JP2002278621A - Plant operation monitoring apparatus and method - Google Patents

Abstract

(57) [Summary] [Problem] To monitor the tendency of a gradual state change of a plant. SOLUTION: A data registration unit 1 for converting a process value inputted from a plant every moment into time-series process data, and a storage unit 2 for sequentially storing the process data.
And a monitoring unit 3 that detects a tendency of a change in a process value by statistically processing a plurality of process data in a predetermined inspection section K stored in the storage unit 2.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a plant operation monitoring device for monitoring an operation state of a plant.

[0002]

2. Description of the Related Art In a conventional plant operation monitoring apparatus, the level and the change rate are different from the threshold values set for the level of process data of various devices constituting the plant and the change rate in a short period of time. If the value does not exceed the value, the operation state of the plant is determined to be normal, and if the value exceeds the threshold value, an alarm is issued indicating that the operation state of the plant is abnormal.

[0003] In addition to the above technology, Japanese Patent Laid-Open No. 2000-2
Japanese Unexamined Patent Publication No. 9513 discloses a technique of calculating the sharpness or the degree of distortion of the residual between the average value and the moving average value of a process data in a short period to obtain the threshold value. On the other hand, Japanese Patent Application Laid-Open No. 7-210239 discloses a plant abnormality determination technique using a model equation. Furthermore, JP-A-2000-99333 discloses that A
A plant monitoring technique using case-based reasoning, which is one of the I methods, is disclosed.

[0004]

However, in the above-mentioned various prior arts, it is not possible to detect a tendency of a gradual state change of the plant. For example, one of the components of a plant
When one of the valves is gradually clogged over time, the flow rate (process value) of the fluid passing through the valve tends to gradually decrease. In the related art, such a process value is gradually reduced. A monotonous change cannot be detected. This gradual monotonic change does not indicate an imminent abnormal state of the plant, but is extremely important in grasping the operating state of the plant.

The present invention has been made in view of the above-described problems, and has as its object to monitor the tendency of a gradual state change of a plant.

[0006]

In order to achieve the above-mentioned object, according to the present invention, as a first means relating to a plant operation monitoring apparatus, a process value inputted from a plant every time is converted into time-series process data. A data registration unit for converting the process data into a plurality of process data, a storage unit for sequentially storing the process data, and a process value change tendency detected by statistically processing a plurality of process data in a predetermined inspection section K stored in the storage unit. And a monitoring unit that performs the monitoring.

[0007] A second aspect related to the plant operation monitoring device.
In the first means, the monitoring unit sets the inspection section K to the first inspection section T1 and the first inspection section T1 by setting a boundary point tc between the start point ta and the end point tb of the inspection section K.
A second inspection section T2 other than the inspection section T1 is divided into a plurality of first inspection sections T1 and a plurality of second inspection sections T2 when the boundary point tc is sequentially moved at a predetermined width toward the end point tb. A first-order approximation formula by the multiplication method and a variance of the process value with respect to the first-order approximation formula are obtained, and each variance is smaller than a predetermined variance threshold, and a slope of the first-order approximation is larger than a predetermined slope threshold. In such a case, means for determining that the process value has a certain change tendency is adopted.

[0008] Further, as a third means relating to the plant operation monitoring device, in the above-mentioned second means, the monitoring unit may be:
The smallest variance among the variances of the first test section T1 and the second test section T2 is obtained. If the smallest variance is smaller than a predetermined variance threshold, the boundary point tc corresponding to the smallest variance is obtained. Is used as the starting point of the change of the process value.

As a fourth means relating to the plant operation monitoring apparatus, in any one of the first to third means, the data registration unit stores the target value and the control amount in the control in a time series with the process value. The monitoring unit employs means for monitoring whether or not the plant is under control of the plant control device based on the state of following the control amount to the target value.

As a fifth means relating to the plant operation monitoring device, in any one of the first to fourth means, the monitoring unit may be configured to determine whether the process value exceeds a predetermined level threshold value or the process value. When the change rate exceeds a predetermined change rate threshold value, a means for determining that the plant is abnormal is adopted.

On the other hand, in the present invention, as a first means relating to the plant operation monitoring method, of the process values obtained from the plant every moment, the process values within a predetermined inspection section K are statistically processed to thereby carry out the process. Means of detecting the tendency of the value change is adopted.

