Process data editing method, process abnormality detection method, process data editing device, and process abnormality detection device

Abstract

A process data editing method according to the present invention is for a production line composed of a plurality of pieces of equipment that include a plurality of apparatuses that perform prescribed processing on a starting material and a transport device that links the plurality of apparatuses and transports the starting material. The process data editing method includes a collection step in which a collection means of a computer collects process data that indicates the operating state of the plurality of pieces of equipment and an editing step in which an editing means of the computer corrects a time delay in accordance with the type of equipment through which the starting material is passing on the production line and associates the process data.

Description

Process data editing method, process abnormality detection method, process data editing device, and process abnormality detection device

[0001] The present invention relates to a method for editing process data, a method for detecting a process abnormality, an apparatus for editing process data, and an apparatus for detecting a process abnormality.

[0002] The sintering process in the steel industry spans multiple manufacturing steps, such as granulation, firing, and cooling, and sinter is produced over a long period of time. Therefore, when analyzing process data given to raw materials for certain product or operational characteristics, it is necessary to correct for time delays that take into account the differences in the times at which each process data was given.

[0003] In offline data analysis work, a data set required for analysis may be created by adding a fixed time correction (time delay correction) according to the process data of interest. For example, Patent Document 1 proposes a method of measuring the temperature distribution and its changes in a sintering machine using thermometers attached to the raw materials and pallets. In this way, data obtained from a thermometer that moves with the raw materials laid on the pallet does not require consideration of the above-mentioned time delay correction.

[0004] Furthermore, Patent Document 2 proposes a method of acquiring data such as temperature distribution at each position within a sintering machine by using RFID tags to individually identify pallets of the sintering machine.

[0005] JP 2011-84758 A JP 2013-122341 A

[0006] However, in the method proposed in Patent Document 1, the wireless master device does not correspond to each individual pallet, so it is uncertain at which position on the sintering machine the obtained data such as temperature distribution was measured. Therefore, it can be said that the method proposed in Patent Document 1 is not suitable for analysis that focuses on the relationship between the obtained data such as temperature distribution and other process data.

[0007] Furthermore, although the method proposed in Patent Document 2 solves the above-mentioned problems of Patent Document 1, the equipment it targets is limited to sintering machines. Therefore, in the method proposed in Patent Document 2, in order to analyze the quality of raw materials and operational characteristics from the relationship between process data spanning processes, such as the granulation process and the firing process, and the temperature distribution on the sintering machine, it is still necessary to consider the above-mentioned time delay correction.

[0008] The present invention has been made in consideration of the above, and aims to provide a process data editing method, a process anomaly detection method, a process data editing device, and a process anomaly detection device that are capable of correcting time delays and correlating various process data in a production line.

[0009] In order to solve the above-mentioned problems and achieve the object, the method for editing process data according to the present invention includes: a collection step in which a collection means provided in a computer collects process data indicating the operating status of a plurality of pieces of equipment in a production line constituted by a plurality of pieces of equipment each consisting of a plurality of pieces of equipment that perform predetermined processing on raw materials and a conveying device that connects the plurality of pieces of equipment and conveys the raw materials; and an editing step in which an editing means provided in the computer associates the process data by correcting a time delay in accordance with the type of equipment through which the raw materials pass on the production line.

[0010] In addition, in the process data editing method according to the present invention, in the above invention, when the conveying device is included in the equipment through which the raw material passes, the editing step corrects the time delay based on the conveying speed of the raw material by the conveying device and the equipment length of the conveying device.

[0011] Furthermore, in the process data editing method according to the present invention, in the above invention, when the equipment through which the raw materials pass includes a device for depositing the raw materials, the editing step corrects the time delay based on the weight of the deposited raw materials and the insertion speed of the raw materials at the inlet side of the equipment, or based on the weight of the deposited raw materials and the discharge speed of the raw materials at the outlet side of the equipment.

