Monitoring device, display device, monitoring method and monitoring program

Abstract

The present invention provides a monitoring device, a display device, a monitoring method and a monitoring program that can determine the operating state of a plant device even when the amount of process data actually measured is limited. The monitoring device (10) comprises: an input unit (10e, 11) for receiving input of process data related to the plant device; a model generating unit (12) for generating a model representing the correlation of the process data based on the input process data; a determining unit (13) for determining the operating state of the plant device through a first determining mode or a second determining mode, wherein the first determining mode uses a model generated based on a predicted value of the process data input by a person, and the second determining mode uses a model generated based on an actual measured value of the process data; and a display unit (10f) for displaying the determination result of the determining unit.

Description

Technical Field

[0001] The present invention relates to a monitoring device, a display device, a monitoring method and a monitoring program. Background Art

[0002] Conventionally, time series data (ie, process data) related to the operating status of plant equipment is measured and past process data is used as learning data to generate a model representing the correlation of the process data. The generated model is sometimes used to determine whether the plant equipment is operating normally.

[0003] For example, Patent Document 1 described below describes a learned knowledge evaluation device that compares a target value when a control device controls a controlled object with a past measured value related to the controlled object to determine whether the learned knowledge is appropriate.

[0004] Patent Document 2 below describes a demand forecasting device that creates a forecasting model for forecasting demand based on actual data of past demand and corrects demand based on actual data, future weather forecast data, and the forecasted demand.

[0005] Previous technical literature

[0006] Patent Literature

[0007] Patent Document 1: Japanese Patent Application Laid-Open No. 7-219604

[0008] Patent Document 2: Japanese Patent Application Publication No. 2018-73214 Summary of the invention

[0009] Technical issues to be solved by the invention

[0010] When using past process data as learning data to generate a model that represents the correlation of process data, it is a prerequisite to store a certain amount of actually measured process data. However, when the amount of actually measured process data is limited, such as immediately after the start-up of factory equipment, it is difficult to generate a model, and there is sometimes a period during which the operating status of the factory equipment cannot be determined.

[0011] Therefore, the present invention provides a monitoring device, a display device, a monitoring method, and a monitoring program that can determine the operating status of plant equipment even when the amount of process data actually measured is limited.

[0012] Means for solving technical problems

[0013] One embodiment of the present invention provides a monitoring device, which includes: an input unit for receiving input of process data related to factory equipment; a model generating unit for generating a model representing the correlation of process data based on the input process data; a determination unit for determining the operating status of the factory equipment through a first determination mode or a second determination mode, wherein the first determination mode uses a model generated based on a predicted value of process data input by a person, and the second determination mode uses a model generated based on an actual measured value of the process data; and a display unit for displaying the determination result of the determination unit.

[0014] According to this embodiment, the operating state of the plant equipment is determined by the first determination mode, so that even when the amount of actually measured process data is limited, a model representing the correlation of the process data can be generated to determine the operating state of the plant equipment, wherein the first determination mode uses a model generated based on the predicted value of the process data that has not been actually measured. As a result, the operating state can be determined immediately after the plant equipment is started, and downtime can be reduced.

[0015] Another embodiment of the present invention provides a display device that accepts input of process data related to factory equipment, generates a model representing the correlation of the process data based on the input process data, determines the operating status of the factory equipment through a first judgment mode or a second judgment mode, and displays the judgment result, wherein the first judgment mode uses a model generated based on a predicted value of the process data input by a person, and the second judgment mode uses a model generated based on an actual measured value of the process data.

[0016] Another embodiment of the present invention provides a monitoring method, which enables a monitoring device for monitoring factory equipment to perform the following steps: accepting input of process data related to the factory equipment; generating a model representing the correlation of the process data based on the input process data; determining the operating status of the factory equipment through a first judgment mode or a second judgment mode, wherein the first judgment mode uses a model generated based on a predicted value of the process data input by a person, and the second judgment mode uses a model generated based on an actual measured value of the process data; and displaying a judgment result based on the judgment.

[0017] Another embodiment of the present invention provides a monitoring program that performs the following steps: accepting input of process data related to factory equipment; generating a model representing the correlation of process data based on the input process data; determining the operating status of the factory equipment through a first determination mode or a second determination mode, wherein the first determination mode uses a model generated based on a predicted value of process data input by a person, and the second determination mode uses a model generated based on an actual measured value of the process data; and displaying a determination result based on the determination.

