Measurement device, measurement method, and measurement program

Abstract

To enable provision of acquired measurement data at an effective time.SOLUTION: A field device 10 disclosed herein is configured to generate and store measurement data based on a measured signal and, upon detection of an event, acquire the stored measurement data and display the data on a display device 20 according to event information regarding the event for outputting the measurement data to the display device 20.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a measurement device, a measurement method, and a measurement program. [Background technology]

[0002] 2. Description of the Related Art There is known a technique for a field device that sequentially stores waveform data when measuring a flow rate in a plant and displays the stored waveform data as a waveform on the screen of a portable terminal, a control system, or the like. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2019-045320 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, with the above-mentioned technology, it is difficult to effectively output the waveform data acquired in the plant. For example, with the above-mentioned technology, the timing for outputting the waveform data to a portable terminal, a control system, etc. is limited.

[0005] An object of the present invention is to provide acquired measurement data at an effective timing. [Means for solving the problem]

[0006] The present invention provides a measuring instrument comprising an acquisition unit that acquires measurement data and stores it in a memory unit, and an output unit that acquires the measurement data from the memory unit and outputs it to an external device when it detects the occurrence of an event based on event information relating to the event for outputting the measurement data to an external device.

[0007] The present invention also provides a measurement method in which a computer acquires and retains measurement data, and when it detects the occurrence of an event based on event information relating to the event in which the measurement data is output to an external device, acquires the retained measurement data and outputs it to the external device.

[0008] The present invention also provides a measurement program that causes a computer to execute a process of acquiring and retaining measurement data, and when an occurrence of an event is detected based on event information relating to an event in which the measurement data is output to an external device, acquiring the retained measurement data and outputting it to the external device. Effect of the Invention

[0009] According to the present invention, it is possible to provide acquired measurement data at an effective timing. [Brief description of the drawings]

[0010] [Figure 1] 1 is a diagram illustrating a configuration example and a processing example of an information processing system according to a first embodiment. [Diagram 2] 1 is a block diagram showing an example of the configuration of each device in an information processing system according to a first embodiment. [Diagram 3] 4 is a diagram illustrating an example of a waveform data storage unit of the field device according to the first embodiment. FIG. [Figure 4] 4 is a diagram illustrating an example of a device setting information storage unit of the field device according to the first embodiment; FIG. [Diagram 5] FIG. 4 is a diagram showing a specific example of data processing by the field device according to the first embodiment. [Figure 6] FIG. 2 is a diagram illustrating an example of a hardware configuration according to the first embodiment. [Figure 7] 4 is a flowchart showing an example of a processing flow of the information processing system according to the first embodiment. [Figure 8] 11 is a diagram illustrating a configuration example and a processing example of an information processing system according to a second embodiment. FIG. [Figure 9]FIG. 11 is a block diagram showing an example of the configuration of each device in an information processing system according to a second embodiment. [Figure 10] FIG. 11 is a diagram illustrating an example of an estimation result storage unit of the server device according to the second embodiment. [Figure 11] FIG. 11 is a diagram illustrating an example of an estimation model storage unit of a server device according to the second embodiment. [Figure 12] FIG. 11 is a diagram illustrating an example of data processing by the server device according to the second embodiment. [Figure 13] FIG. 11 is a diagram illustrating an example of a hardware configuration according to a second embodiment. [Figure 14] 10 is a flowchart showing an example of a processing flow of an information processing system according to a second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] An information processing system, an information processing method, and an information processing program according to first and second embodiments of the present invention will be described in detail below with reference to the drawings. Note that the present invention is not limited to the embodiments described below.

[0012] [Embodiment 1] The configuration and processing of an information processing system 100-1 according to the first embodiment, the configuration and processing of each device of the information processing system 100-1, and the processing flow of the information processing system 100-1 will be described below in this order, and finally the effects of the first embodiment will be described.

[0013] 1. Configuration and Processing of Information Processing System 100-1 The configuration and processing of the information processing system 100-1 according to the embodiment 1 will be described in detail with reference to Fig. 1. Fig. 1 is a diagram showing an example of the configuration and processing of the information processing system 100-1 according to the embodiment 1. In the following, an example of the overall configuration of the information processing system 100-1, an example of the processing of the information processing system 100, and the effects of the information processing system 100-1 will be described.

[0014] (1-1. Example of the Overall Configuration of Information Processing System 100-1) An example of the overall configuration of an information processing system 100-1 according to the first embodiment will be described. The information processing system 100-1 has a field device 10, which is a measuring device, and a display device 20 (terminal device 20A, control device 20B) used by an operator O. Here, the field device 10 and the display device 20 are connected to each other via a predetermined communication network (not shown) so as to be able to communicate with each other by wire or wirelessly. Note that the predetermined communication network can be various communication networks such as the Internet or a dedicated line. The information processing system 100-1 shown in FIG. 1 may include a plurality of field devices 10.

[0015] (1-1-1. Field device 10) The field device 10 is a plant device that is installed in a plant and acquires waveform data. In the example of Fig. 1, the field device 10 is realized by a vortex flowmeter that acquires vortex waveform data.

[0016] (1-1-2.Display equipment 20) The display device 20 is a plant device having a display function that is installed in a plant and used by an operator O who manages the plant. In the example of Fig. 1, the display device 20 is realized by a terminal device 20A that is a portable terminal, and a control device 20B that is included in a control system such as a distributed control system (DCS).

[0017] (1-2. Example of processing of the entire information processing system 100-1) An example of the overall processing of the information processing system 100-1 according to the embodiment 1 will be described. Note that the processing of steps S1 to S4 below may be executed in a different order. Also, some of the processing of steps S1 to S4 below may be omitted.

[0018] (1-2-1. Waveform data acquisition process) First, the field device 10 acquires waveform data (step S1). For example, the field device 10 acquires waveform data during flow rate measurement. At this time, the field device 10 stores the acquired waveform data.

[0019] (1-2-2. Event detection processing) Secondly, the field device 10 detects an event (step S2). For example, the field device 10 detects an alarm generated by the field device 10 due to a piping abnormality. The field device 10 also detects a regular inspection time set by the operator O for the field device 10.

[0020] (1-2-3. Waveform data transmission process) Thirdly, the field device 10 transmits the waveform data to the display device 20 (step S3). For example, when the field device 10 detects an event, the field device 10 refers to the stored waveform data, acquires waveform data for a predetermined period from the occurrence of the event, and transmits the waveform data to the display device 20. At this time, the field device 10 may transmit device setting information indicating the measurement conditions of the field device 10 to the display device 20.

[0021] (1-2-4. Waveform screen display processing) Fourth, the display device 20 displays a waveform screen (step S4). For example, the display device 20 displays a time series graph showing the waveform of the waveform data for a predetermined period received from the field device 10 on the monitor screen of the display device 20. At this time, the operator O infers detailed causes such as piping abnormality, pump abnormality, sensor abnormality, resonance, electrical noise, etc., based on differences in the waveform patterns shown in the time series graph.

[0022] (1-2-5.Others) The information processing system 100-1 according to the first embodiment can also execute the processing of the information processing system 100-2 according to the second embodiment, which will be described later. That is, in the information processing system 100-1, the server device 30 according to the second embodiment can estimate a detailed cause from the waveform data in the processing of step S4 described above. At this time, the server device 30 estimates a detailed cause of a piping abnormality, a pump abnormality, a sensor abnormality, resonance, electrical noise, etc. of the field device 10 using an estimation model EM, which is a machine learning model and outputs a detailed cause of a predetermined event that occurs in a plant in response to input of waveform data and device setting information.

