 7 is a plot of flow vs. time showing a "U" type disturbance in the flow signal;

Fig. 8 is a semi-log plot of energy vs. frequency of the plot of Fig. 7 indicating, at one energy threshold, that a disturbance exceeds the threshold.

DETAILED DESCRIPTION OF THE INVENTION

The system and method for carrying out the invention determine whether a plant process is operating within a steady state or a non-steady state condition. In brief, the system determines a reference power spectral density (PSD) of a process variable, during a time that the process variable is in a steady state. The reference PSD is then compared with a current PSD derived when the process variable is in operation. A power spectral density is a representation of an energy content of a signal as a function of its component frequencies and is computed via a fast Fourier transform (FFT) which converts a time-domain signal into its frequency-domain representation. If the current PSD is too energetic (either with respect to slow changes like ramps or with respect to fast changes like spikes) relative to the reference PSD, then the process variable is determined to be unsteady and such condition is signaled. Turning to Fig. 1, flow monitors 10 and 12 continuously monitor the state of flow in a pair of pipes 14 and 16, respectively. Pipes 14 and 16 form portions of a plant whose process variables are continuously monitored to determine if any one has moved from a steady state to an unsteady state. Those skilled in the art will realize that the representation of inputs from pipes 14 and 16 is merely exemplary and that a plurality of other types of system variables (e.g. pressure, volume, temperature, etc.) can be monitored. The outputs from each of flow monitors 10 and 12 are fed to respectively connected analog to digital converters (A/D) 18, 20 whose outputs are, in turn, connected to a bus 22 that forms the main communication pathway in a control data processing system.

A central processing unit (CPU) 24 is interconnected with bus 22 and also is connected to A/D converters 18 and 20 to enable timed, sample signals to be derived therefrom. A read only memory (ROM) 24 is connected to bus 22 and contains a procedure for operating CPU 24 to monitor sensors 10, 12 and a procedure for enabling CPU 24 to perform a fast Fourier transform (FFT) of input data received from A/D converters 18 and 20. A random access memory (RAM) 26 is connected to bus 22 and contains allocated memory for storing: raw input data from A/D converters 18 and 20; reference PSD data determined during a time that a process variable being monitored is in a steady state; and current PSD data that is determined when the process variable is being currently monitored.

The operation of the system of Fig. 1 will now be described in conjunction with the flow diagram shown in Figs. 2a and 2b. Initially, a steady state (i.e. reference) PSD is determined for a monitored process variable. The steady state PSD is derived by initially sampling a process variable output signal when the plant is operating in a steady state condition (box 30). To avoid contamination of the steady state PSD, the output signal is filtered to remove any aperiodic signals therefrom (box 32). The filtered, periodic signal is then subjected to an FFT analysis by central processor 24, under control of a procedure read out from ROM 24 (box 34). The result of the PSD analysis is a series of frequency values (ωj) for the filtered steady state signal, each frequency value (ωj) having an attribute associated therewith that is equivalent to the energy contained in the particular frequency signal. Those frequencies and energy value attributes are stored in RAM 26 as steady state (or reference) PSD data. At this point, a user-supplied multiplier factor is accessed that is to be applied to each energy value attribute of the steady state PSD. The multiplier factor enables the derivation of an energy threshold which is the amount that a process variable's current PSD must exceed a reference PSD to be considered in an unsteady state (box 36).

The system of Fig. 1 now switches to a "current" monitor state wherein it monitors the outputs of sensors 10, 12, etc. During the time that any one sensor is monitored, its output is converted by an FFT procedure to a PSD representation which is then compared with the previously derived steady state PSD for the same sensor (i.e. is stored in RAM 26). CPU 24 commences the current monitor state by sampling output signals from a sensor (e.g. 10) during operation of the plant (Box 38). After sufficient samples have been accumulated, CPU 24 filters the sampled current output signal to remove aperiodic components (box 39), and then computes a PSD (ωj) for the sampled current output signal using an FFT procedure (box 40). It is now determined whether any determined current PSD (ωj) is greater than its corresponding steady state PSD (ωj), multiplied by the user- inputted multiplier factor (boxes 42, 44). If yes, the system outputs the value of (ωj) at which the maximum energy ratio is found and a signal is issued that the process variable is in a non steady state condition (box 46).

If the decision indicated in decision box 44 is that no current PSD (ωj) exceeds the multiplier factor times the steady state PSD (ωj), then it is determined that the sampled current output signal is in a steady state condition and no further action is required. At this point, the procedure terminates and CPU 24 may then monitor another current process variable signal and the procedure is repeated.

Figs. 3-8 contain examples of the procedure described above. In Fig. 3, a process variable (flow) is plotted over a period of several hours (126 minutes). Each dot on the plot trace evidences a sampled flow value that is input to the system. Prior to monitoring the current flow values, a reference flow during steady state of the same process variable was monitored and a reference PSD was derived (e.g. dotted trace 100 in Fig. 4). Reference PSD trace 100 has been quantized into ten "bins" so as to enable averaged comparisons to be made. The PSD for the signal of Fig. 3 is then calculated (trace 102) and is seen to exhibit considerable variations in power over the frequency spectrum. Note that no portion of current PSD trace 102 exceeds reference PSD trace 100, so it is assumed that the system flow signal indicated in Fig. 3 is within the steady state.

Turning to Fig. 5, a ramp disturbance 103 is plotted (flow vs. time). Furthermore, for exemplary purposes only, it is assumed that two multiplier factors (e.g. 1.0 and 1.5) have been utilized to derive energy PSD thresholds 108, 110 respectively. The conversion of ramp signal 103 to a PSD results in energy versus frequency trace 106 shown in Fig. 6. Note that the ramp disturbance 103 produces the most severe violation of both PSD energy thresholds 108 and 110 in the low frequency range. Under such conditions, an alarm is generated. The system may be adjusted such that an alarm in inhibited from issuance if only energy threshold 108 is exceeded, however the frequencies at which the energy exceeds threshold 108 may be output for user monitoring of a possibly incipient instability mode.

In Fig. 7, a "U" disturbance 113 is depicted in a plot of flow versus time. Over the span of the time during which the flow is monitored in Fig. 7, the flow difference is not large and yet it is clear that a disturbance does occur. As shown by Fig. 8, such disturbance is picked up by comparison of the peak corresponding PSD trace 112 with energy threshold 114.

The method of the invention may be performed in plants which utilize process control systems, including but not limited to, refineries and petrochemical plants.

Claims

CLAIMS:

1. A method for determining a current state of a plant process variable output signal and whether the output signal is within acceptable limits, the method comprising the steps of: (a) establishing reference steady state data for a process variable by sampling its output signal over a period of time when the plant is operating at steady state;

(b) analyzing the sampled output signal to derive reference data therefrom, including an energy content of each of a plurality of frequency components of the sampled output signal;

(c) storing the derived reference data;

2. The method as recited in Claim 1, wherein step (f) further indicates the common frequency for which the signal is issued.

3. The method as recited in Claims 1 or 2, wherein analyzing steps (b) and (e) perform Fourier analyses of the sampled output signals to determine the frequency components and energy content of each thereof.

4. The method as recited in any of the preceding claims, wherein in step (f), the signal is issued after a ratio of energies of each common frequency is determined and it is determined that at least one ratio exceeds a predetermined factor.

5. A plant process which is at least partially controlled by the method of any of Claims 1 through 4.

6. The process of Claim 5, wherein the plant process comprises treatment of a hydrocarbon stream.