[0012] In order to solve the above-mentioned problems and achieve the object, the process abnormality detection method of the present invention includes: a collection step in which a collection means provided in a computer collects process data indicating the operating status of the plurality of pieces of equipment in a production line composed of a plurality of devices that perform predetermined processing on raw materials and a conveying device that connects the plurality of devices and conveys the raw materials; an editing step in which an editing means provided in the computer associates the process data by correcting a time delay in the process data according to the type of equipment through which the raw materials pass in the production line; and an abnormality detection step in which an abnormality detection means provided in the computer detects an abnormality in the quality or productivity of the raw materials in the production line based on the process data with the time delay corrected.

[0013] Further, in the process abnormality detection method according to the present invention, in the above invention, the production line is a sintering process line in a steelworks.

[0014] In order to solve the above-mentioned problems and achieve the object, the process data editing device of the present invention includes a collection means for collecting process data indicating the operating status of a plurality of pieces of equipment in a production line made up of a plurality of pieces of equipment, the plurality of pieces of equipment comprising a plurality of pieces of equipment that perform predetermined processing on raw materials and a conveying device that connects the plurality of pieces of equipment and conveys the raw materials, and an editing means for correcting a time delay in the process data and associating it with the type of equipment through which the raw materials pass on the production line.

[0015] In addition, in the process data editing device of the present invention, in the above invention, when the conveying device is included in the equipment through which the raw material passes, the editing means corrects the time delay based on the raw material conveying speed by the conveying device and the equipment length of the conveying device.

[0016] In addition, in the process data editing device of the present invention, in the above invention, when the equipment through which the raw materials pass includes a device for depositing the raw materials, the editing means corrects the time delay based on the weight of the deposited raw materials and the insertion speed of the raw materials at the inlet side of the equipment, or based on the weight of the deposited raw materials and the discharge speed of the raw materials at the outlet side of the equipment.

[0017] In order to solve the above-mentioned problems and achieve the object, the process abnormality detection device of the present invention is provided with a collection means for collecting process data indicating the operating status of a production line made up of a plurality of pieces of equipment, the plurality of pieces of equipment comprising a plurality of devices that perform predetermined processing on raw materials and a transport device that connects the plurality of devices and transports the raw materials, an editing means for correcting a time delay in the process data and associating the process data according to the type of equipment through which the raw materials pass on the production line, and an abnormality detection means for detecting an abnormality in the quality or productivity of the raw materials on the production line based on the process data with the time delay corrected.

[0018] According to the process data editing method, process anomaly detection method, process data editing device, and process anomaly detection device of the present invention, it is possible to correct time delays and associate various process data in a manufacturing line.

[0019] FIG. 1 is a diagram showing an example of a steel sintering process line. FIG. 2 is a diagram showing an example of a specific configuration of a surge hopper. FIG. 3 is a diagram showing an example of a specific configuration of a sintering machine. FIG. 4 is a diagram showing the insertion and discharge (cut-out) times of raw materials at each piece of equipment while a certain raw material is transported from an upstream piece of equipment to a downstream piece of equipment in a production line. FIG. 5 is a block diagram showing an example of the configuration of a process data editing device according to an embodiment. FIG. 6 is a flowchart showing an example of the flow of a process data editing method according to an embodiment. FIG. 7 is a block diagram showing an example of the configuration of a process anomaly detection device according to an embodiment. FIG. 8 is an example of a process data editing method according to an embodiment, showing an example of the configuration of a blending tank, a drum mixer, and a belt conveyor connecting them, and a method of calculating the actual moisture content of raw materials in the drum mixer. FIG. 9 is an example of a process data editing method according to an embodiment, where (a) is a diagram showing the relationship between the target moisture content and the actual moisture content when tracking according to the present invention is not performed, and (b) is a diagram showing the relationship between the target moisture content and the actual moisture content when tracking according to the present invention is performed.

[0020] A method for editing process data, a method for detecting an abnormality in a process, an apparatus for editing process data, and an apparatus for detecting an abnormality in a process 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 are easily replaceable by those skilled in the art, or those that are substantially identical.

[0021] (Outline of Sintering Process) First, an outline of a sintering process in the blast furnace steel industry to which the process data editing method according to the embodiment is applied will be described with reference to FIGS. 1 to 4. FIG.

