WO2023176467A1 - 要因推論装置、要因推論方法、要因推論システムおよび端末装置 - Google Patents

要因推論装置、要因推論方法、要因推論システムおよび端末装置 Download PDF

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WO2023176467A1
WO2023176467A1 PCT/JP2023/007733 JP2023007733W WO2023176467A1 WO 2023176467 A1 WO2023176467 A1 WO 2023176467A1 JP 2023007733 W JP2023007733 W JP 2023007733W WO 2023176467 A1 WO2023176467 A1 WO 2023176467A1
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information
node
knowledge model
factor
nodes
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English (en)
French (fr)
Japanese (ja)
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洋平 原田
丈英 平田
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JFE Steel Corp
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JFE Steel Corp
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Priority to CN202380025397.7A priority Critical patent/CN118742867A/zh
Priority to EP23770425.9A priority patent/EP4474932A4/en
Priority to US18/842,593 priority patent/US20250181942A1/en
Priority to JP2023536386A priority patent/JP7578199B2/ja
Priority to KR1020247029224A priority patent/KR20240142529A/ko
Publication of WO2023176467A1 publication Critical patent/WO2023176467A1/ja
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G06N5/045—Explanation of inference; Explainable artificial intelligence [XAI]; Interpretable artificial intelligence
    • 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
    • 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
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
    • 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
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0275—Fault isolation and identification, e.g. classify fault; estimate cause or root of failure
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation
    • G06N5/022—Knowledge engineering; Knowledge acquisition

Definitions

  • the present invention relates to a factor inference device, a factor inference method, a factor inference system, and a terminal device.
  • Non-Patent Document 1 proposes a method of calculating relative influence values for input variables that play an important role.
  • Non-Patent Document 1 does not provide information regarding the mechanism of how each variable influences prediction and detection. Therefore, currently, these are estimated using the user's predictions and knowledge of the detection target. Therefore, non-experts and inexperienced users cannot make accurate guesses and cannot take appropriate actions.
  • Patent Document 1 attempts to present the influence of variables by automatically outputting the causal relationships between variables.
  • the present invention has been made in view of the above, and even when used by a user who does not have deep knowledge of the subject of analysis, it is possible to obtain auxiliary information useful for problem solving from operational data such as sensor data and setting values. It is an object of the present invention to provide a factor inference device, a factor inference method, a factor inference system, and a terminal device that can give the following information and lead to appropriate and prompt actions.
  • a factor inference device is a factor inference device for inferring the factors of a phenomenon in a process, and the factor inference device infers the cause of a phenomenon in a process, and the factor inference device infers the cause of a phenomenon in a process.
  • knowledge model acquisition means for acquiring a knowledge model expressed in the form of a network connecting the nodes, and creating information including at least an abnormality index regarding the event based on the data collected from the process; information creation means for searching for information on nodes in the knowledge model based on the names of data collected from the process, and node extraction means for extracting corresponding nodes;
  • the present invention includes a data combining means for linking the information, and a factor inference means for inferring and presenting the cause of the phenomenon based on the structure of the knowledge model and the information linked to the node.
  • the node extraction means searches for character information that is the same as or similar to the name of the data from the character information of the event corresponding to the node of the knowledge model.
  • the node is extracted by
  • the factor inference means presents a causal route connecting the nodes in the knowledge model and indicating a causal relationship between the phenomena, and the causal route is If there are multiple causal routes, the causal routes are ranked and presented based on the information linked to each node of the causal routes.
  • the factor inference means calculates the number of nodes to which an abnormality index indicating an abnormality is connected in the causal route, or the number of nodes selected as a causal route in the past. Based on the number, the causal routes are ranked and presented.
  • the factor inference means blocks the knowledge model based on the information, connects the nodes in the knowledge model, and establishes a causal relationship between the phenomena. A causal route indicating the above is presented for each block.
  • the abnormality index includes at least one of the categories determined based on the degree of abnormality indicating the degree of abnormality and the degree of deviation from a predetermined normal state. One is included.
