JP2004191209A - Method and system for diagnosis of abnormality - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a method and a system for diagnosis of abnormalities, which accurately grasps states of a measuring instrument at a low cost, using measurement result signals output from the measuring instrument. <P>SOLUTION: The system for diagnosis of abnormalities 130 is provided with a wavelet transformation section 131, in which the time-series measurement signals output from the measuring instrument 100 are subjected to the wavelet transformation and transformed into wavelet transformation signals, indicative of the relations between time values and frequency values, distributed processing sections 131-1 to 132-n which calculate the first variance value of the wavelet transformed signals being processed, using a prescribed time interval in a first frequency region and a second variance value of the wavelet transformation signals processed using the prescribed time interval in a second frequency region having a period longer than that of the first frequency region, and an abnormality state deciding section 133, which compares the first variance value to the second variance value and decides the state as being abnormal, when the first variance value is larger than the second variance value. <P>COPYRIGHT: (C)2004,JPO&NCIPI

Description

[0001]
TECHNICAL FIELD OF THE INVENTION
The present invention relates to a method and an apparatus for diagnosing an abnormality of a measurement device by using a wavelet transform signal of a measurement signal output from the measurement device.
[0002]
[Prior art]
At a process site such as a petrochemical plant, various measuring instruments such as a liquid level gauge and a flow meter are installed. These measuring instruments not only measure the state of various plants, but also control the target plant by obtaining an operation amount from the measured data, and thus have an extremely important role.
[0003]
When these measuring instruments become abnormal, it is clear that if the abnormal measurement data is used as it is for plant control, the stability and safety of the plant cannot be maintained.
[0004]
In addition, abnormalities in the measuring device include a temporary change in performance due to accumulation of dirt or the like whose function is restored by cleaning, and a change in performance due to actual deterioration of the device, and it is difficult to distinguish between them.
[0005]
Further, by the time the measuring instrument becomes abnormal, a sign of an abnormality, for example, the ground state of the signal output has deviated from a predetermined value, has occurred. is necessary.
[0006]
Therefore, in general, the measurement equipment is monitored by an operator or a monitor, and when it is determined that the measurement equipment is abnormal, the measurement equipment is repaired or replaced to cope with the occurrence of the abnormality of the measurement equipment. However, each of the measuring instruments has a different service life, and even the same instrument has individual differences. In addition, the number of installed instruments is very large. For this reason, it is not desirable in various aspects to constantly monitor and maintain a measurement system for those measuring devices for which it is not known when an abnormality occurs or a failure occurs, such as an increase in manufacturing cost.
[0007]
Therefore, hitherto, attempts have been made to analyze each signal component by performing a Fourier transform on a process signal output from the measuring device, and to detect a failure or deterioration. However, in the data for each frequency component obtained by the Fourier transform, information about time is lost, so the data for each frequency component is inverse Fourier transformed to reproduce and use the information about time. .
[0008]
[Patent Document 1]
JP-A-2000-146636
[0009]
[Problems to be solved by the invention]
However, it was not easy to distinguish and extract a temporary change in the measurement result due to, for example, contamination and a change in the measurement result due to deterioration of the device itself, from the information. In addition, in the method using the Fourier transform or the like, the inverse Fourier transform is necessary as described above, or the inspection is performed for all the frequency bands, so that it takes time to verify one device.
[0010]
If these are to be processed by a computer, for example, a computer with a high processing capability must be used in order to handle all the inspections of the equipment, and such a computer is expensive. However, monitoring the process can be very costly.
[0011]
On the other hand, the method described in Patent Literature 1 has been proposed, but it has been difficult in some cases to diagnose an abnormality with sufficient accuracy depending on a measuring device or a measuring condition.
[0012]
An object of the present invention is to provide an abnormality diagnosis method and apparatus capable of using a signal of a measurement result output from a measurement device and accurately grasping the state of the measurement device at low cost.