[0022] A steel sintering process line is a production line that connects the raw material granulation process, sintering process, and cooling process, with a time constant of several hours. In the granulation process, as shown in Figure 1, raw materials are processed using equipment such as a blending tank 21 and a drum mixer 22. In the sintering process, raw materials are processed using equipment such as a surge hopper (raw material insertion device) 23, a sintering machine 24, an ignition furnace 25, and a crusher 26. In the cooling process, raw materials are processed using equipment such as a cooler 27 and a sieve 28. The raw materials processed through the sieve 28 are then charged into a blast furnace 29.

[0023] Although not shown in Fig. 1, a conveying device (belt conveyor) that connects the devices and transports the raw materials is provided between the devices. In this embodiment, the term "facility" refers to a combination of the devices that perform predetermined processing on the raw materials and the conveying device that connects the devices and transports the raw materials.

[0024] As shown in Fig. 2, the surge hopper 23 is equipped with a raw material supply port 231, a roll feeder 232, a main / dividing gate 233, and a raw material outlet port 234. This surge hopper 23 plays a role in cutting out a predetermined amount of granulated raw material and spreading it on a pallet 241 of the sintering machine 24 shown in Fig. 3. In addition, since the surge hopper 23 has a mechanism for storing raw material, it has the characteristic that the time delay that occurs changes successively depending on the operating conditions.

[0025] Next, an outline of the transportation of raw materials in the sintering process will be explained. Figure 4 shows the times when raw materials are inserted and discharged (cut out) at each facility while the raw materials are transported from facility A on the upstream side to facility G on the downstream side. In addition to the various devices shown in Figure 1, facilities A to G shown in the figure also include belt conveyors.

[0026] Although it is difficult to identify the actual time when a specific raw material passed through the equipment, in this embodiment, the time when the raw material passed through the equipment is estimated by calculating the time delay correction amount from the operating status of each piece of equipment.

[0027] In this embodiment, for example, at a certain time, data is generated on when and what process data was given to a raw material transported to facility G in the upstream facilities (e.g., facilities A to F), or when and what process data was measured in the upstream facilities. In this process, the time from the insertion to the discharge of the raw material in each facility is calculated, and the data is traced back to the upstream facilities, thereby correcting the time delay in the process data in each facility and associating each process data.

[0028] Here, "process data" in this embodiment refers to various data (various signals) acquired from each piece of equipment. This process data includes, for example, settings (e.g., conveying speed, temperature, etc.) when processing raw materials in each piece of equipment, measured values ​​(e.g., temperature, etc.) by sensors attached to each piece of equipment, and operation variables (e.g., motor current value, etc.) given to each piece of equipment. Furthermore, data on the quality of raw materials at a specific position conveyed to the sintering equipment (observation data such as moisture ratio and particle size) and data on the quality of the produced sintered ore (analysis values ​​such as strength and components) may also be included as process data.

[0029] Furthermore, "associating each process data" means associating the process data related to the raw material inserted into equipment G, indicated by a star in FIG. 4, with the process data related to the raw material inserted into equipment upstream of that equipment (e.g., equipment A to F). Furthermore, the process data are associated with each other after correcting for time delay. In this embodiment, the time from the insertion to the discharge of the raw material in each equipment (i.e., residence time), indicated by X in FIG. 4, is defined as the "time delay correction amount."

[0030] While Fig. 4 shows an example in which the time delay amount for each facility is calculated with facility G on the most downstream side, indicated by a star, as the starting point and facility A on the most upstream side as the end point, it is also possible to perform calculations by adding another facility downstream of facility G, or by adding another facility upstream of facility A. It is also possible to set multiple starting points and end points, indicated by a star in the figure, and perform calculations for each of the starting point and end point.

[0031] Here, the time from the insertion of the raw material into each facility to the discharge thereof (i.e., the time delay correction amount) needs to be calculated taking into consideration the characteristics of the facility through which the raw material passes and the operating status of that facility. For example, in the case of the granulation process, the raw material first dispensed from the blending tank 21 is transported by a belt conveyor, and then inserted into the drum mixer 22 via multiple belt conveyors.