[0018] Effects of the Invention

[0019] 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 representing the correlation of process data even when the amount of process data actually measured is limited. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a diagram showing a functional block of a monitoring device according to an embodiment of the present invention.

[0021] Figure 2 It is a diagram showing the physical structure of the monitoring device involved in this embodiment.

[0022] Figure 3 This is a diagram showing a model representing the correlation of process data generated by the monitoring device according to the present embodiment.

[0023] Figure 4 It is a diagram showing the abnormality degree calculated by the monitoring device involved in this embodiment.

[0024] Figure 5 This is a flowchart of the determination process executed by the monitoring device according to the present embodiment.

[0025] Figure 6 This is a diagram showing data points plotted by the monitoring device according to the present embodiment.

[0026] Figure 7 This is a flowchart of the model generation process executed by the monitoring device according to the present embodiment. DETAILED DESCRIPTION

[0027] Embodiments of the present invention will be described with reference to the accompanying drawings. In the drawings, the same or equivalent components are denoted by the same reference numerals.

[0028] Figure 1 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 10f.

[0029] The acquisition unit 11 acquires process data related to the factory equipment 100. Here, the factory equipment 100 may be any factory equipment, for example, power generation factory equipment including boilers, incineration factory equipment, chemical factory equipment, wastewater treatment factory equipment, and other equipment that can acquire process data. In addition, the process data may be any data related to the factory equipment 100, for example, it may be data on the state of the factory equipment 100 measured by a sensor, and more specifically, it may include measured values ​​of the temperature, pressure, flow rate, etc. of the factory equipment 100. The acquisition unit 11 may acquire process data at a predetermined time interval or continuously, thereby acquiring time series data related to the factory equipment 100.

[0030] The acquisition unit 11 can acquire various process data related to the factory equipment 100. The acquisition unit 11 can acquire various process data measured by a plurality of sensors installed in the factory equipment 100. Here, the various process data may be data representing different physical quantities such as temperature and pressure, or may be data representing the same physical quantity such as temperature measured at different locations of the factory equipment 100.

[0031] The input unit 10e receives input of process data. The input unit 10e may be composed of a touch panel, a pointing device such as a mouse, or a keyboard, and may receive input of a predicted value of process data predicted by a person. The input unit 10e may receive input of a hand-drawn graphic for drawing a drawing area for drawing process data, or may receive input of a hand-drawn range including a hand-drawn graphic. The process data input by the input unit 10e will be described in detail later using the accompanying drawings.

[0032] The display unit 10f displays a plotting area for plotting process data. The display unit 10f is used to monitor the operating state of the plant 100 and can display the determination result of the operating state of the plant 100 by the monitoring device 10. The content displayed on the display unit 10f will be described in detail later using the drawings.

[0033] The model generation unit 12 generates a model representing the correlation of the process data based on the input process data. The model representing the correlation of the process data may be a model representing the range within which the process data falls when the operation state of the plant 100 is normal, or a model extracting the features represented by the process data when the operation state of the plant 100 is abnormal.

[0034] The model generation unit 12 may also generate a model based on the design value of the process data. Here, the design value of the process data refers to: a target value or a set value when the plant equipment is designed. More specifically, the design value of the process data is the value of the process data that should be measured in the design of the plant equipment 100, and is the value of the process data measured when the plant equipment 100 is operating normally. The model generation unit 12 may generate a model representing the correlation of the process data with reference to the design value of the process data that should be measured when the plant equipment 100 is operating normally. Since the model representing the correlation of the process data is generated based on the design value of the process data, even when the amount of process data actually measured is limited, a model representing the correlation of the process data can be generated to determine the operating state of the plant equipment 100.

[0035] The determination unit 13 determines the operating state of the plant equipment 100 by the first determination mode or the second determination mode, wherein the first determination mode uses a model generated based on the predicted value of the process data input by a person, and the second determination mode uses a model generated based on the measured value of the process data. Furthermore, the determination unit 13 may also determine the operating state of the plant equipment 100 by using a model generated based on the designed value of the process data. The model generated based on the predicted value of the process data input by a person and the model generated based on the measured value of the process data may be the same model with different data used in model generation, or may be different models. Furthermore, the second determination mode may be a mode in which a model generated based on the predicted value of the process data input by a person is updated based on the measured value of the process data. The model used in the second determination mode may be a model generated independently of the model used in the first determination mode, or may be a model that modifies the model used in the first determination mode.