[0023] (1-3. Effects of Information Processing System 100-1) In the following, an overview and problems of the reference technology will be described, and then the effects of the information processing system 100-1 will be described.

[0024] (1-3-1. Overview of reference technology and issues) Here, an overview of a reference technique for acquiring waveform data in a plant will be described. The following describes the processing of a field device 10P according to the reference technique, which is a vortex flowmeter and acquires vortex waveform data.

[0025] The field device 10P is composed of a piezoelectric element, a charge amplifier, an A / D conversion section, a frequency analysis section, a Schmitt trigger circuit, a microprocessor, an output circuit, and a waveform acquisition memory.

[0026] First, the field device 10P detects the alternating lift force generated by the Karman vortex with a piezoelectric element. Second, the field device 10P converts the detected charge signal into a voltage signal with a charge amplifier. Third, the field device 10P performs analog-to-digital conversion with an A / D conversion unit. Fourth, the field device 10P processes the converted signal in a frequency analysis unit and extracts a vortex signal. Fifth, the field device 10P pulses the extracted vortex signal with a Schmitt trigger circuit. Sixth, the field device 10P counts the pulse signal as pulses for a certain period instructed by the microprocessor in a counting circuit, and performs frequency calculations, flow rate calculations, correction calculations, etc. with the microprocessor to output a pulse signal equivalent to a flow rate signal. Seventh, the field device 10P converts the output signal into the desired form (4 to 20 mA analog instrumentation signal, digital instrumentation signal) with an output circuit and outputs it. Eighth, the field device 10P sequentially stores the vortex waveform data output from the frequency analysis unit during flow measurement in the waveform acquisition memory. Ninth, the field device 10P reads out the waveform data captured in the waveform acquisition memory using a microprocessor, and transmits it via an output circuit to the display device 20, which is an external device. Tenth, the display device 20 displays the vortex waveform data on its own monitor screen using software that displays the data as a waveform.

[0027] However, the field device 10P according to the reference technology has a problem in that the timing for outputting the acquired waveform data to the display device 20 is limited. For example, it is difficult for the field device 10P to output the waveform data to the display device 20 when an event such as an alarm occurs.

[0028] (1-3-2. Overview of Information Processing System 100-1) The information processing system 100-1 includes a field device 10, which is a measuring device, and a display device 20 used by an operator O. First, the field device 10 acquires waveform data and stores the acquired waveform data. Second, the field device 10 detects events such as an alarm occurrence or a regular inspection time. Third, when the field device 10 detects an event, it refers to the stored waveform data, acquires waveform data for a predetermined period from the occurrence of the event, and transmits the waveform data to the display device 20. Fourth, the display device 20 displays, as a waveform screen, a time series graph showing the waveform of the waveform data for a predetermined period received from the field device 10 on a monitor screen.

[0029] In the information processing system 100-1, the field device 10 can also transmit the received waveform data to a server device 30 (see FIG. 8) that estimates a detailed cause of an alarm by inputting the received waveform data into an estimation model EM. That is, in the information processing system 100-1, the field device 10 may be realized in combination with a server device 30, which will be described later in the second embodiment.

[0030] (1-3-3. Effects) In the information processing system 100-1, the field device 10 detects an event that occurs in the plant, and is able to output acquired waveform data to the display device 20 not only at the timing requested by the operator O, but also when a specific alarm occurs or at a specific time.

[0031] As described above, in the information processing system 100-1, the field device 10 can provide the waveform data acquired in the plant at an effective timing.

[0032] 2. Configuration and Processing of Each Device in Information Processing System 100-1 2 to 6, configuration examples and processing examples of each device included in the information processing system 100-1 shown in Fig. 1 will be described. In the following, after describing an example of the overall configuration of the information processing system 100-1 according to the first embodiment, configuration examples and processing examples of the field device 10 and the display device 20 will be described in detail.

[0033] (2-1. Example of the Overall Configuration of Information Processing System 100-1) An example of the overall configuration of the information processing system 100-1 shown in FIG. 1 will be described with reference to FIG. 2. FIG. 2 is a block diagram showing an example of the configuration of each device of the information processing system 100-1 according to the first embodiment. As shown in FIG. 2, the information processing system 100-1 has a field device 10 which is a measuring device, and a display device 20 which is a user terminal. The field device 10 is communicatively connected to the display device 20 via a communication network N which is realized by the Internet, a dedicated line, or the like. The field device 10 is installed in a plant. In addition, the display device 20 is installed in the plant.

[0034] (2-2. Example of configuration and processing of field device 10) An example of the configuration and processing of the field device 10 shown in Fig. 1 will be described with reference to Fig. 2. The field device 10 includes an input unit 11, a display unit 12, a communication unit 13, a storage unit 14, and a control unit 15.

[0035] (2-2-1. Input section 11) The input unit 11 controls input of various information to the field device 10. For example, the input unit 11 is realized by a mouse, a keyboard, etc., and accepts input of setting information for the field device 10, etc.

[0036] (2-2-2.Display section 12) The display unit 12 controls the output of various information from the field device 10. For example, the display unit 12 is realized by a display or the like, and outputs setting information stored in the field device 10, and the like.

[0037] (2-2-3. Communications Department 13) The communication unit 13 manages data communication with other devices. For example, the communication unit 13 performs data communication with each communication device via a router, etc. The communication unit 13 can also perform data communication with an operator's terminal (not shown).

[0038] (2-2-4. Storage section 14) The storage unit 14 stores various information referred to when the control unit 15 operates and various information acquired when the control unit 15 operates. The storage unit 14 has a waveform data storage unit 14a and a device setting information storage unit 14b. Here, the storage unit 14 can be realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk or an optical disk. In the example of FIG. 2, the storage unit 14 is installed inside the field device 10, but it may be installed outside the field device 10, or multiple storage units may be installed.

[0039] (2-2-4-1. Waveform data storage unit 14a) The waveform data storage unit 14a stores waveform data. For example, the waveform data storage unit 14a stores waveform data acquired by an acquisition unit 15a of the control unit 15, which will be described later. An example of data stored in the waveform data storage unit 14a will be described with reference to FIG. 3. FIG. 3 is a diagram showing an example of the waveform data storage unit 14a of the field device 10 according to the first embodiment. In the example of FIG. 3, the waveform data storage unit 14a has items such as "field device" and "waveform data."

[0040] The "field device" indicates identification information for identifying the field device 10 which is a plant device, such as an identification number or an identification symbol of the vortex flowmeter. The "waveform data" is waveform data acquired by the field device 10 which is a plant device, such as vortex waveform data at the time of flow measurement by the vortex flowmeter.

[0041] That is, FIG. 3 shows an example in which data such as "waveform data FI001-W1," "waveform data FI001-W2," "waveform data FI001-W3," ... are stored in the waveform data storage unit 14a as "waveform data" acquired by the field device 10 identified by "field device FI001."

[0042] (2-2-4-2.Device setting information storage section 14b) The device setting information storage unit 14b stores device setting information. For example, the device setting information storage unit 14b stores device setting information of the field device 10 input by the operator O. An example of data stored in the device setting information storage unit 14b will be described with reference to FIG. 4. FIG. 4 is a diagram showing an example of the device setting information storage unit 14b of the field device 10 according to the first embodiment. In the example of FIG. 4, the device setting information storage unit 14b has items such as "field device" and "device setting information".

[0043] "Field device" refers to identification information for identifying the field device 10, which is a plant device, such as an identification number or an identification symbol of the vortex flowmeter. "Device setting information" refers to information related to the measurement settings of the field device 10, which is a plant device, such as measurement conditions set for each device, such as the measurement fluid, bore size, etc.