[0032] The raw material converted into pseudo-particles by the drum mixer 22 is then transported again by the belt conveyor, inserted into the surge hopper 23, and passed on to the subsequent firing process. In this granulation process, if the belt conveyor is controlled at a constant speed, the time from when the raw material is inserted into the belt conveyor to when it is discharged can be considered constant. However, the time from when the raw material is inserted into the drum mixer 22 to when it is discharged (i.e., the residence time) varies depending on the rotation speed of the drum, so it is necessary to calculate a time delay correction amount that takes this into account.

[0033] Therefore, in this embodiment, the time delay correction amount is calculated using, for example, patterns 1 to 3 shown in Table 1, depending on the type of facility through which the raw material passes, that is, the raw material transport route.

[0034]

[0035] <Pattern 1> For equipment that transports raw materials using a driving device such as a motor, such as a belt conveyor, sintering machine 24, or cooler 27, the time delay correction amount is calculated using the method of pattern 1 in Table 1. In this method, the time delay correction amount is calculated based on the relationship between the raw material transport speed and the equipment length. In addition, the time when the value obtained by integrating (accumulating) the transport speed measured every moment reaches the equipment length is determined as the time when the raw materials are discharged from the equipment. In addition, the difference between the time when the raw materials are discharged from the equipment and the time when they are inserted into the equipment is the time delay correction amount.

[0036] <Pattern 2> For equipment that accumulates raw materials, such as a surge hopper 23, a hot chute, or a discharge hopper, the time delay correction amount is calculated using the method of pattern 2 in Table 1. In this method, the time delay correction amount is calculated based on the relationship between the weight of the accumulated raw materials and the amount of raw materials inserted per unit time, or the relationship between the weight of the accumulated raw materials and the amount of raw materials discharged per unit time. Furthermore, the time delay correction amount is the difference between the time of discharge from the equipment and the time of insertion into the facility.

[0037] <Pattern 3> For example, for the drum mixer 22, the time delay correction amount is calculated using the method of Pattern 3 in Table 1. In this method, the time delay correction amount is calculated based on the rotation speed of the drum mixer 22 and the shape factor of the drum mixer 22. The shape factor of the drum mixer 22 is a coefficient proportional to the diameter of the drum mixer 22.

[0038] (Process Data Editing Device) Next, an example of the configuration of a process data editing device according to an embodiment will be described with reference to Fig. 5. The figure shows the configuration of an information processing device 1 that realizes the process data editing device according to an embodiment. This information processing device 1 is realized by, for example, a general-purpose computer such as a workstation or a personal computer, or a server located on a cloud. The information processing device 1 also includes an input unit 11, an operation DB 12, a calculation unit 13, and an output unit 14.

[0039] The input unit 11 is an input means for the calculation unit 13, and is realized by an input device such as a keyboard, a mouse pointer, a numeric keypad, etc. The input unit 11 inputs information required for various processes in the calculation unit 13.

[0040] Process data acquired from each facility is stored in the operation DB 12. As described above, this process data includes, for example, the operating conditions (e.g., operation variables such as motor current values) when processing raw materials in each facility, and measurement values ​​(e.g., temperature) of sensors attached to each facility.

[0041] The calculation unit 13 is realized by a processor such as a CPU (Central Processing Unit) and a memory (main storage unit) such as a RAM (Random Access Memory) or a ROM (Read Only Memory).

[0042] The calculation unit 13 loads a program into the working area of ​​the main storage unit, executes it, and controls each component unit through the execution of the program, thereby realizing functions that meet a predetermined purpose. The calculation unit 13 functions as a collection unit 131 and an editing unit 132 through the execution of the program. Note that while Fig. 5 shows an example in which the functions of each unit are realized by, for example, one computer (calculation unit), the means for realizing the functions of each unit is not particularly limited, and the functions of each unit may be realized by, for example, multiple computers.