  • a factor inference method is a factor inference method that is executed by a device constructed by a computer and infers the cause of a phenomenon in a process, a knowledge model acquisition step, in which a knowledge model acquisition means included in the computer acquires a knowledge model expressed in a network format connecting the nodes, with events occurring in the process as nodes, regarding the causal relationship of phenomena in the process; an information creation step in which an information creation means included in the computer creates information including at least an abnormality index regarding the event based on data collected from the process; a node extracting step of searching the information of the node of the knowledge model based on the name of the node and extracting the corresponding node; and a data combining means included in the computer extracting the information corresponding to the extracted node. and a factor inference step in which a factor inference means included in the computer infers and presents a factor of the phenomenon based on the structure of the knowledge model and the
  • a factor inference system includes a factor inference server device and a terminal device, wherein the factor inference server device is a process Regarding the causal relationship of the phenomenon in the previous article, a knowledge model acquisition means that acquires a knowledge model expressed in a network format connecting the nodes, with events that occur in the process as nodes, and based on the data collected from the process, information creation means for creating information including at least an abnormality index regarding the phenomenon; and node extraction means for searching for information on nodes of the knowledge model and extracting corresponding nodes based on the names of the data collected from the process; data linking means for linking the corresponding information to the extracted node; factor inference means for inferring the cause of the phenomenon based on the structure of the knowledge model and the information linked to the node; output means for outputting information including at least the cause of the phenomenon inferred by the factor inference means to the terminal device, the terminal device acquiring information including at least the cause of the phenomenon from
  • a terminal device comprises: an information acquisition means for acquiring information including at least a factor of a phenomenon in a process from a factor inference server device; a display means for displaying the acquired information, the cause of the phenomenon is a knowledge model expressed in the form of a network connecting the nodes, with events that occur in the process as nodes, regarding the causal relationship of the phenomenon in the process.
  • information inferred based on a structure and, in the knowledge model, information associated with a node extracted based on a name of data collected from the process, the information including at least an anomaly indicator regarding the event. It is.
  • factor inference device factor inference method, factor inference system, and terminal device according to the present invention
  • problems can be solved from operational data such as sensor data and setting values.
  • operational data such as sensor data and setting values.
  • useful auxiliary information This allows the user to take appropriate and prompt action to solve the problem using this auxiliary information.
  • FIG. 1 is a diagram showing a schematic configuration of a factor inference device according to an embodiment of the present invention.
  • FIG. 2 is a flowchart showing the procedure of the factor inference method executed by the factor inference device according to the embodiment of the present invention.
  • FIG. 3 is a diagram showing an example of a knowledge model in the factor inference method according to the embodiment of the present invention.
  • FIG. 4 is a diagram showing an example in which some nodes of a knowledge model are blocked in the factor inference method according to the embodiment of the present invention.
  • FIG. 5 is a diagram illustrating an example of an abnormality index (abnormality/normality classification) acquired by the standalone monitoring system in the factor inference method according to the embodiment of the present invention.
  • FIG. 1 is a diagram showing a schematic configuration of a factor inference device according to an embodiment of the present invention.
  • FIG. 2 is a flowchart showing the procedure of the factor inference method executed by the factor inference device according to the embodiment of the present invention.
  • FIG. 6 is a diagram showing an example of an abnormality index (degree of abnormality) acquired by the abnormality detection system in the factor inference method according to the embodiment of the present invention.
  • FIG. 7 is a diagram showing an example of monitoring items automatically assigned to a knowledge model in the factor inference method according to the embodiment of the present invention.
  • FIG. 8 is a diagram illustrating an example of the result of factor inference using the abnormal/normal classification in the factor inference method according to the embodiment of the present invention.
  • FIG. 9 is a diagram illustrating an example of a factor inference result using abnormality degree in the factor inference method according to the embodiment of the present invention.
  • FIG. 10 is a diagram showing a modification example in which the factor inference method according to the embodiment of the present invention is applied to predicting the surface quality of a steel plate.
  • FIG. 11 is a diagram showing a schematic configuration of a factor inference system according to an embodiment of the present invention.
  • FIG. 12 is a diagram showing an example of a knowledge model in an example of the factor inference method according to the embodiment of the present invention.
  • FIG. 13 is a diagram showing an example of a factor inference result in an example of the factor inference method according to the embodiment of the present invention.
  • a factor inference device, a factor inference method, a factor inference system, and a terminal device according to embodiments of the present invention will be described with reference to the drawings. Note that the present invention is not limited to the following embodiments, and the components in the following embodiments include those that can be easily replaced by those skilled in the art, or those that are substantially the same.
  • a factor inference device is for inferring the factors of a phenomenon in a process.
  • the factor inference device includes a manufacturing process for steel products, a power generation process for power generation equipment, a transport process for transport equipment, and the like.