[0044] That is, FIG. 4 shows an example in which data such as "equipment setting information FI001-D1," "equipment setting information FI001-D2," "equipment setting information FI001-D3," etc. are stored in the equipment setting information storage unit 14b as "equipment setting information" set for the field equipment 10 identified by "field equipment FI001."

[0045] (2-2-5. Control unit 15) The control unit 15 controls the entire field device 10. The control unit 15 includes an acquisition unit 15a, a detection unit 15b, and an output unit 15c. Here, the control unit 15 can be realized by, for example, an electronic circuit such as a central processing unit (CPU) or a micro processing unit (MPU), or an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

[0046] (2-2-5-1. Acquisition part 15a) The acquiring unit 15a executes a waveform data acquiring process. For example, the acquiring unit 15a acquires measurement data and stores it in the memory unit 14. At this time, the acquiring unit 15a generates waveform data as the measurement data from a vortex signal measured during piping vibration of the vortex flowmeter. The acquiring unit 15a also generates the measurement data based on a signal measured in a plant. The acquiring unit 15a stores the generated waveform data in the waveform data memory unit 14a.

[0047] To explain a specific example, when field device 10 is a vortex flowmeter identified by "field device FI001," acquisition unit 15a measures the vortex signal when field device 10 vibrates through the piping, acquires "waveform data FI001-W1," "waveform data FI001-W2," "waveform data FI001-W3," ... as waveform data generated from the measured vortex signal at predetermined intervals, and stores the data in waveform data memory unit 14a.

[0048] (2-2-5-2. Detection unit 15b) The detection unit 15b executes an event detection process. For example, the detection unit 15b detects the occurrence of an event based on event information related to an event in which measurement data is output to an external device. At this time, the detection unit 15b detects the occurrence of an alarm as the occurrence of an event. The detection unit 15b also detects the time of a regular inspection as the occurrence of an event.

[0049] Here, the event information is information that defines an event in which the field device 10 outputs measurement data to an external device such as the display device 20 or the server device 30, and includes, for example, information such as the occurrence of an alarm in the field device 10, the time of regular inspection of the field device 10, and the time set by the operator O.

[0050] To explain a specific example, when the field device 10 is a vortex flowmeter identified by "field device FI001", the event information stored in the memory unit 14 of the field device is "field device FI001-A1" indicating the type of alarm of the field device 10, and the waveform data of the field device 10 exceeds a threshold value and an alarm indicated by "field device FI001-A1" occurs, the detection unit 15b detects the alarm event "field device FI001-A1" as the occurrence of an event.

[0051] Furthermore, when the field device 10 is a vortex flowmeter identified by "field device FI001" and the event information stored in the memory unit 14 of the field device is "field device FI001-P1 (10:00:00-11:00:00)" indicating the time of regular inspection of the field device 10, which is "10:00:00", the start time of regular inspection indicated by "field device FI001-P1 (10:00:00-11:00:00)", the detection unit 15b detects the regular inspection event "field device FI001-P1 (10:00:00-11:00:00)" as the occurrence of an event.

[0052] (2-2-5-3. Output section 15c) The output unit 15c executes a waveform data output process. For example, when the detection unit 15b detects the occurrence of an event, the output unit 15c acquires measurement data from the storage unit 14 and outputs the measurement data to an external device. At this time, when the occurrence of an event is detected, the output unit 15c acquires measurement data for a predetermined period from the time of the occurrence of the event from the storage unit 14. Furthermore, when the occurrence of an alarm is detected as the occurrence of an event, the output unit 15c acquires measurement data from the storage unit 14. Furthermore, when the periodic inspection time is detected as the occurrence of an event, the output unit 15c acquires measurement data from the storage unit 14.

[0053] To explain a specific example, when the waveform data of the field device 10 becomes equal to or exceeds the threshold value and the occurrence of an alarm indicated by the event information "field device FI001-A1" is detected, the output unit 15c acquires the waveform data "waveform data FI001-W2" from the time when the waveform data became equal to or exceeds the threshold value to the time when the waveform data became less than the threshold value by referring to the waveform data storage unit 14a.

[0054] Furthermore, when the regular inspection start time indicated in the event information "field device FI001-P1 (10:00:00-11:00:00)" reaches "10:00:00", the output unit 15c acquires waveform data "waveform data FI001-W3" from the regular inspection start time "10:00:00" to the regular inspection end time "11:00:00" by referring to the waveform data storage unit 14a.

[0055] The output unit 15c also displays a time series graph showing the waveform of the waveform data for a predetermined period on an external device. At this time, the output unit 15c transmits the waveform data for the predetermined period to the display device 20, which is an external device, and displays the time series graph showing the waveform on the monitor screen of the display device 20.

[0056] To explain a specific example, the output unit 15c displays a time series graph showing the waveform of the waveform data "waveform data FI001-W3" from the regular inspection start time "10:00:00" to the regular inspection end time "11:00:00" on the monitor screen of the display device 20 used by the operator O.

[0057] (2-3. Specific example of data processing of field device 10) A specific example of data processing by the field device 10 will be described with reference to Fig. 5. Fig. 5 is a diagram showing a specific example of data processing by the field device 10 according to the first embodiment.

[0058] The field device 10 is composed of a piezoelectric element, a charge amplifier, an A / D converter, a frequency analyzer, a Schmitt trigger circuit, a microprocessor (CPU), an output circuit, a waveform acquisition memory, and an event switch (SW). The above components are included in the input unit 11, display unit 12, communication unit 13, storage unit 14, control unit 15, etc. of the field device 10 described above, and are a configuration that enables the execution of various processes.

[0059] The following describes the waveform acquisition process executed by the field device 10. First, the field device 10 transmits a waveform data acquisition command when an event switch in the CPU is turned on due to a condition of an arbitrary timing, a specific alarm, or a specific time.

[0060] Secondly, the field device 10 detects the alternating lift force generated by the Karman vortex with a piezoelectric element. Thirdly, the field device 10 converts the detected charge signal into a voltage signal with a charge amplifier. Fourthly, the field device 10 performs analog-to-digital conversion with an A / D conversion unit. Fifthly, the field device 10 processes the converted signal in a frequency analysis unit and extracts a vortex signal. Sixthly, the field device 10 pulses the extracted vortex signal with a Schmitt trigger circuit. Seventhly, the field device 10 counts the pulse signal as a pulse for a certain period instructed by the microprocessor in a counting circuit, and performs frequency calculation, flow rate calculation, correction calculation, etc. with the microprocessor, and outputs it as a pulse signal corresponding to a flow rate signal. Eighthly, the field device 10 converts the output signal into a desired form (4 to 20 mA analog instrumentation signal, digital instrumentation signal) with an output circuit, and outputs it. Ninthly, the field device 10 sequentially stores the vortex waveform data output from the frequency analysis unit during flow rate measurement in a waveform acquisition memory. Tenth, the field device 10 reads out the waveform data captured in the waveform acquisition memory using a microprocessor, and transmits the data to an external device, the display device 20, via an output circuit. Eleventh, the display device 20 displays the vortex waveform data on its monitor screen using software that displays the data as a waveform.

[0061] The field device 10 executes the process of acquiring the waveform data through the above-mentioned second to eleventh processes, and after acquiring the waveform data, turns off the event switch to complete the waveform acquisition process.

[0062] To explain a specific example, the field device 10 turns on the event switch when a high vibration alarm occurs, i.e., when an abnormal vibration in the piping is detected, so that the waveform data at that time can be acquired. The field device 10 also transmits the acquired waveform data and device setting information to a display device 20 such as a portable terminal or a control system. At this time, the field device 10 can also transmit the acquired waveform data and device setting information to a server device 30, which will be described later in the second embodiment, via the display device 20.