A second aspect of the plant operation monitoring method is as follows.
In the first means, the inspection section K is set in the first inspection section T1 and the first inspection section T1 by setting a boundary point tc between the start point ta and the end point tb of the inspection section K.
And a plurality of first inspection sections T1 and second inspection sections T2 when the boundary point tc is sequentially moved at a predetermined width toward the end point tb by the least square method. An approximate expression and a variance of a process value with respect to the first-order approximate expression are obtained, and when each of the variances is smaller than a predetermined variance threshold and the slope of the first-order approximate expression is larger than a predetermined slope threshold, the process is performed. Means of determining that the value has a certain change tendency is adopted.

Further, as a third means relating to the plant operation monitoring method, the second means finds the smallest variance among the variances of the first inspection section T1 and the second inspection section T2, and obtains the smallest variance. When the variance is smaller than a predetermined variance threshold, a means is adopted in which a boundary point tc corresponding to the smallest variance is set as a process value change start point.

As a fourth means relating to the plant operation monitoring method, in any one of the first to third means, a target value and a control amount in control are sequentially stored in chronological order together with a process value, and the control amount is controlled. Means is employed for monitoring whether or not the plant is under the control of the plant control device based on the state of following the target value.

As a fifth means relating to the plant operation monitoring method, in any one of the first to fourth means, when the level of the process value exceeds a predetermined level threshold value or when the rate of change of the process value is a predetermined value. Means for determining that the plant is abnormal when the change rate threshold value is exceeded.

[0016]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of a plant operation monitoring apparatus and method according to the present invention will be described below with reference to the drawings.

FIG. 1 is a block diagram of a plant operation monitoring device according to this embodiment. As shown in this figure, the plant operation monitoring device includes a plant data registration unit 1, a plant data storage unit 2, a plant data monitoring unit 3, a parameter setting unit 4, and an output unit 5.

The plant data registration unit 1 converts a plant state quantity (analog quantity) obtained from the plant control device into time-series plant data (digital value), and stores the plant data in the plant data storage unit 2. Things. Here, the plant state quantity is a process value of various devices constituting the plant, and a target value and a control amount input to the plant control device for controlling these various devices.

The plant data registration unit 1 digitizes a process value as a continuous analog quantity by sampling it at a predetermined time interval, and adds a time stamp to this digital data to convert the digital value into time-series process data. Convert. Similarly, the plant data registration unit 1 similarly digitizes the target value and the control amount, which are analog amounts, and converts them into time-series target value data and control amount data by adding a time stamp.

The plant data storage unit 2 stores a set of plant data (ie, process data, target value data, and control amount data) input from the plant data registration unit 1 moment by moment, and sequentially stores the data in association with a time stamp. Database. That is, the plant data storage unit 2 is configured to be able to read out the plant data including the above four data by designating a time stamp from outside.

The plant data monitoring section 3 monitors the operating state of the plant based on such plant data. The plant data monitoring unit 3 performs a statistic process on the process data among the plant data read out from the plant data storage unit 2 (database) to monitor a gradual monotonous change in the process value, and to perform the target value data and control. It monitors whether the plant is under control of the plant control device based on the quantity data.

The plant data monitoring unit 3 also performs, as additional monitoring processes, level monitoring of process values based on process data and rate of change of process values based on process data. These plant data monitoring units 3
Is performed with reference to various parameters provided from the parameter setting unit 4 as well. The details of the various monitoring processes will be described later.

The parameter setting section 4 sets various threshold values necessary for monitoring the operation state of the plant, such as a dispersion threshold value, a slope threshold value, a level threshold value, a change rate threshold value, and the like as the various parameters. This is provided to the plant data storage unit 2. Details of these various parameters will be described later for convenience of explanation. The output unit 5 outputs a monitoring result of the operating state to a monitor who monitors the operation of the plant. For example, an alarm device for notifying the monitor of an abnormality of the plant and a change tendency of the operating state of the plant are displayed to the monitor. And a display device.

Next, the operation of the plant operation monitoring apparatus thus configured will be described in detail with reference to FIG.