[0013]
[Means for Solving the Problems]
An abnormality diagnosis method according to the present invention includes a first step of performing a wavelet transform of a time-series measurement signal output from a measuring device to a wavelet transform signal indicating a relationship between time and frequency, and a first step of a first cycle. A first variance value of the wavelet transform signal in a predetermined time interval in one frequency domain, and a first variance value of the wavelet transform signal in a second frequency domain in a second cycle larger than the first cycle. A second step of obtaining a second variance value, comparing the first variance value with the second variance value, and when the first variance value is larger than the second variance value And a third step of determining an abnormality. With such a configuration, it is possible to determine that a vibration state different from the steady vibration state, which cannot be captured by the time-series signal output from the measuring device as it is, is abnormal.
[0014]
In the abnormality diagnosis method described above, the second cycle in the second frequency domain may be substantially twice as long as the first cycle in the first frequency domain. Thereby, the abnormality of the measuring device can be determined more accurately.
[0015]
In the above-described abnormality diagnosis method, the first cycle of the first frequency domain may be twice as long as a measurement cycle of the measurement device. Thereby, it is possible to perform an abnormality determination suitable for the measurement of the measuring device.
[0016]
The abnormality diagnosis apparatus according to the present invention further includes a wavelet transform unit configured to perform a wavelet transform on the time-series measurement signal output from the measurement device to convert the time-series measurement signal into a wavelet transform signal indicating a relationship between time and frequency. And a second variance value of the wavelet transform signal at a predetermined time interval of the wavelet transform signal in a second frequency domain having a period larger than that of the first frequency domain. And comparing the first variance value with the second variance value, and determining that the first variance value is abnormal when the first variance value becomes larger than the second variance value. A determination unit. With such a configuration, a vibration state different from the steady vibration state, which cannot be detected by the time-series signal output from the measuring device as it is, is determined to be abnormal.
[0017]
In the abnormality diagnosis device described above, the second cycle in the second frequency domain may be approximately twice as long as the first cycle in the first frequency domain. Thereby, the abnormality of the measuring device can be determined more accurately.
[0018]
In the abnormality diagnosis device described above, the first cycle of the first frequency domain may be twice as long as a measurement cycle of the measuring device. Thereby, it is possible to perform an abnormality determination suitable for the measurement of the measuring device.
[0019]
BEST MODE FOR CARRYING OUT THE INVENTION
An abnormality diagnosis device according to an embodiment of the present invention will be described with reference to FIGS.
[0020]
In a petrochemical plant or the like, for example, a floating type liquid level gauge is used in order to grasp the storage amount in a tank for storing crude oil before refining. In a certain tank, an intermediate product flows in at a predetermined flow rate, and flows out at a predetermined flow rate to the next step. Then, the inflow amount and the outflow amount are controlled so that the liquid level of the intermediate product in the tank becomes a predetermined value.
[0021]
The structure of the floating level gauge will be described with reference to FIG. As shown in FIG. 1, the liquid level gauge 100 has a torque tube 102 fixed to a bearing portion 101, and a lever 103 fixed to a tip of the torque tube 102 so as to be rotatable around the torque tube 102. A cylindrical float 104 is fixed to the tip. A rod 105 is fixed to the distal end of the torque tube 102 to which the lever 103 is attached. One end of the rod 105 penetrates through the torque tube 102 and protrudes to the opposite side of the bearing 101.
[0022]
The rotation of the lever 103 about the torque tube 102 is detected at one end of the rod 105 as the rotation of the rod 105. For example, if a strain gauge 106 is attached to the tip of the rod 105, the rotation of the lever 103 about the torque tube 102 is detected as a change in the electrical output of the strain gauge 106. That is, the vertical movement due to the liquid level change of the float 104 is detected as a change in the electric output of the strain gauge 106. Since the float 104 and the strain gauge 106 can be completely separated from each other via the bearing 101, a material having corrosion resistance such as stainless steel is used for the torque tube 102, the lever 103, and the float 104. If used, the liquid level meter 100 can be used for detecting the liquid level even if the liquid is corrosive.