[0036] The monitoring device 10 according to the present embodiment determines the operating state of the plant 100 through the first determination mode, and thus, even when the amount of actually measured process data is limited, a model representing the correlation of process data can be generated to determine the operating state of the plant 100, wherein the first determination mode uses a model generated based on process data that has not been actually measured. As a result, the operating state can be determined immediately after the plant 100 is started, thereby shortening the startup time when the plant 100 is newly installed and reducing the downtime when the plant 100 is temporarily stopped.

[0037] The determination unit 13 can calculate the abnormality of the operating state of the plant 100 in the first determination mode or the second determination mode based on the actually measured process data, and determine the operating state of the plant 100 based on the abnormality. An example of the abnormality calculated by the determination unit 13 will be described in detail later using the drawings.

[0038] The drawing unit 14 draws the data points representing the hand-drawn graphics received by the input unit 10e in the drawing area. In addition, the drawing unit 14 can draw the data points representing the hand-drawn graphics and the hand-drawn range received by the input unit 10e in the drawing area. The processing by the drawing unit 14 will be described in detail later using the accompanying drawings.

[0039] Figure 2 This is a diagram showing the physical structure of the monitoring device 10 involved in this embodiment. The monitoring device 10 has a CPU (Central Processing Unit) 10a equivalent to a calculation unit, a RAM (Random Access Memory) 10b equivalent to a storage unit, a ROM (Read only Memory) 10c equivalent to a storage unit, a communication unit 10d, an input unit 10e and a display unit 10f. These structures are connected via a bus so that data can be sent and received to each other. In addition, in this example, the case where the monitoring device 10 is composed of a single computer is described, but the monitoring device 10 can also be implemented by combining a plurality of computers. And, Figure 2 The illustrated configuration is an example, and the monitoring device 10 may have other configurations or may not have some of these configurations.

[0040] The CPU 10a is a control unit that performs control related to the execution of the program stored in the RAM 10b or the ROM 10c or performs data calculation and processing. The CPU 10a is a calculation unit that generates a model representing the correlation of process data based on the predicted value of the process data input by a person and uses the model to monitor the factory equipment (monitoring program). The CPU 10a receives various data from the input unit 10e or the communication unit 10d, and displays the calculation results of the data on the display unit 10f or stores them in the RAM 10b or the ROM 10c.

[0041] The RAM 10b is a storage unit in the storage unit that can rewrite data, and can be composed of, for example, a semiconductor storage element. The RAM 10b can store data such as programs executed by the CPU 10a, process data input by a person, and design values ​​of process data. In addition, these are examples, and other data can be stored in the RAM 10b, and some of these data may not be stored.

[0042] The ROM 10c is a storage unit in the storage unit from which data can be read, and can be formed of, for example, a semiconductor memory element. The ROM 10c can store, for example, a monitoring program or data that is not to be rewritten.

[0043] The communication unit 10d is an interface for connecting the monitoring device 10 to other devices. The communication unit 10d can be connected to a communication network N such as the Internet.

[0044] The input unit 10 e is used to receive data input from the user, and may include, for example, a keyboard and a touch panel.

[0045] The display unit 10f visually displays the calculation results based on the CPU 10a, and the display unit 10f can be composed of, for example, an LCD (Liquid Crystal Display). The display unit 10f can display a drawing area for drawing process data and display the process data and the generated model in the drawing area.

[0046] The monitoring program may be provided by being stored in a computer-readable storage medium (RAM 10b or ROM 10c, etc.), or may be provided via a communication network connected to the communication unit 10d. In the monitoring device 10, the CPU 10a executes the monitoring program to realize the use Figure 1 The acquisition unit 11, model generation unit 12, determination unit 13 and drawing unit 14 are described. In addition, these physical structures are examples and are not necessarily independent structures. For example, the monitoring device 10 may include a LSI (Large-Scale Integration) in which a CPU 10a and a RAM 10b or a ROM 10c are integrated.

