WO2020054850A1 - Dispositif d'aide à la surveillance de processus, système d'aide à la surveillance de processus, procédé d'aide à la surveillance de processus, programme d'aide à la surveillance de processus et dispositif terminal - Google Patents

Dispositif d'aide à la surveillance de processus, système d'aide à la surveillance de processus, procédé d'aide à la surveillance de processus, programme d'aide à la surveillance de processus et dispositif terminal Download PDF

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WO2020054850A1
WO2020054850A1 PCT/JP2019/036119 JP2019036119W WO2020054850A1 WO 2020054850 A1 WO2020054850 A1 WO 2020054850A1 JP 2019036119 W JP2019036119 W JP 2019036119W WO 2020054850 A1 WO2020054850 A1 WO 2020054850A1
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index
data
contribution
unit
indicating
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PCT/JP2019/036119
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English (en)
Japanese (ja)
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理 山中
諒 難波
卓巳 小原
由紀夫 平岡
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株式会社東芝
東芝インフラシステムズ株式会社
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Priority to MX2021002926A priority Critical patent/MX2021002926A/es
Publication of WO2020054850A1 publication Critical patent/WO2020054850A1/fr
Priority to PH12021550545A priority patent/PH12021550545A1/en

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring

Definitions

  • the embodiments of the present invention relate to a process monitoring support device, a process monitoring support system, a process monitoring support method, a process monitoring support program, and a terminal device.
  • the process monitoring support device includes a data acquisition unit, an index calculation unit, a contribution ratio calculation unit, and a display control unit.
  • the data acquisition unit acquires a plurality of types of time-series data of process variables indicating the state of the process to be monitored.
  • the index calculation unit calculates an index indicating a possibility that the monitoring target process is healthy without any abnormality based on the plurality of types of the process variables.
  • Contribution rate calculation unit for the index calculated by the index calculation unit, the contribution rate indicating the rate of contribution to increase the possibility that there is no abnormality and is healthy, for each of the plurality of types of process variables Is calculated.
  • the display control unit generates display information so as to display information on some process variables having a relatively small value of the contribution ratio on a screen of the user terminal.
  • FIG. 1 is a diagram showing a specific example of the configuration of the process monitoring support device 2 of the embodiment.
  • FIG. 1 shows a specific example in which the monitoring target of the process monitoring support device 2 is the advanced sewage treatment process 1.
  • Sewage advanced treatment process 1 is a process aimed at removing nitrogen and phosphorus from sewage.
  • the advanced sewage treatment process 1 has a sedimentation basin 101, an anaerobic tank 102, an anoxic tank 103, an aerobic tank 104 and a final sedimentation basin 105.
  • the sewage to be treated (hereinafter referred to as “treatment water”) is first sent to the sedimentation basin 101, the anaerobic tank 102, the anoxic tank 103, the aerobic tank 104, and the final sedimentation basin 105 in this order.
  • the water to be treated returned from the aerobic tank 104 at the subsequent stage is mixed with the water to be treated sent from the anaerobic tank 102, and is agitated without supplying air.
  • the nitric acid in the water to be treated is decomposed into nitrogen by the action of microorganisms and released to the atmosphere. Generally, this treatment is called denitrification.
  • the aerobic tank 104 is a tank for decomposing organic substances in the water to be treated, removing phosphorus, and nitrifying ammonia. Specifically, air is supplied to the water to be treated to activate the microorganisms, and the microorganisms decompose organic substances, and the microorganisms absorb phosphorus in the water to be treated. Microorganisms that discharge phosphorus in an anaerobic state and instead accumulate organic matter absorb the phosphorus that is exhaled by being activated, so that the phosphorus in the water to be treated is removed. In the aerobic tank 104, ammonia is decomposed into nitric acid by supplying air to the water to be treated. This treatment is generally called nitrification.
  • the final sedimentation basin 105 is a reservoir for treated water from which phosphorus has been removed and ammonia has been nitrified.
  • solids remaining in the water to be treated are separated by sedimentation, and the supernatant clear water is discharged as treated water.
  • the first settling basin excess sludge extraction pump 111 is a pump for extracting and removing sludge settled from the first settling basin 101.
  • the first settling basin excess sludge extraction pump 111 has a flow rate sensor for measuring the flow rate of the extracted sludge.
  • the blower 112 is a blower that supplies oxygen to the aerobic tank 104.
  • the blower 112 has a flow sensor that measures the flow rate of the supplied air.
  • the circulation pump 113 is a pump that returns the water to be treated from the aerobic tank 104 to the anoxic tank 103.
  • the circulation pump 113 has a flow rate sensor that measures the flow rate of the returned treated water.
  • the rainfall sensor 121 is a sensor that measures the rainfall near the sewage altitude treatment process 1.
  • the sewage inflow sensor 122 is a sensor that measures the flow rate of sewage flowing into the sewage advanced treatment process 1 (hereinafter, referred to as “inflow sewage”).
  • the inflow TN sensor 123 is a sensor that measures the total amount of nitrogen (TN) contained in the inflow sewage.
  • the inflow TP sensor 124 is a sensor that measures the total amount of phosphorus (TP) contained in the inflow sewage.
  • the inflowing organic matter sensor 125 is a UV (absorbance) sensor or a COD (chemical oxygen demand) sensor that measures the amount of organic matter contained in the inflowing sewage.
  • the ORP sensor 126 is a sensor that measures the ORP (oxidation-reduction potential) of the anaerobic tank 102.
