阅读说明：本技术 监视装置、显示装置、监视方法及监视程序 (Monitoring device, display device, monitoring method, and monitoring program ) 是由 门脇正法 于 2020-03-13 设计创作，主要内容包括：本发明提供一种即使在实际测定的过程数据的数据量有限的情况下也能够判定工厂设备的运行状态的监视装置、显示装置、监视方法及监视程序。监视装置(10)具备：输入部(10e、11),接受与工厂设备有关的过程数据的输入；模型生成部(12),根据所输入的过程数据来生成表示过程数据的关联性的模型；判定部(13),通过第1判定模式或第2判定模式来判定工厂设备的运行状态,其中,所述第1判定模式使用根据由人输入的过程数据的预测值生成的模型,所述第2判定模式使用根据过程数据的实测值生成的模型；及显示部(10f),显示判定部的判定结果。(The invention provides a monitoring device, a display device, a monitoring method and a monitoring program, which can judge the operation state of plant equipment even if the data volume of actually measured process data is limited. A monitoring device (10) is provided with: input units (10e, 11) for receiving input of process data relating to plant equipment; a model generation unit (12) that generates a model indicating the relevance of process data from the input process data; a determination unit (13) that determines the operating state of the plant in a 1 st determination mode or a 2 nd determination mode, wherein the 1 st determination mode uses a model generated from predicted values of process data input by a human, and the 2 nd determination mode uses a model generated from measured values of the process data; and a display unit (10f) for displaying the result of the determination by the determination unit.)

1. A monitoring device is characterized by comprising:

an input unit that accepts input of process data relating to plant equipment;

a model generation unit that generates a model indicating a correlation of the process data from the input process data;

a determination unit that determines an operation state of the plant in a 1 st determination mode or a 2 nd determination mode, wherein the 1 st determination mode uses the model generated from a predicted value of the process data input by a person, and the 2 nd determination mode uses the model generated from an actual measurement value of the process data; and

and a display unit for displaying the result of the determination by the determination unit.

2. The monitoring device of claim 1,

the determination unit calculates a degree of abnormality of the operation state of the plant equipment by the 1 st determination mode or the 2 nd determination mode based on the actually measured process data, and determines the operation state of the plant equipment based on the degree of abnormality.

3. The monitoring device of claim 1 or 2,

the model includes a graph representing a correlation of the process data,

the display unit displays the graphic.

4. The monitoring device of claim 3,

the model includes a range of the process data that falls within a vicinity of the graph with a specified probability,

the display unit displays the graphic and the range.

5. The monitoring device of any one of claims 1 to 4,

the model generation unit generates the model based on a design value of the process data.

6. The monitoring device of any one of claims 1 to 5,

the input unit accepts input of a hand-drawn figure for drawing a drawing area of the process data,

the monitoring device further includes a drawing unit that draws data points representing the hand-drawn figure in the drawing area,

the model generator generates the model from the data points.

7. The monitoring device of claim 6,

the input unit accepts input of a hand-drawn range for the drawing area, the hand-drawn range including the hand-drawn figure,

the drawing section draws data points representing the hand-drawn figure and the hand-drawn range in the drawing area.

8. A display device is characterized in that a display panel is provided,

the method includes receiving an input of process data related to plant equipment, generating a model indicating a correlation of the process data from the input process data, determining an operation state of the plant equipment by a 1 st determination mode or a 2 nd determination mode, and displaying a determination result, wherein the 1 st determination mode uses the model generated from a predicted value of the process data input by a person, and the 2 nd determination mode uses the model generated from an actual measurement value of the process data.

9. A method of monitoring, characterized in that,

causing a monitoring device that monitors plant equipment to perform the steps of:

accepting input of process data relating to plant equipment;

generating a model representing a correlation of the process data from the input process data;

determining an operation state of the plant through a 1 st determination mode or a 2 nd determination mode, wherein the 1 st determination mode uses the model generated from a predicted value of the process data input by a person, and the 2 nd determination mode uses the model generated from an actual measurement value of the process data; and

and displaying a determination result based on the determination.

10. A monitoring program, characterized in that,

causing a monitoring device that monitors plant equipment to perform the steps of:

accepting input of process data relating to plant equipment;

generating a model representing a correlation of the process data from the input process data;

determining an operation state of the plant through a 1 st determination mode or a 2 nd determination mode, wherein the 1 st determination mode uses the model generated from a predicted value of the process data input by a person, and the 2 nd determination mode uses the model generated from an actual measurement value of the process data; and

and displaying a determination result based on the determination.