[0043] The collection unit 131 accesses the operation DB 12 and collects process data indicating the operating status of multiple pieces of equipment.

[0044] The editing unit 132 associates the process data collected by the collection unit 131 with the corrected time delay according to the type of equipment through which the raw materials pass on the production line. That is, the collection unit 131 associates the process data with each other after correcting the time delay. This makes it possible to trace back, for example, the state of a raw material at a certain point in the production process (what processing was performed on it) in an upstream equipment. In this embodiment, the process data associated with the corrected time delay is also referred to as "tracked data."

[0045] For example, when the equipment through which the raw materials pass includes a conveying device (belt conveyor), the editing unit 132 corrects the time delay based on the conveying speed of the raw materials by the conveying device and the equipment length of the conveying device, as shown in Pattern 1 in Table 1. Then, the editing unit 132 associates the process data with each other after correcting the time delay.

[0046] Furthermore, for example, when the equipment through which the raw materials pass includes equipment for depositing raw materials, the editing unit 132 corrects the time delay using the method shown in pattern 2 in Table 1. That is, the editing unit 132 corrects the time delay based on the weight of the raw materials deposited in the equipment and the rate at which the raw materials are inserted at the inlet side of the equipment, or based on the weight of the raw materials deposited in the equipment and the rate at which the raw materials are discharged at the outlet side of the equipment. Then, the editing unit 132 associates each of the process data for which the time delay has been corrected.

[0047] Furthermore, for example, when the equipment through which the raw material passes includes a drum mixer 22, the editing unit 132 calculates the time delay correction amount based on the rotation speed of the drum mixer 22 and the shape factor of the drum mixer 22, as shown in Pattern 3 in Table 1. Then, the editing unit 132 associates each piece of process data with the time delay corrected.

[0048] For example, when focusing on a certain raw material, the editing unit 132 corrects time delays for all process data from the upstream equipment that processed the raw material to the downstream equipment, and associates them. This makes it possible to easily analyze the process data given to the raw material with respect to certain characteristics of a product or operation, for example.

[0049] Furthermore, when evaluating process data at a specific time point in a sintering process line (see FIG. 1), it is also possible to check the correlation with process data of upstream equipment. For example, if there is a problem with the quality of the raw material currently being processed in the sieve 28 (or the crusher 26, drum mixer 22, etc.), it is possible to check the state of the raw material in the upstream equipment and identify the cause.

[0050] The output unit 14 is realized by a display device such as an LCD display, a CRT display, etc. The output unit 14 outputs the tracked data edited by the editing unit 132, etc.

[0051] (Process Data Editing Method) An example of a process data editing method according to the embodiment will be described with reference to Fig. 4 and Fig. 6. Fig. 6 shows an example of details of processing performed by the editing unit 132 after the collection unit 131 collects process data.

[0052] First, a facility from which a search is to be started is set (step S1). This "facility from which a search is to be started" refers to, for example, "facility G" in FIG. 4. Next, an arbitrary time from which the search is to be started is set as the search start time (step S2). The facility from which the search is to be started and the search start time are set as in steps S1 and S2 in order to link, by tracking, the process data given to the material in the upstream facility for the material that passed through the target facility at the target time.

[0053] Next, the equipment immediately upstream of the equipment from which the search is to be started is set as the equipment to be searched for tracking (step S3). This "equipment to be searched" refers to, for example, "equipment F" in FIG. 4. Here, for adjacent equipment, the discharge time of the upstream equipment and the insertion time of the downstream equipment can be considered to be the same. Therefore, the discharge time of the equipment to be searched, which is located immediately upstream of the equipment from which the search is to be started, is set as the search start time (step S4).

[0054] Next, the time delay correction amount for the equipment to be searched is calculated (step S5) using one of patterns 1 to 3 in Table 1. Next, the search start time is moved back by the time delay correction amount (step S6), and the moved back time is recorded as the insertion time of the equipment to be searched (step S7).