  • the hot rolling process is a process of rolling a heated slab to a predetermined thickness.
  • the hot rolling process uses a large number of equipment such as heating furnaces, rolling mills, and quenching equipment, but if an abnormality occurs in the equipment, there is a possibility of serious problems such as breakage of the plate during rolling or perforation. There is. Therefore, it is important for stable production to infer the cause of the abnormality and present it to the user (operator, etc.) to prevent these serious troubles.
  • FIG. 1 shows an example of the configuration of an information processing device 1 for realizing a factor inference device.
  • This information processing device 1 is realized by, for example, a general-purpose computer such as a workstation or a personal computer. Further, the information processing device 1 includes an input section 10, a storage section 20, a calculation section 30, and an output section 40, as shown in FIG.
  • the input unit 10 is an input means for the calculation unit 30, and is realized by, for example, an input device such as a keyboard, a mouse pointer, or a numeric keypad.
  • the input unit 10 inputs information necessary for various calculations in the calculation unit 30.
  • the storage unit 20 is realized by a recording medium such as an EPROM (Erasable Programmable ROM), a hard disk drive (HDD), and a removable medium.
  • a recording medium such as an EPROM (Erasable Programmable ROM), a hard disk drive (HDD), and a removable medium.
  • removable media include disc recording media such as a USB (Universal Serial Bus) memory, a CD (Compact Disc), a DVD (Digital Versatile Disc), and a BD (Blu-ray (registered trademark) Disc).
  • the storage unit 20 can store an operating system (OS), various programs, various tables, various databases, and the like.
  • a knowledge model 21 is stored in the storage unit 20. Note that in addition to the knowledge model 21, the storage unit 20 may also store calculation results in the calculation unit 30, etc., as necessary.
  • the knowledge model 21 is a model in which the causal relationship between phenomena in a process is expressed in a network format in which events that occur in the process are nodes, and the nodes are connected.
  • This knowledge model 21 is, for example, created manually in advance and stored in the storage unit 20 in a form that can be read by a computer.
  • only one knowledge model 21 may be created so as to correspond to a plurality of phenomena (troubles), or a plurality of knowledge models 21 may be created corresponding to a plurality of phenomena. Note that details of the knowledge model 21 will be described later (see FIGS. 3 and 4).
  • the calculation unit 30 is realized by a processor such as a CPU (Central Processing Unit), and a memory (main memory) such as a RAM (Random Access Memory) or a ROM (Read Only Memory).
  • a processor such as a CPU (Central Processing Unit)
  • a memory main memory
  • RAM Random Access Memory
  • ROM Read Only Memory
  • the calculation unit 30 loads a program into the work area of the main storage unit and executes it, and controls each component through the execution of the program, thereby achieving functions that meet a predetermined purpose.
  • the calculation unit 30 functions as a knowledge model acquisition unit 31, an information creation unit 32, a node extraction unit 33, a data combination unit 34, and a factor inference unit 35 through execution of the above-described program.
  • FIG. 1 shows an example in which the functions of each part of the calculation unit 30 are realized by one computer, the method of realizing the functions of each part is not particularly limited. For example, the function of each part is realized by multiple computers. You may realize each of them.
  • the knowledge model acquisition unit 31 acquires the knowledge model 21 from the storage unit 20.
  • the information creation unit 32 collects operational data from the process, and creates information that includes at least an abnormality index regarding the event based on the operational data.
  • the information created by the information creation unit 32 is information that is linked to nodes of the knowledge model 21 in a data combination unit 34, which will be described later.
  • this information may include, for example, data format, equipment type, event timing, and the like.
  • the abnormality index includes, for example, an abnormality degree (abnormality score (abnormal value)) indicating the degree of abnormality, a classification determined based on the degree of deviation from a predetermined normal state, and the like. Note that the "classification determined based on the degree of deviation from the normal state" indicates, for example, a determination result of normality/abnormality.
  • abnormality degree abnormality score (abnormal value)
  • classification determined based on the degree of deviation from the normal state indicates, for example, a determination result of normality/abnormality.
  • the data format includes, for example, observed values, set values, categories, etc.
  • observed value refers to sensor data of equipment acquired by a sensor.
  • setting value refers to a setting value set in the equipment.
  • category indicates the type of data.