[0047] Figure 3 This is a diagram showing a model representing the relationship between process data generated by the monitoring device 10 according to the present embodiment. Figure 3 , an example is shown in which input of predicted values ​​of process data is received from a person and a model is generated based on the predicted values ​​of the input process data (process data that has not been actually measured).

[0048] The display unit 10f of the monitoring device 10 displays a drawing area DA in which the process data is drawn. The user of the monitoring device 10 draws a data point D1 predicted to be associated with the first process data and the second process data in the drawing area DA using the input unit 10e. In addition, the data point D1 can be input by a touch panel or a pointing device, but can also be obtained from a storage unit (RAM10b, etc.) built into the monitoring device 10 or an external memory. For example, the data point D1 can be a design value of the first process data and the second process data.

[0049] The model generation unit 12 generates a model representing the correlation between the first process data and the second process data based on the input data point D1. The model may include a graph M1 representing the correlation between the first process data and the second process data, and the display unit 10f may display the graph M1 in the drawing area DA. For example, the model generation unit 12 may assume a predetermined function representing the correlation between the first process data and the second process data, and determine the parameters of the function by the least square method in such a way that the function is fitted to the data point D1. In this way, by displaying the graph M1 representing the correlation between the process data, it is possible to understand at a glance whether the generated model is appropriate.

[0050] The model may include a range M2 in which the process data falls near the graph M1 with a specified probability, and the display unit 10f may display the range M2 in the drawing area DA. The model generation unit 12 may, for example, calculate the standard deviation σ of the input data point D1, and set ±σ as the range M2 with the graph M1 as the center, or set ±2σ as the range M2 with the graph M1 as the center. In the case where the deviation of the process data follows a normal distribution, the range of ±σ is the range in which the process data falls near the graph M1 with a probability of 68.27%, and the range of ±2σ is the range in which the process data falls near the graph M1 with a probability of 95.45%. In this way, by displaying the range M2 in which the process data falls near the graph M1 with a specified probability, it is possible to grasp at a glance whether the newly acquired process data is within the normal range.

[0051] Figure 4 is a diagram showing the abnormality degree calculated by the monitoring device 10 according to the present embodiment. Figure 4 In FIG. 1 , the vertical axis represents the value of the abnormality degree, the horizontal axis represents time, and the temporal change of the abnormality degree is shown by a bar graph.

[0052] The determination unit 13 of the monitoring device 10 can calculate the abnormality of the operating state of the plant equipment 100 based on the actually measured process data by the first determination mode or the second determination mode, and display it on the display unit 10f. The determination unit 13 can calculate the abnormality by a known abnormality determination algorithm, for example, based on the average μ and variance σ of the process data actually measured in the past. 2 And through a(x)=(x-μ) 2 / σ 2 The abnormality a(x) of the current process data x is calculated. At this time, the square root of the abnormality indicates how many times the current process data deviates from the standard deviation based on the average value of the past process data. For example, if the abnormality is 25, it means that the current process data deviates from the standard deviation by 5 times the average value of the past process data. The determination unit 13 can periodically calculate the abnormality of the operating state of the plant equipment 100 and display the value as a bar graph to perform Figure 4 The display shown in the figure can also be used to periodically calculate the abnormality of the operating status of the plant equipment 100 and display the average value over a longer period as a bar graph to perform Figure 4 The display shown. Furthermore, the determination unit 13 can calculate the abnormality according to how much the actually measured process data deviates from the graph M1. For example, the determination unit 13 can calculate the abnormality according to at least one of the value indicating whether the actually measured process data is inside or outside the range M2 and the deviation amount of the actually measured process data based on the standard deviation of the data point D1.

[0053] The determination unit 13 can determine the operating state of the plant equipment 100 based on the calculated abnormality. For example, the determination unit 13 can compare a threshold value set for the abnormality with the newly calculated abnormality, and determine that the operating state of the plant equipment 100 is normal when the abnormality is less than the threshold value, and determine that the operating state of the plant equipment 100 is abnormal when the abnormality is greater than the threshold value.

[0054] Through Figure 4 The abnormality degree shown can indicate whether the operating state of the plant 100 is normal or abnormal with a quantitative value, so even an operator who is not skilled in reading process data can make an accurate judgment on the operating state of the plant 100 .