[0055] Next, it is determined whether the equipment to be searched is the final equipment in the search (step S8). If the equipment to be searched is not the final equipment in the search (No in step S8), the process returns to step S3, and the processes in steps S3 to S7 are repeated until the final equipment in the search is reached.

[0056] On the other hand, in step S8, if the equipment to be searched is the final equipment in the search (Yes in step S8), the following process is performed: In this case, for example, the measurement data (=process data) of the sensors provided on the insertion side or discharge side of each piece of equipment is associated with the data of the time recorded in the tracking process described above (step S9), and created as tracked data.

[0057] (Process Anomaly Detection Device) An example of the configuration of a process anomaly detection device according to an embodiment will be described with reference to FIG. 7 . This figure shows the configuration of an information processing device 1A that realizes the process anomaly detection device according to an embodiment. This information processing device 1A is realized by, for example, a general-purpose computer such as a workstation or a personal computer, or a server located on the cloud. The information processing device 1A also includes an input unit 11, an operation DB 12, a calculation unit 13A, and an output unit 14. Of the configuration of the information processing device 1A, the input unit 11, the operation DB 12, and the output unit 14 are the same as those of the information processing device 1 described above, and therefore description thereof will be omitted.

[0058] The calculation unit 13A is realized by a processor such as a CPU and a memory (main storage unit) such as a RAM or ROM. The calculation unit 13A loads a program into the working area of ​​the main storage unit, executes it, and controls each component through the execution of the program to realize functions that meet a predetermined purpose. The calculation unit 13A functions as a collection unit 131, an editing unit 132, and an anomaly detection unit 133 through the execution of the program. Note that while FIG. 7 illustrates an example in which the functions of each unit are realized by, for example, a single computer (calculation unit), the means for realizing the functions of each unit is not particularly limited, and the functions of each unit may be realized by, for example, multiple computers. Furthermore, among the components of the calculation unit 13A, the collection unit 131 and the editing unit 132 are similar to those of the calculation unit 13 described above, and therefore, description thereof will be omitted.

[0059] The abnormality detection unit 133 performs abnormality diagnosis to detect abnormalities in the quality or productivity of raw materials on the production line based on the process data for which the time delay has been corrected in the editing unit 132 .

[0060] An example of an abnormality diagnosis is a method in which a prediction model that predicts specific process data based on other process data is constructed in advance, and a prediction error, which is the difference between a predicted value of the process data based on the prediction model and its actual value, is evaluated. Another abnormality diagnosis method is to detect abnormalities by evaluating the degree of deviation of each process data from normal times. Furthermore, it is also possible to perform abnormality diagnosis based on statistical abnormality diagnosis techniques such as principal component analysis using multiple process data.

[0061] The abnormality detection unit 133 determines, for example, abnormality monitoring items and abnormality diagnosis methods as described above, and detects the presence or absence of an abnormality for each abnormality monitoring item. Furthermore, if there is a problem with the quality of the raw material currently being processed in the sieve 28 (or the crusher 26, drum mixer 22, etc.), the abnormality detection unit 133 can also check the state of the raw material in the equipment upstream of that and identify the cause.

[0062] (Process Abnormality Detection Method) A process abnormality diagnosis method according to the embodiment will be described. In the process abnormality detection method according to the embodiment, an abnormality in the quality or productivity of raw materials in a production line is detected based on process data in which the time delay has been corrected by the above-described process data editing method. An example of the production line is a sintering process line in a steelworks.

[0063] Furthermore, in the process anomaly detection method according to the embodiment, the data representing the physical and spatial extent from upstream to downstream equipment is associated with data representing the state of each piece of equipment or the state of raw materials based on the amount of time delay, using the process data compilation method described above.The process anomaly detection method according to the embodiment can also use such a data set to build a prediction model required for anomaly detection.