  • Anaerobic tank pH sensor 127 is a sensor that measures the pH of anaerobic tank 102.
  • the anoxic tank ORP sensor 128 is a sensor that measures the ORP of the anoxic tank 103.
  • the anoxic tank pH sensor 129 is a sensor that measures the pH of the anoxic tank 103.
  • the phosphoric acid sensor 130 is a sensor that measures the concentration of phosphoric acid in the aerobic tank 104.
  • the DO sensor 131 is a sensor that measures the dissolved oxygen concentration (DO) in the aerobic tank 104.
  • the ammonia sensor 132 is a sensor that measures the ammonia concentration in the aerobic tank 104.
  • the MLSS sensor 133 is a sensor that measures the activated sludge concentration (MLSS) in at least one of the anaerobic tank 102, the anaerobic tank 103, and the aerobic tank 104.
  • the water temperature sensor 134 is a sensor that measures the water temperature in at least one location of the anoxic tank 103 or the aerobic tank 104.
  • the surplus sludge SS sensor 135 is a sensor that measures the solid (SS) concentration of sludge extracted from the final sedimentation basin 105.
  • the discharge SS sensor 136 is a sensor that measures the SS concentration of the water discharged from the final sedimentation basin 105.
  • the sludge interface sensor 137 is a sensor that measures the sludge interface level of the final settling basin 105.
  • the sewage discharge flow sensor 138 is a sensor that measures the flow rate of discharged water.
  • the discharge TN sensor 139 is a sensor that measures the total amount of nitrogen contained in the discharge water.
  • the discharge TP sensor 140 is a sensor that measures the total amount of phosphorus contained in the discharge water.
  • the discharged organic substance sensor 141 is a UV sensor or a COD sensor that measures the amount of organic substances contained in the discharged water.
  • Each of the above-mentioned devices such as the first settling basin excess sludge pulling pump 111, the blower 112, the circulation pump 113, the return sludge pump 114, and the final settling basin excess sludge pulling pump 115 operates under the control of a predetermined cycle.
  • the above-described sensors including the flow rate sensors included in the equipment of the initial sedimentation tank excess sludge extraction pump 111, the blower 112, the circulation pump 113, the return sludge pump 114, and the final sedimentation tank excess sludge extraction pump 115 are sensed at predetermined intervals. Measure the target.
  • the flow rate sensors included in the first settling tank excess sludge extraction pump 111, the blower 112, the circulation pump 113, the return sludge pump 114, and the final settling tank excess sludge extraction pump 115 are collectively referred to as operation amount sensors, and other sensors are referred to.
  • process sensors are collectively referred to as process sensors.
  • Each of the operation amount sensors and each of the process sensors transmits measurement data obtained by sensing in a predetermined cycle to the process monitoring support device 2 as process data.
  • the process monitoring support device 2 includes a CPU (Central Processing Unit), a memory, and an auxiliary storage device connected by a bus, and executes a monitoring support program.
  • the process monitoring support device 2 functions as a device including a data collection unit 201, a data storage unit 202, a state definition unit 21, a state calculation unit 22, and a display control unit 23 by executing a monitoring support program.
  • all or a part of each function of the process monitoring support device 2 may be realized using hardware such as an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), and an FPGA (Field Programmable Gate Array). Good.
  • ASIC Application Specific Integrated Circuit
  • PLD Programmable Logic Device
  • FPGA Field Programmable Gate Array
  • the data collection unit 201 may be implemented as a device having a housing different from that of the process monitoring support device 2 using a PLC (Programmable Logic Controller).
  • the monitoring support program may be recorded on a computer-readable recording medium.
  • the computer-readable recording medium is, for example, a portable medium such as a flexible disk, a magneto-optical disk, a ROM, a CD-ROM, or a storage device such as a hard disk built in a computer system.
  • the monitoring support program may be transmitted via a telecommunication line.
  • the data collection unit 201 acquires process data from each operation amount sensor and each process sensor.
  • the acquired process data is time-series data of each process variable indicating the state of the monitoring target process.
  • the data collection unit 201 records the obtained process data in the data storage unit 202 according to a predetermined format.
  • the data storage unit 202 is configured using a storage device such as a magnetic hard disk device or a semiconductor storage device.
  • the data storage unit 202 stores the process data obtained by the data collection unit 201.
  • the state definition unit 21 defines data relating to the health of the process to be monitored.
  • the state definition unit 21 reads past time-series data of process variables recorded in the data storage unit 202.
  • the state definition unit 21 includes an index (hereinafter, referred to as a “process health index”) used to determine the health of the process, and a contribution ratio (hereinafter, referred to as “process health index”) indicating the degree of influence of each process variable on the process health index. Process soundness contribution ratio.)).
  • the process health index is an index indicating a possibility that an abnormality has occurred in the state of the monitored process.
  • the process soundness contribution ratio is a value indicating a ratio of each process variable contributing to an increase in the possibility that an abnormality has occurred in the process soundness index.
  • the state definition unit 21 generates typical representative values and representative pattern data (hereinafter, referred to as “normal state patterns”) in the past normal state of the process.
  • the state definition unit 21 operates, for example, at a predetermined cycle T0.
  • the state calculation unit 22 reads the current time-series data at the time of monitoring the process variables recorded in the data storage unit 202.
  • the state calculation unit 22 calculates a process soundness index and a process soundness contribution ratio of each process variable using the definition in the state definition unit 21.