[0063] (2-4. Example of hardware configuration of field device 10) An example of a hardware configuration of the field device 10 will be described with reference to Fig. 6. Fig. 6 is a diagram showing an example of a hardware configuration according to the first embodiment. As shown in Fig. 6, the field device 10 includes a communication device 10a, an EEPROM (Electrically Erasable Programmable Read-Only Memory) 10b, a memory 10c, and a processor 10d. The components shown in Fig. 6 are connected to each other via a bus or the like.

[0064] The communication device 10a is a network interface card or the like, and communicates with other servers. The EEPROM 10b stores programs and DBs that operate the functions shown in FIG.

[0065] The processor 10d reads out a program that executes the same processes as the respective processing units shown in Fig. 2 from the EEPROM 10b etc. and loads it in the memory 10c, thereby operating a process that executes each function described in Fig. 2. For example, this process executes a function similar to that of each processing unit of the field device 10. Specifically, the processor 10d reads out a program having functions similar to those of the acquiring unit 15a, the detecting unit 15b, the output unit 15c etc. from the EEPROM 10b etc. Then, the processor 10d executes a process that executes the same processes as those of the acquiring unit 15a, the detecting unit 15b, the output unit 15c etc.

[0066] In this way, the field device 10 operates as a device that executes various processing methods by reading and executing a program. The field device 10 can also realize functions similar to those of the first embodiment by reading the program from a recording medium using a medium reading device and executing the read program. Note that the program in these other embodiments is not limited to being executed by the field device 10. For example, the present invention can be similarly applied to cases where another computer or server executes a program, or where these cooperate to execute a program.

[0067] This program can be distributed via a network such as the Internet. This program can also be recorded on a computer-readable recording medium such as a hard disk, a solid-state drive (SSD), a flexible disk (FD), a CD-ROM, a magneto-optical disk (MO), or a digital versatile disk (DVD), and can be executed by being read from the recording medium by a computer.

[0068] (2-5. Configuration and Processing Examples of Display Device 20) A configuration example and a processing example of the display device 20 will be described with reference to Fig. 2. The display device 20 is a device used by an operator O who manages a plant, and is, for example, a terminal device 20A, a control device 20B, etc. The display device 20 is a device having a screen display function, and displays various information.

[0069] (2-5-1. Terminal device 20A) The terminal device 20A may be realized, for example, by a smartphone, a tablet terminal, a desktop PC (Personal Computer), a notebook PC, a smart watch, a mobile phone, a PDA (Personal Digital Assistant), etc., and displays the waveform data received from the field device 10 on a monitor screen.

[0070] (2-5-2. Control device 20B) The control device 20B is realized by, for example, a Human Interface Station (HIS) constituting a DCS, and displays the waveform data received from the field device 10 on a monitor screen.

[0071] 3. Processing Flow of Information Processing System 100-1 A process flow of the information processing system 100-1 according to the first embodiment will be described with reference to Fig. 7. Fig. 7 is a flowchart showing an example of a process flow of the information processing system 100-1 according to the first embodiment. Note that the processes of steps S101 to S104 below may be executed in a different order. Also, some of the processes of steps S101 to S104 below may be omitted.

[0072] (3-1. Waveform data acquisition process) First, the field device 10 executes a waveform data acquisition process (step S101). For example, the field device 10 acquires waveform data during flow rate measurement, and stores the acquired waveform data.

[0073] (3-2. Event detection processing) Secondly, the field device 10 executes an event detection process (step S102). For example, the field device 10 detects an alarm generated by the field device 10 due to a piping abnormality, or a periodic inspection time set by the operator O for the field device 10.

[0074] (3-3. Waveform data transmission processing) Third, the field device 10 executes a waveform data transmission process (step S103). For example, when the field device 10 detects an event, the field device 10 refers to the stored waveform data, acquires the waveform data for a predetermined period from the occurrence of the event, and transmits the waveform data to the display device 20.

[0075] (3-4. Waveform screen display processing) Fourth, the display device 20 executes a waveform screen display process (step S104). For example, the display device 20 displays a time series graph showing the waveform of the waveform data received from the field device 10 for a predetermined period on the monitor screen of the display device 20.

[0076] 4. Effects of the First Embodiment Finally, a description will be given of the effects of the first embodiment. Below, effects 1 to 8 corresponding to the processing according to the first embodiment will be described.

[0077] (4-1. Effect 1) First, in the process according to the above-described first embodiment, the field device 10 acquires and stores measurement data, and when it detects the occurrence of an event based on event information related to the event for outputting the measurement data to the display device 20, acquires the stored measurement data and outputs it to the display device 20. Therefore, in the process according to the first embodiment, it is possible to provide the acquired measurement data at an effective timing.

[0078] (4-2. Effect 2) Secondly, in the process according to the above-mentioned embodiment 1, the field device 10 generates waveform data as measurement data from a vortex signal measured during piping vibration of the vortex flowmeter. Therefore, in the process according to the embodiment 1, the acquired vortex waveform data can be provided at an effective timing.

[0079] (4-3. Effect 3) Thirdly, in the process according to the above-described first embodiment, when the field device 10 detects the occurrence of an event, the field device 10 acquires measurement data for a predetermined period from the time point when the event occurs. Therefore, in the process according to the first embodiment, the acquired measurement data can be provided at the timing when the event occurs.

[0080] (4-4. Effect 4) Fourth, in the process according to the above-described first embodiment, the field device 10 acquires measurement data when detecting the occurrence of an alarm as the occurrence of an event. Therefore, in the process according to the first embodiment, the acquired measurement data can be provided at the timing when the alarm occurs.

[0081] (4-5. Effect 5) Fifth, in the process according to the above-described first embodiment, the field device 10 acquires measurement data when detecting the time of regular inspection as the occurrence of an event. Therefore, in the process according to the first embodiment, the acquired measurement data can be provided at a preventive timing before an abnormality occurs.

[0082] (4-6. Effect 6) Sixth, in the process according to the above-described first embodiment, the field device 10 displays a time series graph showing the waveform of the waveform data for a predetermined period on the display device 20. Therefore, in the process according to the first embodiment, the acquired measurement data can be provided at an effective timing and in a format that is easy for the operator O to understand.

[0083] (4-7. Effect 7) Seventh, in the process according to the above-described first embodiment, the field device 10 generates measurement data based on a signal measured in a plant. Therefore, in the process according to the first embodiment, the acquired measurement data can be provided at an effective timing in a plant where safety is required.

[0084] (4-8. Effect 8) Eighth, in the process according to the above-mentioned embodiment 1, the server device 30 receives measurement data from the field device 10 when a predetermined event occurs in the field device 10, inputs the received measurement data into a trained estimation model EM trained to output information related to the cause of the predetermined event in response to the input of the measurement data, estimates the cause of the predetermined event, and outputs the estimated cause of the predetermined event. Therefore, in the process according to embodiment 1, the acquired measurement data can be provided at an effective timing to the server device 30, which effectively provides information related to the field device 10 from the measurement data.

[0085] [Embodiment 2] The configuration and processing of an information processing system 100-2 according to the second embodiment, the configuration and processing of each device of the information processing system 100-2, and the processing flow of the information processing system 100-2 will be described below in order, and finally, the effects of the second embodiment will be described. Note that the description of the configuration and processing common to the first embodiment will be omitted.