[0023]
Next, an abnormality diagnosis device 130 that diagnoses an abnormality of the liquid level gauge 100 will be described with reference to FIG.
[0024]
As shown in FIG. 2, the liquid level of the intermediate product 111 in the tank 110 is measured by the liquid level meter 100, and the control unit 120 controls the flow rate of the intermediate product 111 into the tank 110 so that the measured liquid level falls within a predetermined range. The amount and the outflow amount from the tank 110 are controlled.
[0025]
For example, the control unit 120 controls the opening of the inflow valve 113 of the inflow pipe 112 through which the intermediate product 111 flows into the tank 110 and the opening of the outflow valve 115 of the outflow pipe 114 through which the intermediate product 111 flows out of the tank 110. The degree is controlled by a signal from the liquid level gauge 100. In the present embodiment, the measurement signal output from the level gauge 100 is diagnosed by the abnormality diagnosis device 130 to detect an abnormality.
[0026]
In the abnormality diagnosis device 130, first, the wavelet transform unit 131 performs a wavelet transform on the input measurement signal that is a time-series signal.
[0027]
Next, the first distributed processing unit 132-1, the second distributed processing unit 132-2, ..., the n-th distributed processing unit 132-n converts the wavelet transform signal obtained by the wavelet transform into n frequency components. The variance value is determined for a predetermined time period, for example, a 24-hour section of the wavelet transform signal in each frequency region.
[0028]
In the present embodiment, the liquid level is measured by the liquid level meter 100 every minute, and a measurement signal is output from the liquid level meter 100 every minute. The first distributed processing unit 132-1 obtains a first variance value W1, which is the variance in a 24-hour section in the first frequency domain having a period of 1 to 2 minutes. A second variance value W2, which is a variance in a 24-hour section in a second frequency domain having a period of 〜 to 4 minutes, is obtained. An nth variance value Wn, which is the variance in a 24 hour section in the region, is obtained.
[0029]
Here, the variance value V indicates the degree of variation of the wavelet transform signal within a predetermined time section (24 hours). For example, when the n variables are x1, x2,..., Xn, the variance value V = (1 / n) Σ (xi−average value) ² = (1 / n) Σxi ² −average value ²
Alternatively, the difference between the maximum value (or its 80% value) and the minimum value (or its 80% value) within a predetermined time section (24 hours) may be obtained, and the difference may be used as the variance value V.
[0030]
The anomaly determination unit 133 is based on the distribution values W1, W2, ..., Wn obtained by the first distribution processing unit 132-1, the second distribution processing unit 132-2, ..., the n-th distribution processing unit 132-n. Determines and detects abnormalities in the measurement signal.
[0031]
The input unit 134 sets the number of times of expansion in the wavelet transform unit 131, and sets the frequency domain in the first distributed processing unit 132-1, the second distributed processing unit 132-2, ..., the n-th distributed processing unit 132-n. The setting is made, or a judgment criterion in the abnormality judgment unit 133 is set.
[0032]
The display unit 135 displays changes in the variance values W1, W2, ..., Wn by the first distribution processing unit 132-1, the second distribution processing unit 132-2, ..., the n-th distribution processing unit 132-n, The determination result by the abnormality determination unit 133 is displayed.
[0033]
Next, in order to determine the determination conditions in the abnormality determination unit 133 according to the present embodiment, the dispersion values W1, W2,. The result is shown in FIG. The average of the dispersion values W1 for the plurality of liquid level gauges 100 is shown by a solid line, and the average of the dispersion values W2 for the plurality of liquid level gauges 100 is shown by a wavy line. The dispersion values W3,..., Wn are not shown. Between the 10th week and the 19th week from the start of the measurement, the torque tubes 102 of the plurality of liquid level gauges 100 were simultaneously replaced.
[0034]
The following points were found from FIG.