  • the timing of the event includes the time of setting calculation, during operation, and during non-operation. Note that “at the time of setting calculation” indicates that the event occurred during setting calculation. Furthermore, “during operation” indicates that the event occurred during operation. Furthermore, “during non-operation” indicates that the event occurred during non-operation.
  • the node extraction unit 33 searches for node information in the knowledge model 21 based on the name of the data collected from the process, and extracts the corresponding node. Specifically, the node extraction unit 33 extracts a node from the character information of the event corresponding to the node of the knowledge model 21 by searching for character information that is the same as or similar to the name of the data. Then, monitoring items related to the content of the event of the extracted node are automatically assigned to the extracted node based on the similarity of characters. Note that details of the processing of the node extracting unit 33 will be described later (see FIG. 7).
  • the data linking unit 34 links information corresponding to the node (information created by the information creating unit 32) to the node of the knowledge model 21 extracted by the node extracting unit 33. Note that details of the processing of the data combining unit 34 will be described later (see FIGS. 8 and 9).
  • the factor inference unit 35 infers the factors that cause the phenomenon based on the structure of the knowledge model 21 and the information linked to the nodes of the knowledge model 21, and presents it to the user. Specifically, the factor inference unit 35 identifies a causal route that connects nodes in the knowledge model 21 and indicates a causal relationship between phenomena. Then, the factor inference unit 35 presents the causal route to the user through the output unit 40.
  • the factor inference unit 35 ranks and presents the causal routes based on information linked to each node of the causal routes.
  • the factor inference unit 35 ranks the causal routes based on, for example, the number of nodes to which an abnormality index indicating an abnormality is connected in the inferred causal route, or the number of nodes selected as causal routes in the past. and can be presented.
  • the factor inference unit 35 can also divide the knowledge model 21 into blocks based on information linked to each node, and present a causal route for each block in the knowledge model 21. Note that details of each process in the factor inference unit 35 will be described later (see FIGS. 8 and 9).
  • the output unit 40 is an output means that outputs the calculation result by the calculation unit 30.
  • the output unit 40 is realized by, for example, an input device such as a display or a printer.
  • the output unit 40 outputs, for example, the knowledge model 21 created in advance, the inference result of the cause of the phenomenon (for example, a causal route) in the factor inference unit 35, and the like.
  • the factor inference method includes a knowledge model acquisition step (step S1), an operation data collection step (step S2), an information creation step (step S3), a node extraction step (step S4), A data combination step (step S5), a factor inference step (step S6), and a factor presentation step (step S7) are performed in this order.
  • step S1 knowledge model acquisition step
  • step S2 operation data collection step
  • step S3 information creation step
  • step S4 a node extraction step
  • a data combination step step S5
  • a factor inference step step S6
  • a factor presentation step step S7
  • ⁇ Knowledge model acquisition step> the knowledge model acquisition unit 31 acquires the knowledge model 21 stored in the storage unit 20 in advance. The details of the knowledge model 21 will be explained below.
  • the knowledge model 21 is a model in which the causal relationship between phenomena in a process is expressed in a network format in which events that occur in the process are nodes and the nodes are connected.
  • a computer in order to use a computer to infer how events related to process monitoring items will develop and what kind of trouble they will lead to, a computer will use knowledge about the mechanisms of phenomena that cause trouble. It is prepared as a processable knowledge model 21.
  • the knowledge model 21 expresses the mechanism of trouble occurrence using a network structure in the form of a causal chain that connects events with causal relationships.
  • the final result of this causal chain that is, the destination (top) of each event, becomes the trouble to focus on in the process.
  • events such as equipment setting conditions, physical phenomena, equipment states, etc. related to troubles are treated as nodes in a network structure, and causally related events are connected by edges.
  • Specific troubles (trouble A in FIG. 3) described at the top of the knowledge model 21 include, for example, holes, bends, and breaks in the hot rolling process. Furthermore, examples of events indicated by each node of the knowledge model 21 include "uneven heating of the slab in the heating furnace,” “uneven heat occurs in the slab,” “crown becomes larger,” and the like.
  • Blocking of knowledge model As will be described later, when attribute information is registered for a node of the knowledge model 21, the knowledge model 21 selects nodes corresponding to some events based on the attribute information, as shown in FIG. 4, for example. It is also possible to block.
  • block X shown in the figure includes another knowledge model 21 having common attribute information. For example, events 3, 5, and 6 shown in the figure are all expressed as one block because the attribute [timing] is "at the time of setting calculation.”