[0086] 1. Configuration and Processing of Information Processing System 100-2 The configuration and processing of the information processing system 100-2 according to the second embodiment will be described in detail with reference to Fig. 8. Fig. 8 is a diagram showing a configuration example and processing example of the information processing system 100-2 according to the second embodiment. In the following, a configuration example of the entire information processing system 100-2, a processing example of the information processing system 100, and the effects of the information processing system 100-2 will be described.

[0087] (1-1. Example of the Overall Configuration of Information Processing System 100-2) An example of the overall configuration of an information processing system 100-2 according to the second embodiment will be described. The information processing system 100-2 includes a field device 10 which is a measuring device, a display device 20 (terminal device 20A, control device 20B) used by an operator O, and a server device 30 which is an estimation device. Here, the field device 10, the display device 20, and the server device 30 are connected to each other via a predetermined communication network (not shown) so as to be able to communicate with each other by wire or wirelessly. Note that the predetermined communication network may be any of various communication networks such as the Internet or a dedicated line. The information processing system 100-2 shown in FIG. 8 may include a plurality of field devices 10 or a plurality of server devices 30.

[0088] (1-1-1. Field device 10) The field device 10 is a plant device that is installed in a plant and acquires waveform data. In the example of Fig. 8, the field device 10 is realized by a vortex flowmeter that acquires vortex waveform data.

[0089] (1-1-2.Display equipment 20) The display device 20 is a plant device having a display function that is installed in a plant and used by an operator O who manages the plant. In the example of Fig. 8, the display device 20 is realized by a terminal device 20A that is a portable terminal, and a control device 20B that is included in a control system such as a distributed control system (DCS).

[0090] (1-1-3. Server device 30) The server device 30 is installed in a cloud environment and provides an estimation result. The server device 30 may be installed in an on-premise environment, an edge environment, or the like.

[0091] (1-2. Example of processing of the entire information processing system 100-2) An example of the overall processing of the information processing system 100-2 according to the second embodiment will be described. Note that the processing of steps S1 to S6 below may be executed in a different order. Also, some of the processing of steps S1 to S6 below may be omitted.

[0092] (1-2-1. Waveform data acquisition process) First, the field device 10 acquires waveform data (step S1). For example, the field device 10 acquires waveform data during flow rate measurement. At this time, the field device 10 stores the acquired waveform data.

[0093] (1-2-2. Waveform data transmission process) Secondly, the field device 10 transmits the waveform data to the display device 20 (step S2). For example, the field device 10 refers to the waveform data stored based on the operation of the operator O, acquires the waveform data for a predetermined period, and transmits it to the display device 20. At this time, the field device 10 transmits device setting information indicating the measurement conditions of the field device 10 to the display device 20.

[0094] Thirdly, the display device 20 transmits the waveform data to the server device 30 (step S3). For example, the display device 20 displays a time series graph showing the waveform of the waveform data for a predetermined period received from the field device 10 on the monitor screen of the display device 20, and transmits the waveform data for the predetermined period received from the field device 10 to the server device 30. At this time, the display device 20 transmits device setting information indicating the measurement conditions of the field device 10 received from the field device 10 to the server device 30.

[0095] (1-2-3. Detailed cause estimation process) Fourth, the server device 30 estimates a detailed cause from the waveform data (step S4). For example, the server device 30 estimates a detailed cause of the vibration anomaly of the field device 10 as a detailed cause of the predetermined event by using an estimation model EM, which is a machine learning model and outputs a detailed cause of a predetermined event occurring in a plant in response to input of the waveform data and device setting information.

[0096] (1-2-4. Estimation result transmission process) Fifth, the server device 30 transmits the estimation result to the display device 20 (step S5). For example, the server device 30 transmits, as the estimation result, a notification message indicating a detailed cause of the vibration abnormality of the field device 10 and a solution to the vibration abnormality to the display device 20.

[0097] (1-2-5. Detailed cause display processing) Sixth, the display device 20 displays the detailed cause (step S6). For example, the display device 20 displays a notification message indicating a solution to the vibration abnormality of the field device 10 together with the detailed cause of the vibration abnormality on the monitor screen of the display device 20.

[0098] (1-2-6.Others) The information processing system 100-2 according to the second embodiment can also execute the processing of the information processing system 100-1 according to the first embodiment described above. That is, in the information processing system 100-2, in the processing of the above steps S1 to S3, the detailed cause can be estimated from the waveform data acquired and transmitted by the field device 10 according to the first embodiment when an event is detected. At this time, when the field device 10 detects an event such as an alarm occurrence or a periodic inspection time, the field device 10 refers to the stored waveform data, acquires the waveform data for a predetermined period from the occurrence of the event, and transmits the waveform data to the server device 30 via the display device 20.

[0099] (1-3. Effects of Information Processing System 100-2) In the following, an overview and problems of the reference technology will be described, and then the effects of the information processing system 100-2 will be described.

[0100] (1-3-1. Overview of reference technology and issues) Here, an overview of a reference technique for estimating detailed causes of alarm occurrence in a plant using waveform data will be described. The following describes the processing of a field device 10P according to the reference technique, which is a vortex flowmeter and acquires vortex waveform data.

[0101] In the reference technique, first, the field device 10P acquires waveform data and stores the acquired waveform data. Second, the field device 10P refers to the stored waveform data based on the operation of the operator O who recognizes the occurrence of an alarm, acquires waveform data for a predetermined period from the occurrence of the alarm, and transmits it to the display device 20. Third, the display device 20 displays on the monitor screen as a waveform screen a time series graph showing the waveform of the waveform data for a predetermined period received from the field device 10. Fourth, the operator O views the waveform screen displayed by the display device 20, visually confirms the difference from the normal state, and empirically estimates the detailed cause of the occurrence of the alarm, such as a piping abnormality, a sensor abnormality, a pump abnormality, resonance, electrical noise, etc.

[0102] However, in the reference technology, since the operator O must visually determine the detailed cause of an alarm, there is a problem in that the burden on the operator O is large when an alarm occurs. In addition, in the reference technology, there is a problem in that the determination of the detailed cause of an alarm depends on the experience of the operator O.

[0103] (1-3-2. Overview of Information Processing System 100-2) The information processing system 100-2 includes a field device 10 which is a measuring device, a display device 20 used by an operator O, and a server device 30 which is an estimation device. First, the field device 10 acquires waveform data and stores the acquired waveform data. Second, the field device 10 acquires waveform data for a predetermined period based on the operation of an operator O who recognizes the occurrence of an alarm, and transmits the waveform data to the server device 30. Third, the server device 30 estimates a detailed cause at the time of the alarm occurrence using the estimation model EM. Fourth, the server device 30 transmits the detailed cause at the time of the alarm occurrence to the display device 20. Fifth, the display device 20 displays the detailed cause at the time of the alarm occurrence on a monitor screen.

[0104] In addition, in the information processing system 100-2, the server device 30 can receive waveform data for a predetermined period from the occurrence of an alarm transmitted by the field device 10 that detects the occurrence of an alarm, and input the received waveform data into the estimation model EM, thereby estimating a detailed cause of the occurrence of an alarm. That is, in the information processing system 100-2, the server device 30 may be realized in combination with the field device 10 described above in the first embodiment.

[0105] (1-3-3. Effects) In the information processing system 100-2, the server device 30 receives waveform data and device setting information sent by the field device 10 at the time an alarm occurs, and utilizes a machine learning model to estimate the detailed cause of the alarm, enabling rapid troubleshooting.

[0106] As described above, in the information processing system 100-2, the server device 30 can effectively provide information about the plant from the waveform data acquired in the plant.