(1) The dispersion values W1 and W2, which were large before the torque tube was replaced, are rapidly reduced and stable after the torque tube is replaced.
(2) The variance values W1 and W2 (particularly the variance value W1) have exceeded 0.1 from about five weeks before the torque tube replacement.
(3) As shown in FIG. 4, the magnitude relationship between the variance W1 and the variance W2 is reversed before and after the replacement of the torque tube. That is, the dispersion value W1 <the dispersion value W2 or the dispersion value W1 = the dispersion value W2 before the torque tube replacement, but the dispersion value W1> the dispersion value W2 after the torque tube replacement.
[0035]
This is because when the torque tube 102 of the liquid level gauge 100 is deteriorated or the float 103 is contaminated and an abnormality occurs, it does not change smoothly when the liquid level fluctuates, but changes while finely vibrating. This is presumed to be because the measured value fluctuates in a cycle shorter than the cycle of the actual liquid level fluctuation.
[0036]
As a result, in the present embodiment, the following is adopted as a criterion for the abnormality determination unit 133.
(1) The first dispersion value W1 in the first frequency domain having a period of 1 to 2 minutes by the first distributed processing unit 132-1 and the first dispersion value W1 having a period of 2 to 4 minutes by the second distributed processing unit 132-2. The second variance value W2 in the second frequency domain is compared with the second variance value W2.
First dispersion value W1> second dispersion value W2
Is established, it is determined that an abnormality (for example, deterioration of the torque tube 102) has occurred in the liquid level gauge 100.
(2) The first variance value W1 in the first frequency domain having a period of 1 to 2 minutes by the first variance processing unit 132-1 becomes larger than a predetermined value experimentally determined, for example, 0.1. It is determined that an abnormality (for example, deterioration of the torque tube 102) has occurred in the liquid level gauge 100 when the error occurs.
[0037]
If the abnormality determination unit 133 determines that an abnormality has occurred based on the determination criteria described above, a display indicating that an abnormality has occurred is displayed on the display unit 135 to notify the operator. Thus, the operator arranges for the level gauge 100 to be checked.
[0038]
As described above, according to the present embodiment, the measurement signal output from the liquid level gauge is used to accurately detect the abnormality of the liquid level gauge due to the deterioration of the torque tube or the adhesion of the float to the float. You can understand well.
[0039]
An abnormality diagnosis device according to another embodiment of the present invention will be described with reference to FIG.
[0040]
The abnormality diagnosis apparatus according to the above-described embodiment includes the wavelet transform unit, the distributed processing unit, and the abnormality determination unit. In the present embodiment, these operations are performed by a computer device having a CPU.
[0041]
As shown in FIG. 5, the computer device includes an A / D converter 501 that converts an input measurement signal into a digital signal, a CPU 502 that controls the entire computer device, and a memory 503 that temporarily stores programs, data, and the like. , An external storage device 504, a keyboard 505 for inputting operation signals and the like, and a monitor 506 for displaying information are connected via a bus line 502a.
[0042]
The input time-series signal is converted into a digital signal by the analog / digital conversion unit 501, and the converted signal is processed by the CPU 502. CPU 502 of the computer device performs a wavelet transform of the time-series measurement signal output from the measurement device according to a program stored in memory 503 to convert the signal into a wavelet transform signal indicating a relationship between time and frequency. And a first dispersion value of the wavelet transform signal in the first frequency domain at a predetermined time interval, and a first variance value of the wavelet transform signal in the second frequency domain having a period larger than that of the first frequency domain. A second variance value is calculated, and the first variance value is compared with the second variance value. When the first variance value is larger than the second variance value A third step of determining that the abnormality is abnormal. When an abnormality occurs, the fact is displayed on the monitor 506.
[0043]
The present invention is not limited to the above embodiment, and various modifications are possible. For example, in the above-described embodiment, the liquid level gauge using a float has been described as an example.However, the present invention is not limited to this. Abnormal detection of other various measuring devices, particularly, a measuring device having a mechanically movable part Can be used for detection.