Technical Field

The invention relates to a monitoring device, a display device, a monitoring method, and a monitoring program.

Background

Conventionally, time series data (i.e., process data) relating to the operation state of plant equipment is measured, and a model indicating the correlation of the process data is generated using the past process data as learning data. The generated model is sometimes used to determine whether or not the plant equipment is operating normally.

For example, patent document 1 listed below describes an evaluation device for learning knowledge, which compares a target value when a control device controls a control target with a past measured value relating to the control target to determine whether or not learning knowledge is appropriate.

Patent document 2 listed below describes a demand prediction device that creates a prediction model for predicting a demand amount from past actual data of the demand amount, and corrects the demand amount from the actual data, future weather prediction data, and the predicted demand amount.

Prior art documents

Patent document

Patent document 1: japanese laid-open patent publication No. 7-219604

Patent document 2: japanese patent laid-open publication No. 2018-73214

Disclosure of Invention

Technical problem to be solved by the invention

When a model representing the correlation of process data is generated using the past process data as learning data, it is assumed that a certain degree of actually measured process data is stored. However, when the data amount of process data actually measured is limited, such as immediately after the plant is started, it is difficult to generate a model, and there may be a period during which the operating state of the plant cannot be determined.

Accordingly, the present invention provides a monitoring device, a display device, a monitoring method, and a monitoring program that can determine the operating state of plant equipment even when the amount of data of process data actually measured is limited.

Means for solving the technical problem

One embodiment of the present invention provides a monitoring device including: an input unit that accepts input of process data relating to plant equipment; a model generation unit that generates a model indicating a correlation of process data from the input process data; a determination unit that determines an operation state of the plant in a 1 st determination mode or a 2 nd determination mode, wherein the 1 st determination mode uses a model generated from a predicted value of the process data input by a person, and the 2 nd determination mode uses a model generated from an actual measurement value of the process data; and a display unit for displaying the result of the judgment by the judgment unit.

According to this embodiment, since the operating state of the plant is determined by the 1 st determination mode using the model generated from the predicted values of the process data that are not actually measured, even when the data amount of the process data that are actually measured is limited, it is possible to generate the model indicating the correlation of the process data and determine the operating state of the plant. Thus, the operating state can be determined immediately after the plant is started, and the downtime can be reduced.

Another embodiment of the present invention provides a display device that receives an input of process data related to plant equipment, generates a model indicating a correlation of the process data from the input process data, determines an operation state of the plant equipment in a 1 st determination mode or a 2 nd determination mode, and displays a determination result, wherein the 1 st determination mode uses the model generated from a predicted value of the process data input by a person, and the 2 nd determination mode uses the model generated from an actual measurement value of the process data.

In another embodiment of the present invention, there is provided a monitoring method for causing a monitoring device that monitors plant equipment to execute: accepting input of process data relating to plant equipment; generating a model representing a correlation of process data from the input process data; determining an operation state of the plant through a 1 st determination mode or a 2 nd determination mode, wherein the 1 st determination mode uses a model generated from a predicted value of the process data input by a person, and the 2 nd determination mode uses a model generated from an actually measured value of the process data; and displaying the determination result based on the determination.

Still another embodiment of the present invention provides a monitoring program that performs the steps of: accepting input of process data relating to plant equipment; generating a model representing a correlation of process data from the input process data; determining an operation state of the plant through a 1 st determination mode or a 2 nd determination mode, wherein the 1 st determination mode uses a model generated from a predicted value of the process data input by a person, and the 2 nd determination mode uses a model generated from an actually measured value of the process data; and displaying the determination result based on the determination.

Effects of the invention

According to the present invention, it is possible to provide a monitoring device, a display device, a monitoring method, and a monitoring program capable of generating a model indicating the correlation of process data even when the amount of actually measured process data is limited.

Drawings

Fig. 1 is a diagram showing functional blocks of a monitoring device according to an embodiment of the present invention.

Fig. 2 is a diagram showing a physical configuration of the monitoring device according to the present embodiment.

Fig. 3 is a diagram showing a model indicating the correlation of process data generated by the monitoring device according to the present embodiment.

Fig. 4 is a diagram showing the degree of abnormality calculated by the monitoring device according to the present embodiment.

Fig. 5 is a flowchart of a determination process executed by the monitoring device according to the present embodiment.

Fig. 6 is a diagram showing data points plotted by the monitoring device according to the present embodiment.