  • the information creation unit 32 collects operation data from the process. Subsequently, in the information creation step, the information creation unit 32 creates information including at least an abnormality index regarding the event based on the collected operation data.
  • operation data can be collected through, for example, an existing standalone monitoring system or an abnormality detection system, and information including abnormality indicators can be created.
  • a stand-alone monitoring system In a stand-alone monitoring system, a large amount of operational data is collected using various sensors installed in the equipment, and after determining monitoring items, monitoring is performed based on whether or not the data deviates from a predetermined threshold.
  • a monitoring method in a standalone monitoring system for example, there is a method in which a deviation of 3 ⁇ or more from the average of the normal distribution is determined to be abnormal, and a method in which the value of a monitored item deviates from the upper or lower management limit range, as shown in Figure 5. For example, there is a method of determining that there is an abnormality when the condition occurs.
  • the cause of the trouble is inferred using the abnormality index acquired by the standalone monitoring system and the knowledge model 21.
  • the abnormality index acquired by the standalone monitoring system may be an abnormal/normal classification (judgment result) as shown in FIG. 5, or may be an abnormality degree (abnormality score) indicating the degree of abnormality.
  • Anomaly detection system employ methods using machine learning to determine whether equipment is exhibiting abnormalities.
  • a single monitoring item is looked at to determine whether there is an abnormality
  • a plurality of monitoring items are looked at in combination to determine whether there is an abnormality.
  • a machine learning method in an anomaly detection system for example, a statistical-based method such as a graphical Gaussian model or principal component analysis, or a deep learning-based method such as an autoencoder can be used.
  • data of monitoring items during normal times is learned, and whether or not the current data is abnormal is output.
  • the anomaly detection system as shown in FIG. 6, for example, the degree of abnormality (abnormality score) for each monitored item is output, and whether or not a monitored item is abnormal is determined based on the magnitude of the degree of abnormality.
  • monitoring items in the hot rolling process include, for example, "meandering amount", “load difference", “slab temperature difference”, etc.
  • the number of monitoring items in the anomaly detection system is enormous, reaching several thousand items. Therefore, it is difficult to instantly determine what kind of trouble will result from a monitoring item determined to be abnormal. Furthermore, it is difficult for inexperienced users who do not have sufficient knowledge about problems to speculate about the mechanism by which phenomena related to each monitoring item can develop into problems in order to take actions to deal with problems. It is difficult. Therefore, in this embodiment, the cause of the trouble is inferred using the anomaly index acquired by the anomaly detection system and the knowledge model 21.
  • the node extraction unit 33 searches the character information of the node of the knowledge model 21 based on the name of the data collected from the process, and extracts the corresponding node. Then, monitoring items related to the content of the event of the extracted node are automatically assigned to the extracted node based on the similarity of characters.
  • Methods for searching character information for nodes include searching for character strings based on rules, preparing a dictionary of synonyms that summarizes variations in spelling, etc., and searching for similar words while taking into account the effects of variations in spelling. Examples include a method of searching, a method of searching for similar words using a machine learning model, etc.
  • the monitoring item “temperature difference of the slab” is assigned. Note that some events cannot be monitored (observed) by sensors, etc. Therefore, it is not necessary to assign monitoring items to all nodes, and there may be nodes that do not have monitoring items.
  • attribute information may be assigned to nodes in which monitoring items are registered, as necessary.
  • This attribute information includes, for example, abnormality indicators related to the event, data format, type of equipment, timing of the event, and the like.
  • the abnormality index includes, for example, an abnormality degree (abnormality score) indicating the degree of abnormality, a classification determined based on the degree of deviation from a predetermined normal state, and the like.
  • the data format includes, for example, observed values, set values, categories, and the like.
  • the timing of the event includes the time of setting calculation, during operation, and during non-operation. Note that, similarly to monitoring items, it is not necessary to assign attribute information to all nodes, and there may be nodes without attribute information.
  • the data combination unit 34 connects information corresponding to the node (information created by the information creation unit 32) to the node of the knowledge model 21 (the node extracted by the node extraction unit 33).
  • the information linked to the knowledge model 21 is information that includes at least the abnormality index acquired by the above-described standalone monitoring system or abnormality detection system.
  • Information linked to the knowledge model 21 includes abnormality indicators, data formats, types of equipment, timing of events, etc.