  • the state calculation unit 22 assigns a high importance to each process variable in, for example, an ascending order of the process soundness contribution rate (an ascending order of abnormality).
  • the state calculation unit 22 extracts a predetermined number of upper process variables in descending order of importance at the time of monitoring.
  • the state calculation unit 22 operates, for example, in a predetermined cycle T1 ( ⁇ T0).
  • the display control unit 23 generates an image indicating the time-series data of a predetermined number of higher-order process variables in descending order of importance based on the information obtained by the state calculation unit 22. Such an image may be generated, for example, as a trend graph.
  • the display control unit 23 generates data (display information) for displaying the time series data of the process health index and the time series data of each process variable with high importance, and outputs the data to the user terminal 3.
  • the state definition unit 21 functions as a past data acquisition unit 211, an index definition unit 212, a contribution ratio definition unit 213, and a normal state pattern generation unit 214.
  • the past data acquisition unit 211 reads out past data (offline data) that is time-series data of a predetermined period (hereinafter, referred to as “predetermined period”) of each process variable from the data storage unit 202.
  • predetermined period a predetermined period of each process variable
  • the user may be able to define a past predetermined period by operating the user terminal 3. It is preferable that such a definition can be input by a GUI on the monitoring screen.
  • the plant state often changes gradually due to a change in plant operation or a seasonal change. Therefore, it is often more realistic to update the past predetermined period defining the process health index.
  • the length of the past predetermined period is set in advance, and the predetermined past period may be updated according to the length of the past predetermined period in a predetermined cycle.
  • the past data acquisition unit 211 reads from the data storage unit 202 the past data for the past predetermined period defined at the time of data acquisition. For example, assuming that the length of the past predetermined period is one week, data for the past one week may be read as data of the past predetermined period every week.
  • the past time-series data acquired by the past data acquisition unit 211 in this manner is described as X.
  • the time-series data X is a matrix having process variables in the row direction and time-series data over a predetermined past period in the column direction. In the following description, the number of process variables is n and the number of time-series data is m. Therefore, the time series data X is m ⁇ n time series data.
  • the index definition unit 212 uses the past time-series data X read by the past data acquisition unit 211 to define a process health index.
  • the index definition unit 212 may define the process health index by using a multivariate analysis or a machine learning technique.
  • the method of defining the process health index may be implemented in any manner. Since the process soundness index is generated from the past time-series data X, it includes information on n process variables, and the indicators for measuring the soundness of the process are collected into one index.
  • ⁇ The following are specific examples of process integrity indicators.
  • Q-statistic used in a method called MSPC (Multivariate Statistical Process Management), which is an advanced process monitoring and diagnostic technique.
  • Data for detecting abnormalities called Hotelling's T ⁇ 2 statistic.
  • Mahalanobis distance used in the Taguchi method (a technique similar to MSPC) used in the field of quality engineering.
  • the Mahalanobis distance is essentially equivalent to the T ⁇ 2 statistic used in MSPC.
  • the T ⁇ 2 statistic is defined after performing dimension reduction (dimension of n ′ ⁇ n), but in the method using the Mahalanobis distance, the distance is defined in an n-dimensional space. You. When the dimensions are aligned, the Mahalanobis distance and the T ⁇ 2 statistic are essentially the same, and there is only a difference of a constant multiple.
  • MSPC is used as a specific example of the process soundness index.
  • PCA principal component analysis
  • T_a is an m ⁇ n matrix based on the number m of samples and the number n of principal components, and is called a score matrix.
  • P_a is an n ⁇ n matrix indicating a relationship between n process variables and n principal components, and is called a loading matrix.
  • T is a sub-matrix of T_a in which the principal components are truncated by p ( ⁇ n), and this T is generally called a score matrix.
  • P is a sub-matrix (n ⁇ p) of P_a in which the main component is truncated by p, and this P is generally called a loading matrix.
  • E is an error matrix (m ⁇ n) based on the number m of samples and the number n of process variables, and represents an error when the number of principal components is cut off by p.
  • T_a is referred to as a score matrix, and T is referred to as a main score matrix.
  • P_a is referred to as a loading matrix, and P is referred to as a main loading matrix. If these matrices are used, the Q statistic Q (x (t)) and Hotelling's T ⁇ 2 statistic T ⁇ 2 (x (t)) are defined as the following equations (2) and (3).
  • x (t) represents the t-th element of the past time-series data X.
  • I is a unit matrix of an appropriate size.
  • ⁇ is a matrix having the variance of the principal component as a diagonal element, which means normalization of the variance. At the time of online monitoring to be described later, this x (t) is calculated by replacing the process data measured online.
  • either one of the Q statistic or Hotelling's T ⁇ 2 statistic may be defined as the process health index.
  • the Q statistic tends to show (detect) minor fluctuations. Such minor fluctuations include abnormal signs that are difficult for the user to notice only by looking at one process variable. Therefore, when the purpose is to monitor such a small change, it is preferable to use the Q statistic as the process soundness index.
  • Hotelling's T ⁇ 2 statistic tends to detect relatively large variations in each process variable. Therefore, when it is desired to monitor a clear change in the process, it is preferable to use Hotelling's T ⁇ 2 statistic as the process soundness index. Also, any fluctuations, including signs of process anomalies, are detected in either the Q statistic or the Hotelling T ⁇ 2 statistic.
  • the one having a larger value of the Q statistic and the Hotelling's T ⁇ 2 statistic may be used as the process soundness index.