[0107] 2. Configuration and Processing of Each Device in Information Processing System 100-2 9 to 13, configuration examples and processing examples of each device included in the information processing system 100-2 shown in Fig. 8 will be described. In the following, an example of the overall configuration of the information processing system 100-2 according to the second embodiment will be described, and then an example of the configuration and processing examples of the server device 30 will be described in detail. Note that the configuration examples and processing examples of the field device 10 and the display device 20 are common to those of the first embodiment, and therefore will not be described.

[0108] (2-1. Example of the Overall Configuration of Information Processing System 100-2) An example of the overall configuration of the information processing system 100-2 shown in FIG. 8 will be described with reference to FIG. 9. FIG. 9 is a block diagram showing an example of the configuration of each device of the information processing system 100-2 according to the second embodiment. As shown in FIG. 9, the information processing system 100-2 has a field device 10 which is a measuring device, a display device 20 which is a user terminal, and a server device 30 which is an estimation device. The server device 30 is communicatively connected to the field device 10 and the display device 20 via a communication network N which is realized by the Internet, a dedicated line, or the like. The field device 10 is installed in a plant. The field device 10 is installed in a plant. The display device 20 is installed in a plant. The server device 30 is installed in a cloud environment.

[0109] (2-2. Configuration and Processing Examples of Server Device 30) A configuration example and a processing example of the server device 30 shown in FIG. 8 will be described with reference to FIG. 9. The server device 30 has an input unit 31, a display unit 32, a communication unit 33, a storage unit 34, and a control unit 35. Although a configuration example and a processing example of the server device 30 will be described below, the field device 10 or the display device 20 can also execute the same processing. That is, the information processing system 100-2 can be realized even by the field device 10 having the function of the server device 30, or the display device 20 having the function of the server device 30.

[0110] (2-2-1. Input unit 31) The input unit 31 is responsible for inputting various types of information to the server device 30. For example, the input unit 31 is realized by a mouse, a keyboard, etc., and accepts input of setting information and the like to the server device 30.

[0111] (2-2-2.Display section 32) The display unit 32 is responsible for outputting various types of information from the server device 30. For example, the display unit 32 is realized by a display or the like, and outputs setting information stored in the server device 30, and the like.

[0112] (2-2-3. Communications Department 33) The communication unit 33 manages data communication with other devices. For example, the communication unit 33 performs data communication with each communication device via a router, etc. The communication unit 33 can also perform data communication with an operator's terminal (not shown).

[0113] (2-2-4. Storage section 34) The storage unit 34 stores various information referenced when the control unit 35 operates and various information acquired when the control unit 35 operates. The storage unit 34 has an estimation result storage unit 34a and an estimation model storage unit 34b. Here, the storage unit 34 can be realized by, for example, a semiconductor memory element such as a RAM or a flash memory, or a storage device such as a hard disk or an optical disk. Note that, in the example of FIG. 9, the storage unit 34 is installed inside the server device 30, but it may be installed outside the server device 30, or multiple storage units may be installed.

[0114] (2-2-4-1. Estimation result storage unit 34a) The estimation result storage unit 34a stores the estimation result. For example, the estimation result storage unit 34a stores the estimation result generated by an estimation unit 35b of the control unit 35 described later. Here, an example of data stored in the estimation result storage unit 34a will be described with reference to FIG. 10. FIG. 10 is a diagram showing an example of the estimation result storage unit 34a of the server device 30 according to the second embodiment. In the example of FIG. 10, the estimation result storage unit 34a has items such as "field device" and "estimation result".

[0115] The "field device" indicates identification information for identifying the field device 10 which is a plant device, such as an identification number or an identification symbol of the vortex flowmeter. The "estimated result" indicates the cause of a predetermined event estimated by the estimation unit 35b, such as a detailed cause of a piping abnormality, a pump abnormality, a sensor abnormality, resonance, electrical noise, etc., of the field device 10 at the time of the alarm occurrence.

[0116] That is, FIG. 10 shows an example in which data such as "estimated result FI001-E1," "estimated result FI001-E2," "estimated result FI001-E3," ... are stored in the estimation result memory unit 34a as "estimated results" estimated from waveform data received from a field device 10 identified by "field device FI001."

[0117] (2-2-4-2. Estimation model storage unit 34d) The estimation model storage unit 34b stores an estimation model EM. For example, the estimation model storage unit 34b stores an estimation model EM used by an estimation unit 35b of the control unit 35, which will be described later. Here, an example of data stored in the estimation model storage unit 34b will be described with reference to FIG. 11. FIG. 11 is a diagram showing an example of the estimation model storage unit 34b of the server device 30 according to the second embodiment. In the example of FIG. 11, the estimation model storage unit 34b has an item such as "estimation model."

[0118] An "estimation model" is model data of a machine learning model, and is data including, for example, execution data for executing the algorithm of the estimation model EM, model parameters, hyperparameters, etc.

[0119] That is, FIG. 11 shows an example in which data such as "estimation model EM001", . . . are stored in the estimation model storage unit 34b as model data of the estimation model EM used by the estimation unit 35b.

[0120] (2-2-5. Control unit 35) The control unit 35 is responsible for overall control of the server device 30. The control unit 35 has a receiving unit 35a, an estimating unit 35b, and an output unit 35c. Here, the control unit 35 can be realized by, for example, an electronic circuit such as a CPU or an MPU, or an integrated circuit such as an ASIC or an FPGA.

[0121] (2-2-5-1. Receiving unit 35a) The receiving unit 35a executes a waveform data receiving process. For example, the receiving unit 35a receives measurement data from the measuring device when a predetermined event occurs in the measuring device. At this time, the receiving unit 35a receives measurement data from the field device 10 when a predetermined event occurs in the field device 10. The receiving unit 35a also receives waveform data from the vortex flowmeter. The receiving unit 35a also receives measurement data from the measuring device when an alarm occurs in the measuring device. The receiving unit 35a also receives measurement data from the measuring device at the time of regular inspection of the measuring device. The receiving unit 35a may store the measurement data in the memory unit 34.

[0122] To explain a specific example, the receiver 35a receives "waveform data FI001-W2" as waveform data when an alarm occurs from a vortex flowmeter, which is a field device 10 identified by "field device FI001."

[0123] Furthermore, the receiving unit 35a receives "waveform data FI001-W3" as waveform data from the regular inspection start time to the regular inspection end time from the vortex flowmeter, which is the field device 10 identified by "field device FI001".

[0124] (2-2-5-2. Estimation part 35b) The estimation unit 35b executes detailed cause estimation processing. For example, the estimation unit 35b inputs the measurement data received by the receiving unit 35a to the estimation model EM, which is a trained machine learning model trained to output information on the occurrence cause of a predetermined event in response to the input of the measurement data, and estimates the occurrence cause (detailed cause) of the predetermined event. At this time, the estimation unit 35b inputs the measurement data received from the field device 10 and the setting information of the field device 10 to the estimation model EM, which is a trained machine learning model trained to output information on the occurrence cause of a predetermined event in response to the input of the measurement data and the setting information of the measuring device (device setting information), and estimates the occurrence cause of the predetermined event. In addition, the estimation unit 35b inputs the waveform data received from the vortex flowmeter and the setting information of the vortex flowmeter to the estimation model EM, which is a trained machine learning model trained to output information on the occurrence cause of a predetermined event in response to the input of the measurement data and the setting information of the measuring device, and estimates the occurrence cause of the vibration abnormality. Here, the waveform data is generated from a vortex signal measured when the piping of the vortex flowmeter vibrates. The setting information of the vortex flowmeter also includes at least one of a measurement fluid and a bore size of the vortex flowmeter.

[0125] The estimation unit 35b estimates the cause of the alarm as the detailed cause of the event, and also estimates the cause of the abnormal state of the measuring device that occurred at the regular inspection time as the detailed cause of the event.