  • examples of the abnormality index include the classification of abnormality/normality as shown in Table 1, the degree of abnormality calculated for each monitoring item as shown in Table 2, and the like. Note that Table 1 shows two patterns of classification consisting of "abnormal/normal” as an example, but for example, three patterns of classification consisting of "normal/minor abnormality/major abnormality” may be used.
  • the factor inference unit 35 infers the cause of the phenomenon based on the structure of the knowledge model 21 and the information linked to the nodes of the knowledge model 21.
  • the knowledge model 21 expressed in a graph structure of causal chains and the anomaly indicators shown in Tables 1 and 2 to determine what kind of trouble may occur in the process and how the trouble will be solved. Infer what happens.
  • inference using the abnormality index (abnormal/normal classification) in Table 1 and inference using the abnormality index (degree of abnormality) in Table 2 will be explained separately.
  • the path is further traced in the direction of the result only when all the corresponding events become abnormal. For example, in FIG. 8, both event 5 and event 6 are abnormal, so event 3 is traced. Further, if a monitoring item reaches a normal event while tracing in the direction of the result, the process of tracing further in the direction of the result is interrupted.
  • the abnormality indicators in Table 2 are reflected (combined) in the knowledge model 21, and then, as shown in FIG. 9, the process is traced from the event of the monitoring item to the result of the causal relationship according to the degree of abnormality.
  • the trouble described in the final node reached (trouble A in the figure) is identified as a trouble that may occur in the process.
  • the causal route of the event that was followed earlier is identified, as shown by the bold line in the same figure.
  • the causal route with the highest total abnormality degree is identified as the generation mechanism described in the final node.
  • the total abnormality degree of the causal route from Event 5 ⁇ Event 3 ⁇ Event 1 ⁇ Trouble A is the highest at "24.0", so as shown by the thick line in the figure, Event 5 ⁇ Event Identify the causal route from 3 to event 1 to trouble A.
  • the factor inference unit 35 presents the causes of the phenomenon inferred in the factor inference step to the user through the output unit 40.
  • the causal routes shown in bold lines in FIGS. 8 and 9 are presented.
  • the causal route in addition to the method of highlighting the causal route with a thick line as shown in the figure, the causal route may be highlighted as shown below, for example.
  • nodes and edges included in the causal route may be displayed in a different color from other nodes and edges, or only nodes and edges included in the causal route may be displayed, Other nodes and edges may be hidden.
  • the multiple troubles and multiple troubles may be presented to the user through the output unit 40.
  • the causal routes identified in the factor inference step the causal routes are ranked based on the number of nodes to which abnormal indicators indicating an abnormality are connected, or the number of nodes that have been selected as causal routes in the past. may be presented.
  • a predictive model (machine learning model) has been developed that predicts the occurrence of flaws based on the manufacturing conditions of steel sheets.
  • This technology uses numerical indicators (hereinafter referred to as ⁇ degree of influence'') to indicate which of the data items of the manufacturing conditions used (hereinafter referred to as ⁇ explanatory variables'') influence the results predicted by the prediction model. ) can be output.
  • ⁇ degree of influence'' numerical indicators
  • ⁇ explanatory variables'' influence the results predicted by the prediction model.
  • a knowledge model 21 expressed in a network structure of causal relationships of events that cause defects is created in a form that can be read by a computer. Further, to the nodes of this knowledge model 21, explanatory variables (see explanatory variables 1 to 4 in the figure) of the prediction model related to the event of the node are assigned.
  • information regarding whether each explanatory variable is important or not is linked to the nodes of the knowledge model 21.
  • Information regarding whether each explanatory variable is important can be created, for example, based on whether the degree of influence of the explanatory variable calculated by the prediction model exceeds a predetermined threshold.
  • the factor inference system 2 includes a factor inference server device 3 and a terminal device 4, as shown in FIG.
  • the factor inference server device 3 and the terminal device 4 are configured to be able to communicate through a network N such as the Internet network, for example. Further, in the factor inference system 2, the terminal device 4 is placed, for example, in a steelworks.
  • the factor inference server device 3 is realized by, for example, a server placed on a cloud.
  • This factor inference server device 3 is a network compatible server device of the factor inference device realized by the information processing device 1 shown in FIG. 1, and has the same configuration as the factor inference device. That is, the factor inference server device 3 includes, among the components of the information processing device 1, a calculation section that functions as a knowledge model acquisition section, an information creation section, a node extraction section, a data combination section, and a factor inference section, and an output section. At least.