[Monotone Change Monitoring Process] First, the process value monotonic change monitoring process by the plant operation monitoring apparatus will be described. As described above, the plant data storage unit 2 of the present plant operation monitoring apparatus sequentially stores a plurality of plant data (process data, target value data, control amount data) over a certain period according to the storage capacity. I have.

The plant data monitoring unit 3 reads out all the process data in the inspection section K having a predetermined time width from such time-series plant data and performs statistical processing on the data, thereby obtaining the process in the inspection section K. A process for detecting a change tendency of the value is performed. When the plant data monitoring unit 3 finishes the process of detecting the change tendency of the process value in a certain inspection section K, the plant data monitoring unit 3 stores all the process data corresponding to the next inspection section K ′ in chronological order from the plant data storage unit 2. The process of reading and detecting the same change tendency is repeatedly performed. That is, when the process of detecting the change tendency of the process value in a certain inspection section K is completed, the plant data monitoring unit 3 sequentially repeats the process of detecting the change tendency of the process value for the next new inspection section K ′ in a time series. .

FIG. 2 is a first explanatory diagram for explaining the details of the statistical processing. In this figure, the horizontal axis is the time axis indicating time (t), the vertical axis is the level axis indicating the level (y) of the process value indicated by the process data, and the crosses indicate the process data (accurately, the process data). Is the process value indicated by. As shown, the inspection section K includes a plurality of process data. The plant data monitoring unit 3 sets the boundary section tc between the start point ta and the end point tb of the inspection section K, and thereby sets the inspection section K in the first inspection section T1 and the second inspection other than the first inspection section T1. It is divided into a section T2. Here, the start point ta is a temporally older (past) time with respect to the end point tb.

The first inspection section T1 is initially set to a minimum inspection period which is the minimum number of process data required for statistical processing. Therefore, the second inspection section T2 is The period width is set to a period width obtained by subtracting the minimum inspection period (first inspection section T1) from the period width. Then, the boundary point tc is sequentially moved with a predetermined width toward the end point tb until the second inspection section T2 finally becomes the minimum inspection period (that is, until time tc ").
At a plurality of positions in the inspection section K, a plurality of first inspection sections T1 and second inspection sections T2 having a period width corresponding to the position of the boundary point tc are set.

The plant data monitoring unit 3 applies the least squares method to all the plant data included in the first inspection section T1 for the first inspection section T1 and the second inspection section T2 of each period width. As a result, the first inspection section T
Linear approximation formula (y = a1 · t +
b1) and the variance .sigma.1 of the plant data with respect to the gradient a1 of the linear approximation formula.
The least squares method is applied to all the plant data in the inspection section T2, and the variance σ2 of the plant data in the second inspection section T2 with respect to the first-order approximate expression (y = a2 · t + b2) and the slope a2 of the first-order approximate expression Are calculated respectively.

As is well known, if the above-mentioned linear approximation is a general expression y = at + t + b and the process value indicated by the plant data is given by a function f (t), the variance σ is Given by (1). Therefore, the variance σ can be easily calculated from the linear approximation formula and the process value of each plant data.

[0031]

(Equation 1)

Next, FIG. 3 shows the linear approximation formulas of the first inspection section T1 and the second inspection section T2 and changes in the variances σ1 and σ2 according to the position of the boundary point tc. In this drawing, as an example, the process value from the start point ta to the time point B of the inspection section K is substantially constant, and the process value from the time point B to the end point tb monotonously decreases. .

FIG. 3 (a) shows the starting point ta and the point B
FIG. 3 shows a case where the above-mentioned boundary point tc is set at the time point A located between the first inspection section T1 and the minimum inspection period.
(B) shows a case where the boundary point tc 'is set at the time point B, and FIG. 3 (c) shows a case where the boundary point tc "is set at the time point C located between the time point B and the end point tb ( (Second inspection section T2 "= minimum inspection period).

First, in the case of FIG.
1 is almost constant, the first-order approximation equation (y = a1 · t + b1) has a slope a1 = 0 as shown in FIG.
And takes a constant value b1. Also, the variance σ1 of each process data with respect to this linear approximation expression is a small value. On the other hand, the process data of the second inspection section T2 is substantially constant from the time A to the time B,
, There is a monotonic decreasing trend, so the linear approximation (y = a2
(T + b2) is a straight line inclined with an inclination a2 as shown. In the second inspection section T2, the variance σ2 of each process data with respect to this linear approximation is substantially constant from the time A to the time B and monotonically decreases from the time B as described above. Because of the tendency, the value is relatively large.