  • the sum of the Q statistic and the Hotelling's T ⁇ 2 statistic (that is, a comprehensive abnormality index) may be used as the process soundness index.
  • the degree of soundness may be defined in a range of 0 to 1, wherein "1" indicates that the soundness is completely sound, and "0" may be defined so as to indicate that the soundness is completely abnormal.
  • a value obtained by converting the Q statistic and the T ⁇ 2 statistic by a conversion formula such as exp ( ⁇ a ⁇ statistic) may be defined as the soundness.
  • “a” is an adjustment parameter and is a value larger than 0.
  • the contribution ratio definition unit 213 defines a process soundness contribution ratio of each process variable with respect to the index defined by the index definition unit 212.
  • the contribution to each statistic may be defined as the process soundness contribution.
  • the contribution of the Q statistic and the contribution of the Hotelling's T ⁇ 2 statistic are defined as in the following Expressions (4) and (5), respectively.
  • Equation (4) is an equation representing the projection of the Q statistic on the axis of the nth process variable, and it is possible to calculate how much each process variable contributes to the Q statistic by Equation (5).
  • Equation (5) is an equation for decomposing the T ⁇ 2 statistic into the sum of n process variable components. Using equation (5), it is possible to calculate how much each process variable contributes to the T ⁇ 2 statistic.
  • the process soundness index If the larger of the Q statistic and Hotelling's T ⁇ 2 statistic is used as the process soundness index, the corresponding contribution to the larger statistic may be used as the process soundness contribution rate. .
  • the sum of the Q statistic and the Hotelling's T ⁇ 2 statistic is used as the process soundness index, the sum of the respective values obtained by Expressions (4) and (5) is the process soundness. It may be used as a contribution rate.
  • the normal state pattern generation unit 214 generates a normal state pattern by using the past data used by the index definition unit 212 and the contribution ratio definition unit 213 to define the process soundness index and the process soundness contribution ratio.
  • the normal pattern is information indicating a typical value or a typical pattern of each process variable.
  • the normal pattern generation unit 214 generates a normal pattern of each process variable using the past time-series data X. For example, the normal pattern generation unit 214 may calculate a position parameter of each time-series data of each process variable of the past time-series data X (that is, each column vector of X) as a normal pattern.
  • the position parameter is, for example, a value such as an average value, a median value, and a pruning average value.
  • a normal pattern may be obtained by such a simple method.
  • the normal pattern generation unit 214 may further calculate, as a part of the value of the normal pattern, a scale parameter indicating the degree of variation around the typical value obtained as the normal pattern.
  • the scale parameter is, for example, a value such as a standard deviation, a median absolute deviation (MAD: Median Absolute Deviation), or a quadrant.
  • the normal state pattern generation unit 214 may generate typical pattern data of each process data instead of a mere position parameter.
  • a pattern having a periodicity on a daily basis according to a human lifestyle is generally recognized.
  • holidays such as Saturday and Sunday, the behavior often differs from that on weekdays.
  • a pattern having a weekly periodicity is recognized.
  • the data for which daily and weekly patterns are recognized include not only sewage treatment processes but also water demand patterns for water purification, energy demand patterns for energy plants, traffic patterns for automobiles and other traffic, and so on. This is particularly noticeable in infrastructure plants that are closely related to human lifestyles.
  • the normal state pattern generation unit 214 calculates the position parameter in, for example, a predetermined time unit (for example, 1 minute unit, 1 hour unit), and calculates the calculated value. May be generated as a normal pattern by connecting time series data for a predetermined period (for example, one day, one week). In such a case, the scale parameter may be calculated as a part of the normal pattern.
  • the scale parameter in a predetermined time unit may be calculated in the same manner as when the normal pattern is generated. Further, the scale parameter for each column vector of the past time-series data X may be calculated uniformly.
  • the state calculation unit 22 functions as a current data acquisition unit 221, an index calculation unit 222, a contribution ratio calculation unit 223, and an important variable extraction unit 224.
  • the current data acquisition unit 221 reads the current data (online data) of each process variable from the data storage unit 202.
  • the current data is the data of each process variable at the time of monitoring (hereinafter referred to as “monitoring time”).
  • the index operation unit 222 performs an operation using the current data read by the current data acquisition unit 221 and the definition formula of the process health index defined by the index definition unit 212.
  • the index calculation unit 222 acquires an index indicating the current degree of health of the process as the calculation result.
  • a specific example of the process of the index calculation unit 222 will be described.
  • the index calculation unit 222 performs outlier (outlier) processing on the data at the time of monitoring read by the current data acquisition unit 221 as necessary. Then, the index calculation unit 222 calculates the process health index defined by the index definition unit 212 using the data on which the outlier processing has been performed.
  • the index calculating unit 222 calculates the Q statistic by inputting the data at the monitoring time into X (t) of Expression (2). Calculate the process soundness index.
  • the index calculation unit 222 inputs the data at the monitoring time point into X (t) of the T ⁇ 2 statistic defined by Expression (3). By calculating the T ⁇ 2 statistic, the process soundness index is calculated.
  • the current process health index can be calculated by performing the calculation using the equations (2) and (3).
  • the important variable extraction unit 224 sorts the process variables in the order of low process soundness contribution ratio (high order of abnormalities).
  • the important variable extracting unit 224 extracts a predetermined number of upper process variables set in advance based on the sorting result.
  • the important variable extracting unit 224 assigns a larger value to each process variable in descending order of the process soundness contribution ratio.