Fig. 7 is a flowchart of a model generation process executed by the monitoring device according to the present embodiment.

Detailed Description

Embodiments of the present invention will be described with reference to the accompanying drawings. In the drawings, the same or equivalent structures are denoted by the same reference numerals.

Fig. 1 is a diagram showing functional blocks of a monitoring device 10 according to an embodiment of the present invention. The monitoring device 10 is a device for monitoring the operating state of the plant 100, and includes an acquisition unit 11, a model generation unit 12, a determination unit 13, a rendering unit 14, an input unit 10e, and a display unit 10 f.

The acquisition unit 11 acquires process data related to the plant 100. Here, the plant 100 may be any plant, for example, a power plant including a boiler, an incineration plant, a chemical plant, a wastewater treatment plant, or the like, which can acquire process data. The process data may be any data related to the plant 100, and may be data obtained by measuring the state of the plant 100 with a sensor, for example, and more specifically may include measured values of the temperature, pressure, flow rate, and the like of the plant 100. The acquisition unit 11 may acquire the process data at predetermined time intervals or may acquire the process data continuously to acquire the time series data about the plant 100.

The acquisition section 11 can acquire various process data related to the plant 100. The acquisition unit 11 can acquire a plurality of types of process data measured by a plurality of sensors provided in the plant 100. Here, the plurality of types of process data may be data representing different physical quantities such as temperature and pressure, or data representing the same physical quantity such as temperature measured at different locations of the plant 100.

The input unit 10e accepts input of process data. The input unit 10e may be constituted by a pointing device such as a touch panel or a mouse, or a keyboard, and may receive input of a predicted value of process data predicted by a human. The input unit 10e may accept input of a hand-drawn figure for drawing a drawing area of the process data or may accept input of a hand-drawn range including the hand-drawn figure. The process data input by the input unit 10e will be described in detail later with reference to the drawings.

The display unit 10f displays a drawing area for drawing the process data. The display unit 10f is used to monitor the operation state of the plant 100, and may display the result of determination of the operation state of the plant 100 by the monitoring device 10. The contents displayed on the display unit 10f will be described in detail later with reference to the drawings.

The model generation unit 12 generates a model indicating the relevance of process data from the input process data. The model representing the correlation of the process data may be a model representing a range in which the process data falls when the operation state of the plant 100 is normal, or may be a model extracting a feature represented by the process data when the operation state of the plant 100 is abnormal.

The model generation unit 12 may generate a model from design values of the process data. Here, the design values of the process data mean: target values or set values for plant design. More specifically, the design value of the process data is a value of the process data that should be measured in terms of the design of the plant 100, and is a value of the process data that is measured when the plant 100 is operating normally. The model generation unit 12 may generate a model indicating the correlation of the process data with reference to design values of the process data to be measured when the plant 100 is operating normally. Since the model indicating the correlation of the process data is generated based on the design value of the process data, even when the data amount of the process data actually measured is limited, the model indicating the correlation of the process data can be generated to determine the operation state of the plant 100.

The determination unit 13 determines the operation state of the plant 100 in a 1 st determination mode or a 2 nd determination mode, wherein the 1 st determination mode uses a model generated from a predicted value of the process data input by a person, and the 2 nd determination mode uses a model generated from an actual measurement value of the process data. The determination unit 13 may determine the operation state of the plant 100 using a model generated from design values of the process data. The model generated from the predicted value of the process data input by the person and the model generated from the measured value of the process data may be the same model having different data used for the model generation, but may be different models. The 2 nd determination mode may be a mode in which a model generated from predicted values of process data input by a person is updated using measured values of the process data. The model used in the 2 nd determination mode may be a model generated independently of the model used in the 1 st determination mode, or may be a model obtained by correcting the model used in the 1 st determination mode.

The monitoring device 10 according to the present embodiment determines the operating state of the plant equipment 100 by the 1 st determination mode in which a model generated from process data that is not actually measured is used, and therefore, even when the amount of actually measured process data is limited, a model indicating the correlation of the process data can be generated to determine the operating state of the plant equipment 100. Thus, the operating state can be determined immediately after the plant 100 is started, and the start-up time when the plant 100 is newly installed can be shortened, and the downtime when the plant 100 is temporarily stopped can be reduced.

The determination section 13 may calculate the degree of abnormality of the operation state of the plant apparatus 100 by the 1 st determination mode or the 2 nd determination mode based on the actually measured process data, and determine the operation state of the plant apparatus 100 based on the degree of abnormality. An example of the degree of abnormality calculated by the determination unit 13 will be described in detail later with reference to the drawings.