Subsequently, in the case of FIG. 3B, since each process data in the first inspection section T1 'is substantially constant, the first-order approximation equation (y = a1'.t + b1') has a slope a as shown in FIG.
1 '= 0, and takes a constant value b1'. In addition, the variance σ1 ′ of each process data with respect to this linear approximation is a small value. On the other hand, since each process data in the second inspection section T2 'has a monotonous decreasing tendency from the time point B, the first-order approximation equation (y = a2'.t + b2') has a slope a
It becomes a straight line inclined with 2 '. In the second inspection section T2 ', the variance σ2' of each process data with respect to this linear approximation expression has a relatively small value because each process data has a monotonous decreasing tendency from the time point B.

Further, in the case of FIG. 3C, each process data in the first inspection section T1 "is substantially constant from time B to time B, but tends to monotonously decrease from time B to time C. Therefore, a first-order approximation formula (y = a1 ".t + b1")
As shown in the figure, the gradient a1 "= 0 and takes a constant value b1". The variance .sigma.1 "of each process data with respect to this linear approximation is substantially constant from time B to time B, but tends to decrease monotonically from time B to time C, so that it becomes a relatively large value. On the other hand, since the process data in the second inspection section T2 "tends to decrease monotonically, the first-order approximation equation (y = a2" .t + b2 ") is inclined with the inclination a2" as shown in the drawing. A straight line,
In the second inspection section T2 ", the variance .sigma.2" of each process data with respect to this linear approximation expression has a relatively small value because each process data has already started to decrease monotonically at the time point C.

As described above, in the case of FIG. 3B, that is, when the boundary point tc 'is set at the time point B at which the process data starts to decrease monotonically, the first inspection section T1' and the second inspection section T1 ' Both variances σ1 ′ and σ2 ′ in the section T2 ′ have the smallest values. When the boundary point tc 'is moved to the start point ta, the variance .sigma.1 of the first inspection section T1 is small as shown in FIG. Is larger than the variance .sigma.2 'of the second inspection section T2' when. Conversely, when the boundary point tc 'is moved toward the end point tb, the variance σ2 ″ of the second inspection section T2 ″ is small, but the variance σ1 ″ of the first inspection section T1 ″ is small as shown in FIG. When the boundary point tc 'is set at the time point B, the variance .sigma.1' of the first inspection section T1 'is larger.

As described above, the plant data monitoring unit 3 calculates a first approximation formula and a first approximation equation for each of the first inspection section T1 and the second inspection section T2 set by moving the boundary point tc within the inspection section K. Σ1 and σ2 are calculated, and the boundary point tc when σ1 + σ2 = σa is minimized is set as a change start point at which the process data starts to decrease monotonically.

More specifically, each of the variances σ1, σ2 of the first inspection section T1 and the second inspection section T2 is compared with a predetermined variance threshold, and each of the variances σ1, σ2 is the variance threshold. The boundary point tc when the value falls below the value is determined as the change start point of the process data. Then, the plant data monitoring unit 3 obtains the first approximation formula (y =
The absolute value of the slope a2 of (a2.t + b2) is compared with a predetermined slope threshold value, and when the absolute value exceeds the slope threshold value, it is determined that the process data has a monotonous decreasing tendency.

[Control Amount Monitoring Process] Next, a control amount monitoring process by the plant data monitoring unit 3 will be described. The plant data monitoring unit 3 sequentially reads out time-series continuous target value data, control amount data, and process data from the plant data storage unit 2, and determines a control amount (MV) in plant control based on the control amount data. Or not, and if the control amount is not within the control amount threshold for a certain period of time, the process value (PV) based on the process data is It monitors whether or not it follows the target value (SP) based on the target value data.

For example, when the control amount (MV) rises and exceeds the upper limit of the control amount threshold value, the plant data monitoring unit 3 measures the duration of this state. If this duration is continued for a certain period, the absolute value of the difference between the process value (PV) and the target value (SP), that is, | S
P-PV | is calculated, and when the absolute value tends to gradually increase, the process value (PV) is set to the target value (SP).
, That is, the plant is not under the control of the plant control device.