  • the important variable extraction unit 224 extracts a predetermined number (M) of process variables in descending order of importance.
  • the time-series monitoring data of the M process variables is displayed on a screen (a screen of the user terminal) used for monitoring by the user as described later. Therefore, the value of M may normally be set as a value of up to about ten.
  • the display control unit 23 functions as the state display control unit 231 and the monitoring data display control unit 232.
  • the state display control unit 231 generates data for displaying time-series data of the process health index calculated by the index calculation unit 222 for a predetermined period (from a predetermined past to the present: hereinafter, referred to as a “target period”). Generate.
  • the monitoring data display control unit 232 generates data for displaying the process monitoring data (time-series data) of each process variable extracted by the important variable extracting unit 224 during the target period.
  • the state display control unit 231 generates data for displaying time-series data of the process health index calculated in real time by the index calculation unit 222, as shown in the upper part 41 of FIG.
  • the time-series data of the process soundness index may be displayed as, for example, time-series data retroactive to a predetermined period in the past from the present. In the example of FIG. 2, the time-series data for the past 24 hours is displayed, but this display period may be changed according to the operation of the user.
  • the time-series data indicated by a dashed line indicates the normal pattern generated by the normal pattern generation unit 214.
  • the normal pattern may be displayed as one horizontal bar indicating the value in the graph of the time-series data.
  • Comparing the plant monitoring method of the present embodiment with the conventional plant monitoring method has the following differences.
  • the trend graph of each process data displayed on the screen is selected by the user. Therefore, the trend graph of the process data not selected by the user is not displayed on the screen.
  • the important variable extraction unit 224 determines M pieces of process monitoring data with high importance. Then, the determined M pieces of process monitoring data are displayed on the screen of the user terminal 3.
  • the normal pattern generated by the normal pattern generating unit 214 is displayed on a trend graph. Therefore, it can be seen at a glance how much the current trend graph deviates from a typical pattern (normal pattern). Accordingly, the user can easily and visually and intuitively understand how much the situation at the time of monitoring has deviated from the typical state, without necessarily determining whether the system is abnormal or normal.
  • the normal pattern and the time-series data of the actually measured values are simultaneously displayed.
  • the fluctuation range of the normal value hereinafter, referred to as “normal fluctuation range”. May be displayed.
  • the normal fluctuation range is a range in which the value fluctuates in a state where no abnormality has occurred.
  • the normal fluctuation range may be set based on a predetermined criterion based on the scale parameter of the time-series data in the past predetermined period.
  • a representative value such as an average value and a control limit value defined by a standard deviation may be displayed.
  • a user has been monitoring a plant using a trend graph of process monitoring data, whereas a method of monitoring an abnormal degree (contribution plot) of process monitoring data using MSPC has a problem that it is difficult for a user to intuitively understand. there were. Further, when the user feels that something is abnormal, it is necessary to open and confirm the trend graph by himself. Therefore, the affinity with the conventional monitoring method is low. That is, conventionally, a trend graph is always displayed even in a normal case, but MSPC has low affinity with such a configuration.
  • FIG. 6 is a display example when a normal pattern (dashed-dotted line) and measured value data (solid line) are displayed.
  • a normal MSPC the state shown in FIG. 6 is determined to be abnormal. If there is no display corresponding to the normal state pattern, it is difficult for a user with little experience to determine why the state shown in FIG. 6 is abnormal. In such a case, by displaying the normal pattern and the data of the actually measured values together, the movement to be referred to is visualized, so that it is easier to grasp the current situation.
  • the user confirms “process variables to be viewed now” automatically determined by the system as an extension of the conventional plant monitoring of “monitoring process data by a trend graph”. be able to. Therefore, plant monitoring can be performed efficiently. Furthermore, when the normal pattern of the “process variable to be viewed now” is displayed, it is easy to determine whether the time-series data of the actually measured values up to the monitoring time point deviates from the normal pattern (whether or not it is abnormal). It is possible to make a judgment.
  • FIG. 9 is a block diagram illustrating a functional configuration of a process monitoring support device 2a according to the second embodiment.
  • the process monitoring support device 2a according to the second embodiment includes a state definition unit 21a, a state calculation unit 22a, and a display control unit 23a instead of the state definition unit 21, the state calculation unit 22, and the display control unit 23.
  • the display control unit 23a receives an instruction to change the display content from the user terminal 3, and changes the display according to the instruction.
  • a process monitoring support device 2a according to the second embodiment will be described.
  • the contribution ratio definition unit 213 defines the process soundness contribution ratio of each process variable of the process soundness index as in the following equation (6).
  • Equation (6) indicates that when ⁇ i (t) and ⁇ i (t) are defined by an average value and a standard deviation, respectively, without depending on time t, a statistic called a Z value used in SPC or the like is obtained. Corresponding. This value indicates the evaluation of the variation around the average value by the magnification relative to the standard deviation.
  • the index definition unit 212a defines a process health index by integrating the process health contribution rates defined by Expression (6).
  • the integration method may be any method.
  • the sum or average of the process soundness contribution rates calculated by equation (6) may be defined as the process soundness index.
  • the maximum value of the process soundness contribution rate calculated by the equation (6) (that is, the one closest to the abnormality) may be defined as the process soundness index.
  • the process soundness index When the process soundness index is defined by the sum of the process soundness contribution rates, it may be set to n (the number of process variables) times the reference set for the process soundness contribution rates.