[0044]
Further, the abnormality determination unit is not limited to the above-described determination conditions, and may determine based on other conditions.
[0045]
【The invention's effect】
As described above, according to the present invention, a time-series measurement signal output from a measuring instrument is converted into a wavelet transform signal indicating the relationship between time and frequency by a wavelet transform, and the first frequency of the first cycle is A first variance value of the wavelet transform signal at a predetermined time interval in the domain, and a second variance value of the wavelet transform signal at a predetermined time interval in a second frequency domain of a second cycle larger than the first cycle And the first variance value is compared with the second variance value. When the first variance value becomes larger than the second variance value, it is determined to be abnormal. A vibration state different from the steady vibration state, which cannot be detected by the time series signal as it is, can be determined to be abnormal.
[Brief description of the drawings]
FIG. 1 is a diagram showing a structure of a liquid level gauge diagnosed by an abnormality diagnosis device according to an embodiment of the present invention.
FIG. 2 is a diagram showing a configuration of an abnormality diagnosis device according to one embodiment of the present invention.
FIG. 3 is a graph showing a temporal change of an average value of dispersion values W1 and W2 for a plurality of liquid level gauges.
FIG. 4 is a graph showing a magnitude relationship between dispersion values W1 and W2 with respect to a liquid level gauge before and after torque tube replacement.
FIG. 5 is a diagram showing a configuration of an abnormality diagnosis device according to another embodiment of the present invention.
[Explanation of symbols]
Reference Signs List 100 100 Level gauge 101 Bearing 102 Torque tube 103 Lever 104 Float 105 Rod 106 Strain gauge 110 Tank 111 Intermediate product 112 Inflow pipe 113 Inflow valve 114 Outflow pipe 115 Outflow valve 120 ... Control unit 130 ... Abnormal diagnosis device 131 ... Wavelet transform unit 132 ... Dispersion processing unit 133 ... Abnormality decision unit 134 ... Input unit 135 ... Display unit 501 ... A / D conversion unit 502 ... CPU
502a bus line 503 memory 504 external storage device 505 keyboard 506 monitor

Claims (3)

A first step of performing a wavelet transform of a time-series measurement signal output from a measuring device to convert the signal into a wavelet transform signal indicating a relationship between time and frequency;
A first variance value of the wavelet transform signal at a predetermined time interval in the first frequency domain of the first cycle and a wavelet transform signal of the wavelet transform signal in a second frequency domain of a second cycle larger than the first cycle; A second step of obtaining a second variance value at the predetermined time interval;
A third step of comparing the first variance value with the second variance value, and determining that the first variance value is abnormal when the first variance value becomes larger than the second variance value. An abnormality diagnosis method characterized by the following. A wavelet transform unit that performs a wavelet transform on the time-series measurement signal output from the measurement device and converts the signal into a wavelet transform signal indicating a relationship between time and frequency;
A first variance value of the wavelet transform signal in the first frequency domain at a predetermined time interval and a second variance value of the wavelet transform signal in the second frequency domain having a period larger than that of the first frequency domain at the predetermined time interval. A dispersion processing unit that calculates a variance value of 2;
An abnormality determination unit that compares the first variance value with the second variance value and determines that the first variance value is abnormal when the first variance value is larger than the second variance value. Characteristic abnormality diagnosis device. A first step of performing a wavelet transform of a time-series measurement signal output from a measuring device to convert the signal into a wavelet transform signal indicating a relationship between time and frequency;
A first variance value of the wavelet transform signal at a predetermined time interval in the first frequency domain of the first cycle and a wavelet transform signal of the wavelet transform signal in a second frequency domain of a second cycle larger than the first cycle; A second step of obtaining a second variance value at the predetermined time interval;
A third step of comparing the first variance value with the second variance value, and determining that the first variance value is abnormal when the first variance value becomes larger than the second variance value. .

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