The drawing unit 14 draws data points representing the hand-drawn figure accepted by the input unit 10e in the drawing area. The drawing unit 14 may draw data points representing the hand-drawn figure and the hand-drawn range received by the input unit 10e in the drawing area. The processing by the rendering unit 14 will be described in detail later with reference to the drawings.

Fig. 2 is a diagram showing the physical configuration of the monitoring device 10 according to the present embodiment. The monitoring device 10 includes a CPU (Central Processing Unit) 10a corresponding to an arithmetic Unit, a RAM (Random Access Memory) 10b corresponding to a storage Unit, a ROM (Read only Memory) 10c corresponding to a storage Unit, a communication Unit 10d, an input Unit 10e, and a display Unit 10 f. These structures are connected via a bus so as to be able to transmit and receive data to and from each other. In the present example, a case where the monitoring apparatus 10 is configured by one computer is described, but the monitoring apparatus 10 may be realized by combining a plurality of computers. The configuration shown in fig. 2 is an example, and the monitoring device 10 may have other configurations or may not have some of these configurations.

The CPU10a is a control unit that performs control related to execution of programs stored in the RAM10b or the ROM10c, and performs arithmetic operations on data and processing. The CPU10a is an arithmetic unit that executes a program (monitoring program) that generates a model indicating the correlation of process data from predicted values of the process data input by a person and monitors plant equipment using the model. The CPU10a receives various data from the input unit 10e or the communication unit 10d, and displays the calculation result of the data on the display unit 10f or stores the calculation result in the RAM10b or the ROM10 c.

The RAM10b is a storage unit capable of rewriting data in the storage unit, and may be formed of, for example, a semiconductor memory element. The RAM10b may store data such as programs executed by the CPU10a, process data input by a person, and design values of the process data. These are examples, and other data may be stored in the RAM10b, or a part of these data may not be stored.

The ROM10c is a storage unit capable of reading data in the storage unit, and may be formed of, for example, a semiconductor memory element. The ROM10c may store, for example, a monitor program or data that is not rewritten.

The communication unit 10d is an interface for connecting the monitoring apparatus 10 to another device. The communication unit 10d can be connected to a communication network N such as the internet.

The input unit 10e is used for receiving data input from a user, and may include, for example, a keyboard and a touch panel.

The Display unit 10f visually displays the calculation result by the CPU10a, and the Display unit 10f may be formed of, for example, an LCD (Liquid Crystal Display). The display unit 10f may display a rendering area for rendering the process data and display the process data and the generated model in the rendering area.

The monitoring program may be provided by being stored in a computer-readable storage medium (such as the RAM10b or the ROM10 c), or may be provided via a communication network connected to the communication unit 10 d. In the monitoring apparatus 10, the CPU10a executes the monitoring program to realize the acquisition unit 11, the model generation unit 12, the determination unit 13, and the rendering unit 14, which are described with reference to fig. 1. In addition, these physical structures are examples and are not necessarily independent structures. For example, the monitoring device 10 may include an LSI (Large-Scale integrated circuit) in which the CPU10a and the RAM10b or the ROM10c are integrated.

Fig. 3 is a diagram showing a model indicating the correlation of process data generated by the monitoring device 10 according to the present embodiment. Fig. 3 shows an example in which a predicted value of process data is input from a person and a model is generated from the input predicted value of process data (process data that is not actually measured).

The display unit 10f of the monitoring device 10 displays the drawing area DA in which the process data is drawn. The user of the monitoring apparatus 10 uses the input unit 10e to plot the data point D1, which is predicted to be the correlation between the 1 st process data and the 2 nd process data, on the plot area DA. The data point D1 may be input by a touch panel or a pointing device, but may be acquired from a storage unit (such as the RAM10 b) built in the monitoring apparatus 10 or an external memory. For example, the data point D1 may be the design value for the 1 st and 2 nd process data.

The model generator 12 generates a model indicating the correlation between the 1 st process data and the 2 nd process data from the input data point D1. The model may include a graphic M1 indicating the correlation between the 1 st process data and the 2 nd process data, and the display unit 10f may display the graphic M1 in the drawing area DA. The model generating unit 12 may determine the parameters of a predetermined function representing the correlation between the 1 st process data and the 2 nd process data by a least squares method, for example, assuming the function and fitting the function to the data point D1. In this way, by displaying the graph M1 indicating the relevance of the process data, it is possible to grasp at a glance whether the generated model is appropriate.