[Level Monitoring Processing] Next, the level monitoring processing by the plant data monitoring unit 3 will be described. The plant data monitoring unit 3 monitors whether the level of the process value is at an abnormal level based on the process data sequentially read from the plant data storage unit 2 in chronological order.
That is, the level threshold value supplied from the parameter setting unit 4 is composed of a lower threshold value indicating the lower limit of the process value and an upper threshold value indicating the upper limit of the process value. By sequentially comparing the level threshold value with the process value based on the process data, it is monitored whether the process value has deviated from the normal range and has reached the abnormal level.

[Change Rate Monitoring Process] The change rate monitoring process by the plant data monitoring unit 3 will be described. The plant data monitoring unit 3 monitors whether or not the rate of change of the process value is at an abnormal rate based on the process data sequentially read from the plant data storage unit 2 in chronological order. That is, the change rate threshold value supplied from the parameter setting unit 4 indicates the upper limit of the change rate of the process value, and the plant data monitoring unit 3 is based on such a change rate threshold value and the process data. By sequentially comparing the change rate of the process value with the change rate of the process value, it is monitored whether or not the process value is out of the normal range and is changing at an abnormally high speed.

As a result of the various monitoring processes described above, a monotonous change in the process value (including not only a monotonic decrease but also a monotonic increase),
The plant is not under control of the plant control,
When an abnormal level of the process value or an abnormal rate of change of the process value is detected, the plant data monitoring unit 3 outputs a notification signal indicating the monitoring result to the output unit 5.
As a result, the output unit 5 notifies the monitoring result of the monitoring to a plant supervisor.

According to the present embodiment, not only the abnormality of the process value and its change rate performed conventionally, but also the gradual monotonic change of the process value, that is, the monotone decrease, the monotone increase and the change thereof as shown in FIG. A starting point can also be detected. That is, in the inspection section A, the constant state of the process value has turned to a monotonic increase, in the inspection section B, the process value has a monotonic increase, and in the inspection section C, the process value has changed to a minor decrease. Further, in the inspection section D, a monotonous decrease in the process value is detected.

Therefore, a monotonous change in the process value, that is, a sign of an abnormality can be detected at a stage before the operation state of the plant becomes abnormal. Therefore, it is necessary to take appropriate preventive measures before the occurrence of the abnormality. It becomes possible. Moreover, since the correlation between the target value in the plant control and the change tendency of the control amount is also monitored, it is also monitored whether the plant is normally controlled by the plant control device.
The occurrence of an abnormality in the plant control can also be confirmed.

In the above-described embodiment, as a method for detecting a gradual monotonous change in the process value, an inspection section K having a predetermined time width is set and a boundary point tc is specified for the inspection section K. Sets the first inspection section T1 and the second inspection section T2, and further sets the boundary point tc to the end point tb.
, A plurality of first inspection sections T1 and second inspection sections T2 having a period width corresponding to the position of the boundary point tc are set by sequentially moving the first inspection section T1 and the second inspection section T2. Then, a first-order approximation formula, which is a statistical method, and a variance are calculated for the plurality of first inspection intervals T1 and the second inspection intervals T2 set as described above, and the occurrence or change of the monotonic change is calculated based on the calculation result. The starting point is determined.

Here, the time width of the inspection section K, the minimum inspection period, and the time width for moving the boundary point tc are determined by the attribute of the process value (what kind of equipment is involved) or the type of plant (petroleum plant or other It is necessary to optimize based on the type of plant).

[0049]

As described above, according to the present invention,
Of the process values obtained moment by moment from the plant, the process values within a predetermined inspection section K are statistically processed to detect the change tendency of the process values, so that the tendency of the plant to gradually change its state is monitored. Becomes possible. Therefore, it is possible to recognize a change in the operating state of the plant before the plant reaches an abnormality.

[Brief description of the drawings]

FIG. 1 is a block diagram showing a functional configuration of a plant operation monitoring device according to an embodiment of the present invention.

FIG. 2 is a first explanatory diagram for explaining a monotonic change monitoring process of a process value in the plant operation monitoring device.