  • the process soundness index When the process soundness index is defined by the maximum value of the process soundness contribution rate, the same threshold value may be used as a criterion. As described above, by applying the concept of the control limit of the SPC, the process soundness index and the abnormality judgment criterion of the process soundness contribution ratio are set.
  • an abnormality determination criterion in a normal MSPC may be applied to the abnormality determination criterion. That is, for example, the abnormality determination criterion may be set using the theoretical threshold values of the following equations (7) and (8) for the Q statistic and the T ⁇ 2 statistic. Equation (7) is the theoretical threshold of the Q statistic. Equation (8) is the theoretical threshold of the T ⁇ 2 statistic.
  • p is the number of process variables left in the model.
  • ⁇ _i is a diagonal element of ⁇ . That is, ⁇ _i is the sum of the i-th power of each component included in the error term.
  • Equation (8) p is the number of selected process variables (remaining in the model). m is the number of all process variables.
  • F (p, mp, ⁇ ) represents the F distribution when the degree of freedom is (p, mp) and the confidence limit is ⁇ . Note that 0.01 or 0.05 is often used for ⁇ .
  • An abnormality determination threshold value can be determined using Expression (7) or Expression (8) according to the statistics used for the process soundness index.
  • the statistics include the Q statistics, the T ⁇ 2 statistics, the sum of the Q statistics and the T ⁇ 2 statistics, and the maximum values of the Q statistics and the T ⁇ 2 statistics.
  • a threshold value within a predetermined range is defined as the ratio of the process soundness contribution rate of each process variable to the statistics (process soundness contribution rate / statistics). (For example, 20% to 50%), and a criterion for determining a process variable having a large process soundness contribution rate as abnormal can be provided.
  • the operations of the current data acquisition unit 221, the index calculation unit 222, the contribution ratio calculation unit 223, and the important variable extraction unit 224 in the second embodiment are almost the same as the operations of the same function unit in the first embodiment.
  • the MSPC is applied to the definition of the process soundness index and the process soundness contribution ratio
  • the process soundness index and the process soundness are deviated from typical patterns. The calculation is different because the contribution is defined.
  • the state calculation unit 22a in the second embodiment further includes an abnormal data extraction unit 225.
  • the abnormal data extraction unit 225 determines the process health index calculated by the index calculation unit 222 and the process health contribution ratio of each process monitoring data calculated by the contribution ratio calculation unit 223 in the determination criterion definition unit. It is determined based on the criteria defined by H.215 whether it is normal.
  • the abnormal data extraction unit 225 adds a flag indicating a determination result to each value. For example, a flag “0” may be added for normal, and a flag “1” for abnormal.
  • the display control unit 23a according to the second embodiment further includes a monitoring point display control unit 233 and a contribution rate display control unit 234.
  • FIGS. 10 and 11 are diagrams illustrating a specific example of a screen displayed by the operation of the display control unit 23a according to the second embodiment.
  • the display control unit 23a will be described with reference to FIGS.
  • abnormal data data to which a flag of abnormal has been added by the abnormal data extracting unit 225 in displaying the time series data of the process health index.
  • abnormal data data to which a flag of normal is added: hereinafter, referred to as “normal data”.
  • the abnormal data may be displayed, for example, in an emphasized manner. Specifically, the abnormal data may be displayed in a more prominent color (for example, red) than the normal data, or may be displayed using a prominent line type (for example, a thicker line). Further, the display may be performed in a different manner depending on the magnitude of the abnormality of the abnormality data.
  • a flicker display may be performed for a case where the degree of abnormality (degree of deviation) is larger than a predetermined reference. Further, the larger the degree of the abnormality, the larger the display area of the graph may be displayed.
  • abnormal data is indicated by a broken line
  • normal data is indicated by a solid line.
  • the threshold value defined by the criterion definition unit 215 may be displayed in the time series data of the process health index.
  • the threshold value may be displayed in a line type (for example, yellow) different from the abnormal data and the normal data.
  • abnormal data and normal data may be displayed in different modes.
  • abnormal data may be displayed in a manner emphasized compared to normal data.
  • the display control unit 23a may be configured to control the user terminal 3 so as to issue an audio alarm when the highlighted display is performed. It is also possible to simultaneously issue an alarm by voice or text. When the alarm is issued by voice, the alarm may be issued at a volume lower than the normal volume. With such a configuration, it is possible to support the user's plant monitoring without overemphasizing “abnormality” and avoiding flooding of alarms.
  • the monitoring point display control unit 233 controls not a display of time-series data of process variables such as a trend graph, but a display of devices installed in a plant as in a process flow.
  • FIG. 11 is a diagram illustrating a specific example of a screen controlled by the monitoring point display control unit 233.
  • the monitoring point display control unit 233 performs highlighting near the image of the device related to the abnormal data. For example, near the device from which the abnormal data was obtained, the name of the process variable from which the abnormal data was obtained may be displayed. At this time, the names of the process variables may be displayed in the same manner (for example, red) as the highlighting performed by the monitoring data display control unit 232.
  • the character strings “aeration air volume” and “MLSS” are displayed as specific examples of the name of the process variable from which the abnormal data was obtained.
  • the monitoring point display control unit 233 displays an image of this device or an image such as the name of the above-described process variable to prompt the user to pay attention to this image.
  • an image of an arrow in which an arrow is directed to an image of a device or an image of a name of a process variable may be displayed.
  • the target image may be displayed in the same mode (for example, red) as the highlighting performed by the monitoring data display control unit 232.