The model may include a range M2 in which the process data falls in the vicinity of the graph M1 with a prescribed probability, and the display section 10f may display the range M2 in the drawing area DA. The model generator 12 may calculate a standard deviation σ of the input data point D1, and set ± σ as a range M2 with the graph M1 as the center, or set ± 2 σ as a range M2 with the graph M1 as the center, for example. In the case where the deviation of the process data follows a normal distribution, the range of ± σ is a range in which the process data falls near the graph M1 with a probability of 68.27%, and the range of ± 2 σ is a range in which the process data falls near the graph M1 with a probability of 95.45%. In this manner, by displaying the process data in the range M2 in the vicinity of the graph M1 with a predetermined probability, it is possible to grasp at a glance whether the newly acquired process data is within the normal range.

Fig. 4 is a diagram showing the degree of abnormality calculated by the monitoring device 10 according to the present embodiment. In fig. 4, the vertical axis represents the value of the degree of abnormality, the horizontal axis represents time, and the time change in the degree of abnormality is shown by a bar graph.

The determination unit 13 of the monitoring device 10 may calculate the degree of abnormality of the operating state of the plant 100 in the 1 st determination mode or the 2 nd determination mode based on the actually measured process data, and display the degree of abnormality on the display unit 10 f. The determination unit 13 may calculate the degree of abnormality by a known abnormality determination algorithm, for example, from the average μ and variance (variance) σ of process data actually measured in the past²And by a (x) ═ x-mu²/σ²The degree of abnormality a (x) of the current process data x is calculated. At this time, the square root of the degree of abnormality indicates how many times the current process data deviates from the standard deviation with reference to the average value of the past process data. For example, if the degree of abnormality is 25, it indicates that the current process data is deviated from the average value of the past process data by 5 times of the standard deviation. The determination unit 13 may perform the display shown in fig. 4 by periodically calculating the degree of abnormality of the operating state of the plant 100 and displaying the value as a bar graph, or may perform the display shown in fig. 4 by periodically calculating the degree of abnormality of the operating state of the plant 100 and displaying the average value over a longer period as a bar graph. The determination unit 13 may calculate the degree of abnormality based on how much the actually measured process data is deviated from the pattern M1. The judgment unit 13 may be based on a value indicating whether the actually measured process data is inside or outside the range M2 and a standard deviation of the data point D1, for exampleThe degree of abnormality is calculated from at least one of the deviation amounts of the actually measured process data.

The determination unit 13 may determine the operation state of the plant 100 based on the calculated abnormality degree. The determination unit 13 may compare a threshold value set for the degree of abnormality with the newly calculated degree of abnormality, and determine that the operation state of the plant 100 is normal when the degree of abnormality is smaller than the threshold value, and determine that the operation state of the plant 100 is abnormal when the degree of abnormality is equal to or greater than the threshold value.

By displaying the degree of abnormality as shown in fig. 4, it is possible to express whether the operation state of the plant apparatus 100 is normal or abnormal in a quantitative numerical value, and therefore even an unskilled operator who reads process data can make an accurate judgment of the operation state of the plant apparatus 100.

Fig. 5 is a flowchart of a determination process executed by the monitoring device 10 according to the present embodiment. First, the monitoring device 10 receives an input of a predicted value of the process data from a person (S10). Then, the monitoring device 10 generates a model indicating the correlation of the process data from the input predicted value (S11). This model is used in decision mode 1.

The monitoring device 10 acquires actually measured process data and stores the process data in the storage unit (S12). Next, the monitoring apparatus 10 calculates the degree of abnormality of the plant equipment 100 by the 1 st determination mode using a model generated from the predicted values of the process data input by the person, and determines the operation state of the plant equipment 100 (S13). The monitoring device 10 displays the determination result on the display unit 10f (S14). Here, the monitoring apparatus 10 may display the degree of abnormality or may display actually measured process data together with the graph M1 and the range M2.

Then, the monitoring device 10 determines whether or not the data storage amount of the actually measured process data is equal to or larger than a predetermined amount (S15). Here, the predetermined amount may be an amount to the extent that a model indicating the correlation of the process data can be generated from the actually measured process data.

If the data storage amount of the actually measured process data is not equal to or greater than the predetermined amount (no in S15), the monitoring device 10 newly acquires and stores the actually measured process data (S12), determines the operation state of the plant 100 in the 1 st determination mode (S13), and displays the determination result (S14).