FIG. 3 is a second explanatory diagram for explaining a monotonic change monitoring process of a process value in the plant operation monitoring device.

FIG. 4 is an explanatory diagram illustrating a state of a monotonous change in a test section K according to an embodiment of the present invention.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 ... Plant data registration part 2 ... Plant data storage part 3 ... Plant data monitoring part 4 ... Parameter setting part 5 ... Output part K ... Inspection section ta ... Start point tb ... End point tc, tc ', tc "... Boundary point T1, T1 ', T1" ... First inspection section T2, T2', T2 "... Second inspection section σ1, σ2 ... Dispersion

Continued on the front page F term (reference) 2F076 BA14 BA16 BE04 BE05 BE08 BE11 5C086 AA60 BA20 DA14 DA40 EA40 EA45 5H223 EE06 EE30 FF02

Claims (10)

[Claims] 1. A data registration unit (1) for converting a process value input from time to time from a plant to time-series process data; a storage unit (2) for sequentially storing the process data; And a monitoring unit (3) for statistically processing a plurality of pieces of process data in a predetermined inspection section K stored in (2) to detect a change tendency of a process value. apparatus. 2. The monitoring unit (3) sets a boundary point tc between a starting point ta and an ending point tb of the inspection section K, thereby setting the inspection section K to a position other than the first inspection section T1 and the first inspection section T1. Of the plurality of first inspection sections T1 and second inspection sections T2 when the boundary point tc is sequentially moved at a predetermined width toward the end point tb by the least square method. And the variance of the process value with respect to the linear approximation expression. If the variance is smaller than a predetermined variance threshold and the slope of the linear approximation is larger than a predetermined slope threshold, the process value is calculated. 2. The plant operation monitoring device according to claim 1, wherein it is determined that has a certain change tendency. 3. The monitoring unit (3) obtains the smallest variance among the variances of the first test interval T1 and the second test interval T2, and when the smallest variance is smaller than a predetermined variance threshold. The boundary point tc corresponding to the smallest variance
3. The plant operation monitoring device according to claim 2, wherein is a change start point of the process value. 4. A data registration unit (1) sequentially stores a target value and a control amount in control together with a process value in a time series in a storage unit (2). The plant operation monitoring device according to any one of claims 1 to 3, wherein whether or not the plant is under control of the plant control device is monitored based on a state following the value. 5. The monitoring unit (3), when the level of the process value exceeds a predetermined level threshold, or when the rate of change of the process value exceeds the predetermined rate threshold, the plant is abnormal. 2. The method according to claim 1, further comprising:
A plant operation monitoring device according to any one of claims 1 to 4. 6. A plant operation monitor characterized by detecting a change tendency of a process value in a predetermined inspection section K by statistically processing a process value among process values obtained from time to time from a plant. Method. 7. By setting a boundary point tc between a start point ta and an end point tb of the inspection section K, the inspection section K is changed to a first inspection section T1 and a second inspection section T2 other than the first inspection section T1.
And a plurality of first inspection sections T1 and second inspection sections when the boundary point tc is sequentially moved by a predetermined width toward the end point tb.
A linear approximation by the least squares method and a variance of a process value with respect to the linear approximation are obtained for the inspection section T2, and each variance is smaller than a predetermined variance threshold, and a gradient of the linear approximation is a predetermined gradient. 7. The plant operation monitoring method according to claim 6, wherein it is determined that the process value has a certain change tendency when the value is larger than the threshold value. 8. A first inspection section T1 and a second inspection section T2.
Finding the smallest variance among the variances, and when the smallest variance is smaller than a predetermined variance threshold, the boundary point tc corresponding to the smallest variance is set as the process value change start point The plant operation monitoring method according to claim 7, wherein: 9. A target value and a control amount in control together with a process value are sequentially stored in a time-series manner, and whether or not the plant is under control of a plant control device is determined based on a state in which the control amount follows the target value. The plant operation monitoring method according to any one of claims 6 to 8, wherein monitoring is performed. 10. The plant is determined to be abnormal when the level of the process value exceeds a predetermined level threshold or when the rate of change of the process value exceeds a predetermined rate threshold. The plant operation monitoring method according to any one of claims 6 to 9, wherein