  • the image of the arrow is only a specific example of the image of interest.
  • the image of interest may be a character such as "!, An image of a star, or an image indicating that the image is generally dangerous.
  • FIG. 11 to the left of the character strings “aeration air volume” and “MLSS”, an image of a right-pointing arrow with an arrow directed to these character strings is displayed as a specific example of the image of interest.
  • an image indicating the time-series data of the process variable from which the abnormal data has been acquired may be further displayed.
  • an attention image or the like is displayed in the process flow because abnormal data is obtained for the aeration air volume and the MLSS, but the time series of the aeration air volume and the MLSS for which the abnormal data is obtained along with the process flow are further displayed.
  • An image showing the data is displayed.
  • the monitoring point display control unit 233 displays the display including the device related to the time-series data of the selected process variable (for example, the process flow may be displayed as shown in FIG.
  • the ⁇ contribution ratio display control unit 234 displays a process variable having a large value of the process soundness contribution ratio of each process variable.
  • the contribution rate display control unit 234 generates the contribution rate image 43, for example.
  • a graph for example, a bar graph
  • the contribution image 43 may display, for example, the names of a predetermined number of process variables and the values of the process health contribution in the descending order of the process soundness contribution. It is preferable that the contribution rate image 43 is displayed as a bar graph sorted in a criterion such as a descending order of the value of the process soundness contribution rate.
  • the arrangement of the process variables in the contribution ratio image 43 may be arranged vertically or horizontally.
  • the selection may be performed by an operation of placing the cursor on the graph and clicking the graph. Then, the user selects the process variable displayed on the contribution rate image 43.
  • the monitoring data display control unit 232 displays a graph of the time-series data of the process variable selected by the user in an area where the process data of “total energy intensity” is displayed. At this time, the magnitude of the process soundness contribution ratio is displayed in a bar graph. Therefore, the user can select the process data to be displayed while referring to the magnitude of the process soundness contribution ratio.
  • FIG. 12 is a flowchart illustrating an example of a processing flow of the state definition unit 21a of the process monitoring support device 2a according to the second embodiment.
  • the state defining unit 21a waits until the timing of the predetermined cycle T0 (step S301-NO).
  • the past data acquisition unit 211 acquires past data (step S302).
  • the normal pattern generation unit 214 generates a normal pattern using the acquired past data (step S303).
  • the contribution ratio definition unit 213a defines a process soundness contribution ratio for each process variable using the acquired normal pattern and past data (step S304).
  • the index definition unit 212a defines a process health index using the calculated process health contribution rate (step S305).
  • the process soundness index, the process soundness contribution rate, and the normal state pattern thus obtained are used in the subsequent processing of the state calculation unit 22a.
  • the processing of the state calculation unit 22a is basically the same as that of the first embodiment, and a description thereof will be omitted.
  • the process soundness index is defined in a stacked manner based on the deviation from the normal state pattern without using a special diagnosis algorithm.
  • Such a process has an effect that the soundness can be easily interpreted from the viewpoint that the user monitors the sign of abnormality. That is, since a special diagnosis algorithm is not used, when the soundness is poor (the degree of abnormality is high), it can be easily determined by comparing the badness with the normal pattern.
  • the data determined to be abnormal data by the abnormal data extraction unit 225 is displayed in a mode different from normal data.
  • the abnormal data may be displayed as highlighting.
  • the monitoring point display control unit 233 performs highlighting near the image of the device related to the abnormal data. Therefore, when an abnormality occurs in the plant, the user can more easily estimate the location of the abnormality and the cause of the abnormality.
  • the contribution rate image 43 is displayed. Therefore, based on the contribution rate image 43, the user can easily select a process variable of the trend graph that the user wants to display on the screen. Therefore, plant monitoring that easily reflects the user's intention can be realized.
  • FIG. 13 is a diagram illustrating a modification of the second embodiment.
  • a part of the function implemented as the process monitoring support device 2a in the second embodiment is implemented in an information processing device (process monitoring support server) installed at a remote location via the network 4. I have.
  • the process monitoring support server 9 and the process monitoring support device 2b are communicably connected via the network 4.
  • the process monitoring support server 9 and the process monitoring support device 2b are communicably connected to each other by the communication units (the communication unit 91 and the communication unit 24) included therein functioning.
  • the functions of the state definition unit 21a and the state calculation unit 22a in the second embodiment are implemented in the process monitoring support server 9.
  • the data collection unit 201 of the process monitoring support device 2b transmits the data recorded in the data storage unit 202 to the process monitoring support server 9 at a predetermined timing.
  • the timing may be a timing at which the past data acquisition unit 211 of the process monitoring support server 9 transmits request data indicating that data is requested, or a timing of a predetermined cycle.
  • the state definition unit 21a of the process monitoring support server 9 performs a process based on the received data.
  • the data collection unit 201 of the process monitoring support device 2b transmits the current data to the process monitoring support server 9 at a predetermined timing.
  • the state calculation unit 22a of the process monitoring support server 9 performs a process based on the received current data.
  • the state calculation unit 22a transmits data indicating the processing result to the process monitoring support device 2b.
  • the display control unit 23a of the process monitoring support device 2b generates data representing a screen based on the received data, and causes the user terminal 3 to display the data.
  • the function of the display control unit 23a may be implemented in the process monitoring support server 9.
  • the data collection unit 201 and the data storage unit 202 may be installed in the plant as devices different from the process monitoring support device 2b.