[0055] Figure 5 Flowchart of the determination process performed by the monitoring device 10 according to the present embodiment. First, the monitoring device 10 receives input of a predicted value of process data from a person (S10). Then, the monitoring device 10 generates a model representing the correlation of the process data based on the input predicted value (S11). The model is used in the first determination mode.

[0056] The monitoring device 10 acquires the process data actually measured and stores it in the storage unit (S12). Next, the monitoring device 10 calculates the abnormality of the plant equipment 100 through the first determination mode, and determines the operating state of the plant equipment 100, wherein the first determination mode uses a model generated based on the predicted value of the process data input by a person (S13). The monitoring device 10 displays the determination result on the display unit 10f (S14). Here, the monitoring device 10 can display the abnormality, or can display the process data actually measured together with the graph M1 and the range M2.

[0057] Then, the monitoring device 10 determines whether 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 representing the correlation of the process data can be generated from the actually measured process data.

[0058] If the data storage volume of the actually measured process data is not greater than the specified volume (S15: No), the monitoring device 10 newly acquires and stores the actually measured process data (S12), determines the operating state of the plant 100 through the first determination mode (S13), and displays the determination result (S14).

[0059] On the other hand, if the data accumulation amount of the actually measured process data is equal to or larger than the predetermined amount (S15: Yes), the monitoring device 10 generates a model representing the correlation of the process data based on the actually measured values ​​of the stored process data (S16). Here, the monitoring device 10 may correct the model generated based on the predicted values ​​of the process data that is not actually measured based on the actually measured values ​​of the process data, or may generate a new model using only the actually measured values ​​of the process data.

[0060] Then, the monitoring device 10 acquires the process data actually measured and stores it in the storage unit (S17). Next, the monitoring device 10 calculates the abnormality of the plant equipment 100 through the second determination mode, determines the operating state of the plant equipment 100 (S18) and displays the determination result (S19), wherein the second determination mode uses a model generated based on the actual measured value of the process data. At this time, the monitoring device 10 may also display the abnormality, or may display the process data actually measured together with the model. In addition, in the case where a model generated based on the predicted value of the process data input by a person and a model generated based on the actual measured value of the process data can be used, the monitoring device 10 may accept a designation of which model to use. Furthermore, the monitoring device 10 may determine the operating state of the plant equipment 100 based on the abnormality calculated by the model generated based on the predicted value of the process data input by a person and the abnormality calculated by the model generated based on the actual measured value of the process data.

[0061] Figure 6 is a diagram showing data points plotted by the monitoring device 10 according to this embodiment. Figure 6 2 shows an example in which an input of a hand-drawn graphic G and a hand-drawn range R is accepted from a person and data points D2 representing the input hand-drawn graphic G and hand-drawn range R are drawn in the drawing area DA.

[0062] The display unit 10f of the monitoring device 10 displays a drawing area DA where process data is drawn. The user of the monitoring device 10 uses the input unit 10e to input a graph G predicted to be a correlation between the first process data and the second process data. In addition, the user inputs a hand-drawn range R including the hand-drawn graph. Here, the hand-drawn range R may be a range where process data is predicted to fall near the hand-drawn graph G with a predetermined probability.

[0063] The drawing unit 14 of the monitoring device 10 draws the 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 in a manner following 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 in a manner following a uniform distribution within the hand-drawn range R. After drawing the data point D2, the model generation unit 12 generates a model representing the correlation between the first process data and the second process data based on the data point D2.

[0064] By accepting the input of the hand-drawn graph G and drawing the data point D2 representing the hand-drawn graph G in the drawing area DA, the approximate correlation of the process data can be intuitively displayed in a hand-drawn manner, and a model can be generated.

[0065] Furthermore, by accepting 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 correlation of the process data can be intuitively displayed in a hand-drawn manner, and a model can be generated.

[0066] Figure 7 1 is a flowchart of the model generation process performed by the monitoring device 10 according to the present embodiment. First, the monitoring device 10 receives input of a hand-drawn figure 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).

[0067] Then, the monitoring device 10 generates a model indicating the correlation of the process data based on the data points (S22). In addition, the monitoring device 10 can determine the operating state of the plant 100 by using the first determination mode of the model generated in this way.