[0126] To explain a specific example, the estimation unit 35b inputs the waveform data "waveform data FI001-W2" at the time an alarm occurred, which is received from the vortex flowmeter, which is a field device 10 identified by "field device FI001", and the device setting information "device setting information FI001-D1", "device setting information FI001-D2", and "device setting information FI001-D3" of the vortex flowmeter, into the estimation model "estimation model EM001" stored in the estimation model memory unit 34b, and obtains the output estimation result "estimation result FI001-E1", thereby estimating the detailed cause of the alarm that occurred in the vortex flowmeter.

[0127] In addition, the estimation unit 35b inputs the waveform data "waveform data FI001-W3" from the start time of the regular inspection to the end time of the regular inspection received from the vortex flowmeter, which is the field device 10 identified by "field device FI001", and the device setting information of the vortex flowmeter, "device setting information FI001-D1", "device setting information FI001-D2", and "device setting information FI001-D3", into the estimation model "estimation model EM001" stored in the estimation model memory unit 34b, and obtains the output estimation result "estimation result FI001-E2", thereby estimating the detailed cause of the abnormal condition that occurred at the time of the regular inspection of the vortex flowmeter.

[0128] (2-2-5-3. Output section 35c) The output unit 35c executes an estimation result output process. For example, the output unit 35c outputs the cause of the occurrence of the predetermined event estimated by the estimation unit 35b. The output unit 35c also causes an external device to display a notification message indicating the cause of the occurrence of the predetermined event and a solution means.

[0129] To explain a specific example, the output unit 35c displays, on the monitor screen of the display device 20 used by the operator O, a message indicating a detailed cause of the alarm that has occurred in the vortex flowmeter, which is the field device 10 identified by "field device FI001", "There may be a piping abnormality", as well as a message indicating a solution to the alarm, "Please check the piping and viscosity".

[0130] (2-3. Specific Example of Data Processing by Server Device 30) A specific example of data processing by the server device 30 will be described with reference to Fig. 12. Fig. 12 is a diagram showing a specific example of data processing by the server device 30 according to the second embodiment. The detailed cause estimation processing executed by the server device 30 will be described below.

[0131] First, the server device 30 receives input data to the estimation model EM (see FIG. 12(1)). For example, the server device 30 receives waveform data received from the field device 10 and measurement conditions including a measurement fluid, a bore size, etc. as device setting information of the field device 10.

[0132] Secondly, the server device 30 inputs the input data to the estimation model EM (see FIG. 12(2)). Here, the detection model EM is a trained machine learning model that has been trained in advance with waveform data at the time of occurrence of a past equipment alarm for each measurement condition such as the measurement fluid (e.g., liquid, gas, water, steam), and caliber size.

[0133] Third, the server device 30 acquires the output data output by the estimation model EM (see FIG. 12(3)). For example, the server device 30 acquires, as output data, the detailed cause of the alarm output by the estimation model EM to which the waveform data and the device setting information of the field device 10 are input as input data, and estimates the detailed cause of the alarm of the field device 10.

[0134] (2-4. Example of Hardware Configuration of Server Device 30) An example of a hardware configuration of the server device 30 will be described with reference to Fig. 13. Fig. 13 is a diagram showing an example of a hardware configuration according to the first embodiment. As shown in Fig. 13, the server device 30 includes a communication device 30a, a hard disk drive (HDD) 30b, a memory 30c, and a processor 30d. The components shown in Fig. 13 are connected to each other via a bus or the like.

[0135] The communication device 30a is a network interface card or the like, and communicates with other servers. The HDD 30b stores programs and DBs that operate the functions shown in FIG.

[0136] The processor 30d reads out a program that executes the same processes as the respective processing units shown in FIG. 9 from the HDD 30b or the like and loads it in the memory 30c, thereby operating a process that executes the respective functions described in FIG. 9. For example, this process executes the same functions as the respective processing units of the server device 30. Specifically, the processor 30d reads out a program having the same functions as the receiving unit 35a, the estimating unit 35b, the output unit 35c, or the like from the HDD 30b or the like. Then, the processor 30d executes a process that executes the same processes as the receiving unit 35a, the estimating unit 35b, the output unit 35c, or the like.

[0137] In this way, the server device 30 operates as a device that executes various processing methods by reading and executing the program. The server device 30 can also realize the same functions as in the above-mentioned embodiment 2 by reading the program from a recording medium using a medium reading device and executing the read program. Note that the program in this other embodiment is not limited to being executed by the server device 30. For example, the present invention can be similarly applied to cases where another computer or server executes a program, or where these execute a program in cooperation with each other.

[0138] This program can be distributed via a network such as the Internet. This program can also be recorded on a computer-readable recording medium such as a hard disk, solid-state drive (SSD), flexible disk (FD), CD-ROM, MO, or DVD, and can be executed by being read from the recording medium by a computer.

[0139] 3. Processing Flow of Information Processing System 100-2 A process flow of the information processing system 100-2 according to the second embodiment will be described with reference to Fig. 14. Fig. 14 is a flowchart showing an example of a process flow of the information processing system 100-2 according to the second embodiment. Note that the processes of steps S201 to S205 below may be executed in a different order. Also, some of the processes of steps S201 to S205 below may be omitted.

[0140] (3-1. Waveform data acquisition process) First, the field device 10 executes a waveform data acquisition process (step S201). For example, the field device 10 acquires waveform data during flow rate measurement, and stores the acquired waveform data.

[0141] (3-2. Waveform data transmission processing) Secondly, the field device 10 executes a waveform data transmission process (step S202). For example, the field device 10 displays a time series graph showing the waveform of the waveform data for a predetermined period on the monitor screen of the display device 20, and transmits the waveform data for the predetermined period to the server device 30.

[0142] (3-3. Detailed cause estimation processing) Third, the server device 30 executes a detailed cause estimation process (step S203). For example, the server device 30 estimates a detailed cause of the vibration abnormality of the field device 10 by using an estimation model EM, which is a trained machine learning model and outputs a detailed cause of a predetermined event occurring in a plant in response to input of waveform data and device setting information.

[0143] (3-4. Estimation result transmission process) Fourth, the server device 30 executes an estimation result transmission process (step S204). For example, the server device 30 transmits, as an estimation result, a notification message indicating a detailed cause of the vibration abnormality of the field device 10 and a solution to the vibration abnormality to the display device 20.

[0144] (3-5. Detailed cause display processing) Fifth, the display device 20 executes detailed cause display processing (step S205). For example, the display device 20 displays a notification message indicating a solution to the vibration abnormality along with the detailed cause of the vibration abnormality of the field device 10 on the monitor screen of the display device 20.

[0145] 4. Effects of the Second Embodiment Finally, a description will be given of the effects of the second embodiment. The following describes effects 1 to 8 corresponding to the processing according to the second embodiment.

[0146] (4-1. Effect 1) First, in the process according to the second embodiment described above, the server device 30 receives measurement data from the measurement device when a predetermined event occurs in the measurement device, inputs the received measurement data into a trained estimation model EM that has been trained to output information related to the cause of the predetermined event in response to the input of the measurement data, estimates the cause of the predetermined event, and outputs the estimated cause of the predetermined event. Therefore, in the process according to the second embodiment, information related to the measurement device can be effectively provided from the measurement data.

[0147] (4-2. Effect 2) Secondly, in the process according to the above-mentioned second embodiment, the server device 30 receives measurement data from the field device 10 when a predetermined event occurs in the field device 10, and inputs the measurement data received from the field device 10 and device setting information of the field device 10 into the trained estimation model EM to estimate the cause of the occurrence of the predetermined event. Therefore, in the process according to the second embodiment, information about the field device 10 can be effectively provided from the measurement data.