  • the data collection unit 201 may transmit the past data and the current data by communicating with the process monitoring support server 9.
  • the process monitoring support device 2b itself may be implemented as a user terminal.
  • the user terminal may be a mobile terminal device such as a smartphone or tablet.
  • the functions of the process monitoring support device 2 described in the first and second embodiments can be implemented in a cloud, and a support service can be provided to a plant or the like where a user is located.
  • the state calculation unit 22 and the state calculation unit 22a do not necessarily need to be designed to process only the time-series data (current data) at the time of monitoring.
  • the processing may be performed on time-series data at a past point in time specified by the user.
  • an advanced monitoring / diagnosis system can be monitored while maintaining a plant monitoring method having high affinity with plant monitoring currently widely performed. It becomes possible to incorporate into. As a result, it is possible to realize efficient plant monitoring and plant monitoring that can reduce the possibility of the user overlooking the state at an abnormal time (for example, at the time of abnormality) and can respond more quickly.

Abstract

Le but de la présente invention est d'utiliser plus efficacement un résultat d'analyse pour des données obtenues à partir d'un processus tout en conservant un affichage qu'un utilisateur a l'habitude d'utiliser. Un dispositif d'aide à la surveillance de processus (2) selon un mode de réalisation comprend une unité d'acquisition de données (221), une unité de calcul d'indice (222), une unité de calcul de taux de contribution (223) et une unité de commande d'affichage (23). L'unité d'acquisition de données acquiert des données chronologiques pour une pluralité de types de variables de processus indiquant un état d'un processus (1) qui est surveillé. L'unité de calcul d'indice calcule un indice indiquant la probabilité qu'aucune anomalie ne survienne dans le processus qui est surveillé et que le processus soit sain, sur la base de la pluralité de types de variables de processus. Relativement à l'indice calculé par l'unité de calcul d'indice, l'unité de calcul de taux de contribution calcule, pour chaque type de variable de processus de la pluralité de types de variables de processus, un taux de contribution indiquant le pourcentage de contribution à une probabilité accrue qu'aucune anomalie ne survienne dans le processus surveillé et que le processus soit sain. L'unité de commande d'affichage génère des informations d'affichage de façon à afficher, sur un écran d'un terminal utilisateur (3), des informations concernant une partie des variables de processus pour laquelle le taux de contribution est relativement faible.
PCT/JP2019/036119 2018-09-14 2019-09-13 Dispositif d'aide à la surveillance de processus, système d'aide à la surveillance de processus, procédé d'aide à la surveillance de processus, programme d'aide à la surveillance de processus et dispositif terminal WO2020054850A1 (fr)

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MX2021002926A MX2021002926A (es) 2018-09-14 2019-09-13 Dispositivo de soporte a la supervision de procesos, sistema de soporte a la supervision de procesos, metodo de soporte a la supervision de procesos, programa de soporte a la supervision de procesos y dispositivo terminal.
PH12021550545A PH12021550545A1 (en) 2018-09-14 2021-03-11 Process monitoring support device, process monitoring support system, process monitoring support method, process monitoring support program, and terminal device

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JP2018173057A JP7163117B2 (ja) 2018-09-14 2018-09-14 プロセス監視支援装置、プロセス監視支援システム、プロセス監視支援方法及びプロセス監視支援プログラム

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US20090312851A1 (en) * 2005-07-25 2009-12-17 Biogen Idec Ma Inc. System and Method for Bioprocess Control
WO2012090937A1 (fr) * 2010-12-28 2012-07-05 株式会社 東芝 Dispositif de surveillance d'état d'un processus
JP2013008111A (ja) * 2011-06-22 2013-01-10 Hitachi Engineering & Services Co Ltd 異常予兆診断装置および異常予兆診断方法
JP2013093027A (ja) * 2011-10-24 2013-05-16 Fisher Rosemount Systems Inc 予測された欠陥分析
WO2014073261A1 (fr) * 2012-11-09 2014-05-15 株式会社 東芝 Dispositif de surveillance/diagnostic de procédé et programme de surveillance/diagnostic de procédé
JP2017126258A (ja) * 2016-01-15 2017-07-20 横河電機株式会社 監視制御システム及び作業支援方法
JP2018120343A (ja) * 2017-01-24 2018-08-02 株式会社東芝 プロセス診断装置、プロセス診断方法及びプロセス診断システム

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090312851A1 (en) * 2005-07-25 2009-12-17 Biogen Idec Ma Inc. System and Method for Bioprocess Control
JP2008217617A (ja) * 2007-03-06 2008-09-18 Toshiba Corp プラント状態指標管理装置とその実現用コンピュータプログラム
WO2012090937A1 (fr) * 2010-12-28 2012-07-05 株式会社 東芝 Dispositif de surveillance d'état d'un processus
JP2013008111A (ja) * 2011-06-22 2013-01-10 Hitachi Engineering & Services Co Ltd 異常予兆診断装置および異常予兆診断方法
JP2013093027A (ja) * 2011-10-24 2013-05-16 Fisher Rosemount Systems Inc 予測された欠陥分析
WO2014073261A1 (fr) * 2012-11-09 2014-05-15 株式会社 東芝 Dispositif de surveillance/diagnostic de procédé et programme de surveillance/diagnostic de procédé
JP2017126258A (ja) * 2016-01-15 2017-07-20 横河電機株式会社 監視制御システム及び作業支援方法
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