  • the knowledge model acquisition unit of the factor inference server device 3 acquires a knowledge model expressed in the form of a network connecting nodes, with events that occur in the process as nodes, regarding the causal relationships between phenomena in the process. Furthermore, the information creation unit of the factor inference server device 3 creates information that includes at least an abnormality index regarding the event based on the data collected from the process. Further, the node extraction unit of the factor inference server device 3 searches for information on the nodes of the knowledge model based on the names of data collected from the process, and extracts the corresponding nodes. Further, the data linking unit of the factor inference server device 3 links corresponding information to the extracted nodes.
  • the factor inference unit of the factor inference server device 3 infers the cause of the phenomenon based on the structure of the knowledge model and the information linked to the nodes. Further, the output unit of the factor inference server device 3 outputs information including at least the cause of the phenomenon inferred by the factor inference unit to the terminal device 4 via the network N.
  • the terminal device 4 acquires various information from the factor inference server device 3 through the network N and displays it.
  • This terminal device 4 is realized by, for example, a general-purpose computer such as a personal computer, or a mobile information processing device such as a tablet computer.
  • the terminal device 4 includes a calculation section that functions as an information acquisition section and a display section.
  • an external display such as a liquid crystal display (LCD), an organic EL display (OLED), a touch panel display, etc. is connected to the terminal device 4.
  • LCD liquid crystal display
  • OLED organic EL display
  • touch panel display etc.
  • the information acquisition unit of the terminal device 4 acquires from the factor inference server device 3 information that includes at least the cause of the phenomenon inferred by the factor inference server device 3.
  • the causes of this phenomenon are based on the structure of a knowledge model expressed in the form of a network that connects nodes, with events that occur in the process as nodes, and the information linked to the nodes of the knowledge model. It was inferred that Furthermore, the information linked to the node of the knowledge model is information linked to the node extracted based on the name of the data collected from the process in the knowledge model, and includes at least an abnormality indicator related to the event. be.
  • the causes of the above phenomenon include, for example, the entire causal route that associates the phenomenon in the process with its factors (see Figures 8 and 9), the highlighting of the causal route of the inferred cause of the phenomenon, and the highlighting of multiple causal routes. Ranking results, etc. Then, the display unit of the terminal device 4 causes the above-mentioned external display to display this information acquired by the information acquisition unit.
  • FIG. 12 shows the knowledge model used in this embodiment.
  • the knowledge model shown in FIG. 12 shows how multiple events affect a certain trouble C.
  • Each node of the knowledge model is given an explanation of the event that indicates the causal influence. Note that some nodes (phenomena) cannot be directly observed or data does not exist, so inferring factors is performed using nodes of observable phenomena. In the figure, the shaded nodes indicate nodes of unobservable phenomena, and the other nodes indicate nodes of observable phenomena.
  • Table 3 shows the degree of abnormality calculated by the abnormality diagnosis system for the monitoring items of the observable nodes in FIG.
  • a character search was performed on the monitoring items shown in Table 3, and nodes that were determined to match were extracted (node extraction step). In this node extraction step, if the same word is simply used, the monitoring item is assigned accordingly. On the other hand, if the words are not the same but similar, you can link similar words in advance (for example, "pressure” and “hydraulic”, “operator” and “operation”, etc.), and then link them to the appropriate node. Assign monitoring items to
  • the corresponding anomaly degree was assigned to the extracted nodes (data combination step), and as shown in Figure 13, the knowledge model was used to infer the factors (factor inference step) (causal route). . Note that some nodes (phenomena) may not be directly observable or for which no data exists, so the factors that can be inferred are made using observable nodes.
  • factor inference device factor inference method, factor inference system, and terminal device according to the embodiments described above, even when used by a user (operator, etc.) who does not have deep knowledge of the analysis target, sensor data and setting values It is possible to provide useful auxiliary information (causal route) for problem solving from operational data such as This allows the user to take appropriate and prompt action to solve the problem using this auxiliary information.
  • Information processing device 10 Input unit (input means) 20 Storage unit (storage means) 21 Knowledge model 30 Arithmetic unit (arithmetic means) 31 Knowledge model acquisition unit (knowledge model acquisition means) 32 Information creation department (information creation means) 33 Node extraction unit (node extraction means) 34 Data coupling section (data coupling means) 35 Factor inference section (factor inference means) 40 Output section (output means) 2 Factor inference system 3 Factor inference server device 4 Terminal device N Network

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