[0148] (4-3. Effect 3) Thirdly, in the process according to the above-mentioned second embodiment, the server device 30 receives waveform data from the vortex flowmeter, and inputs the waveform data received from the vortex flowmeter and device setting information of the vortex flowmeter into the trained estimation model EM to estimate the cause of the vibration abnormality. Therefore, in the process according to the second embodiment, information about the vortex flowmeter, which is the field device 10, can be effectively provided from the waveform data.

[0149] (4-4. Effect 4) Fourth, in the process according to the above-mentioned second embodiment, the waveform data is generated from a vortex signal measured when the vortex flowmeter is subjected to piping vibration, and the device setting information of the vortex flowmeter includes at least one of the measurement fluid and the aperture size of the vortex flowmeter. Therefore, in the process according to the second embodiment, when the vortex flowmeter, which is the field device 10, is significantly affected by piping vibration, the waveform data can effectively provide information about the vortex flowmeter.

[0150] (4-5. Effect 5) Fifth, in the process according to the second embodiment described above, the server device 30 receives measurement data from the measurement device when an alarm occurs in the measurement device and estimates the cause of the alarm. Therefore, in the process according to the second embodiment, detailed causes of the alarm in the measurement equipment can be effectively provided from the measurement data.

[0151] (4-6. Effect 6) Sixth, in the process according to the second embodiment described above, the server device 30 receives measurement data from the measurement device at the regular inspection time, and estimates the cause of the abnormal state of the measurement device that occurred at the regular inspection time. Therefore, in the process according to the second embodiment, it is possible to effectively provide a solution for preventive safety of the measurement equipment from the measurement data.

[0152] (4-7. Effect 7) Seventh, in the process according to the above-mentioned second embodiment, the server device 30 causes a notification message indicating the cause of a predetermined event and a means for solving the event to be displayed on the display device 20. Therefore, in the process according to the second embodiment, information about the measurement device can be provided from the measurement data in a form that is easy for the operator O to understand.

[0153] (4-8. Effect 8) Eighth, in the process according to the above-described second embodiment, the field device 10 acquires and retains measurement data, and when it detects the occurrence of an event based on event information related to the event for outputting the measurement data to the server device 30, it acquires the retained measurement data and outputs it to the server device 30. Therefore, in the process according to the second embodiment, information related to the field device 10 can be effectively provided from the measurement data provided at an effective timing.

[0154] 〔system〕 The information including the processing procedures, control procedures, specific names, various data and parameters shown in the above documents and drawings can be changed arbitrarily unless otherwise specified.

[0155] In addition, each component of each device shown in the figure is a functional concept, and does not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure. In other words, all or part of them can be functionally or physically distributed and integrated in any unit according to various loads, usage conditions, etc.

[0156] Furthermore, each processing function performed by each device may be realized, in whole or in part, by a CPU and a program analyzed and executed by the CPU, or may be realized as hardware using wired logic.

[0157] 〔others〕 Some examples of combinations of the disclosed technical features are set out below.

[0158] (1) A measuring device comprising: an acquisition unit that acquires measurement data and stores it in a memory unit; and an output unit that acquires the measurement data from the memory unit and outputs it to an external device when an occurrence of an event is detected based on event information regarding an event for outputting the measurement data to the external device.

[0159] (2) The measuring instrument according to (1), wherein the acquisition unit generates waveform data as the measurement data from a vortex signal measured during piping vibration of the vortex flowmeter.

[0160] (3) The measuring device according to (1) or (2), wherein the output unit, when detecting the occurrence of the event, acquires the measurement data for a predetermined period from the time the event occurs from the memory unit.

[0161] (4) The measuring device according to any one of (1) to (3), wherein the output unit acquires the measurement data from the memory unit when detecting an occurrence of an alarm as the occurrence of the event.

[0162] (5) The measuring device according to any one of (1) to (4), wherein the output unit acquires the measurement data from the memory unit when detecting a regular inspection time as the occurrence of the event.

[0163] (6) The measuring instrument according to any one of (1) to (5), wherein the output unit causes the external device to display a time series graph showing a waveform of the waveform data for a predetermined period of time.

[0164] (7) The measuring device according to any one of (1) to (6), wherein the acquisition unit generates the measurement data based on a signal measured in a plant.

[0165] (8) The measuring device described in any one of (1) to (7), wherein the external device receives measurement data from the measuring device when a specified event occurs in the measuring device, inputs the received measurement data into a trained machine learning model trained to output information regarding the cause of the specified event in response to the input of measurement data, estimates the cause of the specified event, and outputs the estimated cause of the specified event.

[0166] (9) A measurement method in which a computer executes a process in which it acquires and retains measurement data, and when it detects the occurrence of an event based on event information relating to an event in which the measurement data is output to an external device, it acquires the retained measurement data and outputs it to the external device.

[0167] (10) A measurement program that causes a computer to execute a process of acquiring and retaining measurement data, and when an occurrence of an event is detected based on event information regarding an event that outputs the measurement data to an external device, acquiring the retained measurement data and outputting it to the external device. [Explanation of symbols]

[0168] 10 Field Devices 11 Input section 12 Display section 13. Communications Department 14 Storage section 14a Waveform data storage section 14b Device setting information storage section 15 Control section 15a Acquisition part 15b Detection section 15c Output section 20 Display equipment 30 Server device 31 Input section 32 Display section 33 Communications Department 34 Storage section 34a Estimation result storage unit 34b Estimation model memory unit 35 Control section 35a Receiving section 35b Estimation part 35c output section 100-1, 100-2 Information Processing Systems

Claims

1. an acquisition unit that acquires measurement data and stores the data in a storage unit; an output unit that, when detecting an occurrence of an event based on event information related to an event for outputting the measurement data to an external device, acquires the measurement data from the storage unit and outputs the measurement data to the external device; A measuring device comprising:

2. The acquisition unit is As the measurement data, waveform data is generated from a vortex signal measured when a piping of the vortex flowmeter vibrates.

2. The measuring device of claim 1.

3. The output unit is When the occurrence of the event is detected, the measurement data for a predetermined period from the time point of the event occurrence is acquired from the storage unit.

2. The measuring device of claim 1.

4. The output unit is acquiring the measurement data from the storage unit when detecting the occurrence of an alarm as the occurrence of the event; 2. The measuring device of claim 1.

5. The output unit is When a regular inspection time is detected as the occurrence of the event, the measurement data is acquired from the storage unit.

2. The measuring device of claim 1.

6. The output unit is displaying on the external device a time series graph showing the waveform of the waveform data for a predetermined period of time; 2. The measuring device of claim 1.

7. The acquisition unit is generating the measurement data based on signals measured in a plant; 2. The measuring device of claim 1.

8. The external device is receiving measurement data from a measurement device when a predetermined event occurs in the measurement device; inputting the received measurement data into a trained machine learning model that has been trained to output information regarding a cause of occurrence of a predetermined event in response to the input of the measurement data, and estimating a cause of occurrence of the predetermined event; outputting the estimated cause of the occurrence of the predetermined event; A measuring device according to any one of claims 1 to 7.

9. The computer Acquire and hold measurement data, When an occurrence of an event is detected based on event information related to an event for outputting the measurement data to an external device, the measurement data stored in the storage device is acquired and output to the external device. The measurement method by which the process is carried out.

10. On the computer, Acquire and hold measurement data, When an occurrence of an event is detected based on event information related to an event for outputting the measurement data to an external device, the measurement data stored in the storage device is acquired and output to the external device. A measurement program that performs the process.

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