On the other hand, when the data accumulation amount of the actually measured process data is equal to or larger than the predetermined amount (yes in S15), the monitoring device 10 generates a model indicating the correlation of the process data from the actually measured values of the stored process data (S16). Here, the monitoring device 10 may correct a model generated from a predicted value of process data that is not actually measured, based on an actual measured value of the process data, or may generate a new model using only the actual measured value of the process data.

Then, the monitoring device 10 acquires the actually measured process data and stores the process data in the storage unit (S17). Next, the monitoring device 10 calculates the degree of abnormality of the plant 100 in the 2 nd determination mode using the model generated from the measured values of the process data, determines the operation state of the plant 100 (S18), and displays the determination result (S19). In this case, the monitoring device 10 may display the degree of abnormality, or may display the actually measured process data together with the model. In addition, when a model generated from a predicted value of process data input by a person and a model generated from an actual measurement value of the process data can be used, the monitoring device 10 can receive a designation of which model is used. Also, the monitoring device 10 may determine the operation state of the plant apparatus 100 based on the degree of abnormality calculated by the model generated based on the predicted value of the process data input by the person and the degree of abnormality calculated by the model generated based on the measured value of the process data.

Fig. 6 is a diagram showing data points plotted by the monitoring device 10 according to the present embodiment. Fig. 6 shows an example in which the input of the hand-drawn figure G and the hand-drawn range R is accepted from a person and the data point D2 representing the input hand-drawn figure G and hand-drawn range R is drawn in the drawing area DA.

The display unit 10f of the monitoring device 10 displays the drawing area DA in which the process data is drawn. The user of the monitoring apparatus 10 inputs the graph G predicted to be related to the 1 st process data and the 2 nd process data using the input unit 10 e. And, the user inputs a hand-drawn range R including a hand-drawn figure. Here, the hand-drawn range R may be a range predicted to fall near the hand-drawn graph G with a prescribed probability for the process data.

The drawing unit 14 of the monitoring device 10 draws a data point D2 representing the hand-drawn graph G and the hand-drawn range R in the drawing area DA. The drawing unit 14 may draw the data point D2 so as to follow a normal distribution having an average value determined by the hand-drawn graph G and a variance determined by the hand-drawn range R, or may draw the data point D2 so as to follow a uniform distribution within the hand-drawn range R, for example. After the data point D2 is plotted, the model generator 12 generates a model indicating the correlation between the 1 st process data and the 2 nd process data from the data point D2.

By drawing the data point D2 representing the hand-drawn graph G in the drawing area DA after accepting the input of the hand-drawn graph G, the approximate relevance of the process data can be visualized in a hand-drawn manner, and the model can be generated.

Further, by receiving the input of the hand-drawn range R and drawing the data point D2 representing the hand-drawn graph G and the hand-drawn range R in the drawing area, the approximate relevance of the process data can be visually expressed in a hand-drawn manner, and the model can be generated.

Fig. 7 is a flowchart of the model generation process executed by the monitoring device 10 according to the present embodiment. First, the monitoring apparatus 10 receives an input of a hand-drawn pattern and a hand-drawn range from a person (S20). Then, the monitoring device 10 draws data points representing the hand-drawn figure and the hand-drawn range in the drawing area (S21).

Then, the monitoring device 10 generates a model indicating the correlation of the process data from the data points (S22). In addition, the monitoring apparatus 10 can determine the operation state of the plant 100 by the 1 st determination mode using the model thus generated.

The above-described embodiments are for easy understanding of the present invention, and are not intended to limit the present invention. The elements, arrangement, materials, conditions, shapes, dimensions, and the like of the embodiments are not limited to the examples, and can be modified as appropriate. Also, the structures shown in the different embodiments may be partially replaced or combined.

The display unit 10f of the monitoring device 10 may be a display device that receives input of process data related to plant equipment, generates a model indicating a correlation of the process data from the input process data, determines an operation state of the plant equipment in a 1 st determination mode or a 2 nd determination mode, and displays a determination result, the 1 st determination mode using the model generated from a predicted value of the process data input by a person, and the 2 nd determination mode using the model generated from an actual measurement value of the process data. The display device may display at least one of a predicted value of the process data, a generated model, and an actual measurement value of the process data, which are input from the human being, together with the determination result.

Description of the symbols

10-monitoring means, 10a-CPU, 10b-RAM, 10c-ROM, 10 d-communication section, 10 e-input section, 10 f-display section, 11-acquisition section, 12-model generation section, 13-determination section, 14-drawing section, 100-plant equipment.