[0068] In addition, the above-described embodiments are used to facilitate understanding of the present invention and are not used to limit the present invention. The various elements and their configurations, materials, conditions, shapes, and sizes of the embodiments are not limited to illustrative examples and can be appropriately changed. Furthermore, the structures shown in different embodiments can be partially replaced or combined.

[0069] 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 the correlation of the process data based on the input process data, determines the operating state of the plant equipment by a first determination mode or a second determination mode, and displays the determination result, wherein the first determination mode uses a model generated based on a predicted value of process data input by a person, and the second determination mode uses a model generated based on an actual value of the process data. The display device may also display at least one of the predicted value of the process data received from a person, the generated model, and the actual value of the process data, together with the determination result.

[0070] Explanation of symbols

[0071] 10-monitoring device, 10a-CPU, 10b-RAM, 10c-ROM, 10d-communication unit, 10e-input unit, 10f-display unit, 11-acquisition unit, 12-model generation unit, 13-determination unit, 14-drawing unit, 100-factory equipment.

Claims

1. A monitoring device, characterized in that: have: An input unit, which receives input of process data related to factory equipment; a model generating unit for generating a model representing the correlation of the process data based on the input process data; a determination unit that determines the operating state of the plant equipment by a first determination mode or a second determination mode, wherein the first determination mode uses the model generated based on a value corresponding to the process data that has not been actually measured, and the second determination mode uses the model generated based on an actually measured value of the process data; and a display unit for displaying the determination result of the determination unit; When the numerical value of the actual measurement value is smaller than a predetermined numerical value, the determination unit performs determination in the first determination mode.

2. The monitoring device according to claim 1, characterized in that The determination unit calculates the abnormality of the operating state of the plant equipment in the first determination mode or the second determination mode based on the actually measured process data, and determines the operating state of the plant equipment based on the abnormality.

3. The monitoring device according to claim 1 or 2, characterized in that: The model includes a graph representing the relationships of the process data, The display unit displays the graph.

4. The monitoring device according to claim 1 or 2, characterized in that: The model generation unit generates the model using a value input by a person as a value corresponding to the process data that has not been actually measured.

5. The monitoring device according to claim 1 or 2, characterized in that: The model generation unit generates the model using design values ​​of the process data as values ​​corresponding to the process data that have not been actually measured.

6. The monitoring device according to claim 1 or 2, characterized in that: The input unit receives an input of a hand-drawn graphic 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 graphic in the drawing area. The model generation unit generates the model based on the data points.

7. The monitoring device according to claim 6, characterized in that The input unit receives an input of a hand-drawn range for the drawing area, the hand-drawn range including the hand-drawn figure, The drawing unit draws data points representing the hand-drawn graphic and the hand-drawn range in the drawing area.

8. A display device, characterized in that: An input of process data related to factory equipment is accepted, and a model representing the correlation of the process data is generated based on the input process data, and the operating status of the factory equipment is determined through a first determination mode or a second determination mode, and the determination result is displayed, wherein the first determination mode uses the model generated based on a value equivalent to the process data that has not been actually measured, and the second determination mode uses the model generated based on an actual measured value of the process data, and when the numerical value of the actual measured value is less than a specified numerical value, the determination is performed through the first determination mode.

9. A monitoring method, characterized in that: The monitoring device for monitoring the factory equipment executes the following steps: Accepts input of process data related to plant equipment; generating a model representing the correlation of the process data based on the input process data; determining the operating state of the plant equipment by a first determination mode or a second determination mode, wherein the first determination mode uses the model generated based on a value corresponding to the process data that has not been actually measured, and the second determination mode uses the model generated based on an actually measured value of the process data; and displaying a determination result based on the determination, When the numerical value of the actual measurement value is smaller than a predetermined numerical value, the result of determination in the first determination mode is displayed.

10. A storage medium for storing a monitoring program, characterized in that: The monitoring program is executed by a monitoring device that monitors factory equipment to perform the following steps: Accepts input of process data related to plant equipment; generating a model representing the correlation of the process data based on the input process data; The operating state of the plant equipment is determined by a first determination mode or a second determination mode, wherein the first determination mode uses a non-actually measured value corresponding to the process data. value The model generated, the second determination mode uses the model generated based on the actual measurement value of the process data; and displaying a determination result based on the determination, When the numerical value of the actual measurement value is smaller than a predetermined numerical value, the result of determination in the first determination mode is displayed.

Images