[0064] For example, the current value of the raw material conveyor belt before the sintering plant depends on the amount of raw material on the belt conveyor. Therefore, the current value can be predicted from the weight of each raw material by synchronizing the timing of the discharge of multiple raw materials and taking into account the transport time to each motor position. Based on the prediction error between the predicted current value obtained by this prediction model and the actual current value, abnormalities in the belt conveyor motor or the belt conveyor itself can be detected. Furthermore, operational abnormalities, such as those detected using a prediction model for the moisture content of raw materials in the drum mixer 22 (described later), are also detected based on data compiled with time delay correction. Furthermore, it is possible to predict the quality of the produced sinter, including its strength, and detect quality abnormalities based on the prediction error.

[0065] The data set obtained by this embodiment can achieve its effect by focusing on the relationship (correlation, etc.) between multiple variables. Possible methods for detecting anomalies include a method of detecting anomalies by evaluating the degree of deviation between a predicted quantity and an actual value or the degree of deviation between a predicted quantity and a target value based on a prediction model that predicts equipment status, etc., constructed using data from normal times, and anomaly detection based on principal component analysis, etc. The prediction model used for anomaly detection is not limited to a method based on a statistical method such as machine learning, but may also be constructed using a method based on a scientific approach, such as a physical model.

[0066] According to the process anomaly detection method of the embodiment, anomaly detection is performed taking into account the effect of the time delay from the upstream equipment to the downstream equipment, so that the variation in data that depends on the time delay can be suppressed, and accurate anomaly detection becomes possible.

[0067] (Identifying the cause of anomalies) When an anomaly is detected based on a prediction error, it is possible to identify the process data that are explanatory variables that contribute to the prediction error and clearly indicate them as candidates for the cause of the anomaly. It is also possible to evaluate the degree of deviation from the normal state of each explanatory variable and extract candidate anomaly signals. This allows the cause of operational anomalies to be quickly identified, minimizes the impact of abnormal operation on product quality, and shortens the time it takes to restore normal conditions, contributing to maintaining production speed and product quality.

[0068] An example of a method for editing process data according to the present invention will be described with reference to Figures 8 and 9. Figure 8 shows an example of the configuration of blending tanks #1 to #20, drum mixers, and belt conveyors 1 to 3 connecting them, as well as a method for calculating the moisture content (effective moisture content) of raw materials in the drum mixer.

[0069] The raw materials used in the sintering process are stored in multiple blending tanks, and are extracted from multiple blending tanks and granulated for use, taking into consideration the raw material blending, production schedule, etc. The amount of raw material extracted from each blending tank (extracted amount) is measured for each blending tank using a weighing machine attached to each blending tank. In addition, the moisture content in each blending tank is measured for each blending tank using a moisture meter attached to each blending tank.

[0070] The raw materials discharged from the blending tank are sprayed with water and agitated in a drum mixer to be granulated. To optimize this granulation, it is necessary to maintain an appropriate moisture content, and it is necessary to manage the target and actual moisture content values.

[0071] The actual moisture content of the raw material is calculated using the formula shown in the lower part of Figure 8. Meanwhile, the weighers and moisture content meters installed in each blending tank are located physically away from the drum mixer. Therefore, by utilizing the present invention, process data showing the operating status of each device that makes up the entire process is continuously collected, and the process data is correlated with the raw material transport route after correcting the time delay, thereby creating tracked data.

[0072] For example, assume that the delay time from the downstream equipment, the drum mixer, to the upstream equipment, the blending tank #1, is 30 minutes. In this case, the actual moisture content of the raw material currently in the drum mixer is linked to the amount of raw material discharged and the moisture content in blending tank #1 30 minutes prior. Similarly, the discharge amounts and moisture contents of the other blending tanks #2 to #20 are also linked to the actual moisture content of the drum mixer, thereby creating tracked data. Furthermore, if the conveying speeds of belt conveyors 1 to 3 are different, this is also taken into account to correct the time delay and create tracked data.

[0073] This makes it possible to calculate the actual moisture content of the raw material in the drum mixer with high accuracy, as shown in Fig. 9. Fig. 9(a) shows the relationship between the target moisture content and the actual moisture content when the tracking according to the present invention is not performed, and Fig. 9(b) shows the relationship between the target moisture content and the actual moisture content when the tracking according to the present invention is performed.

[0074] Furthermore, in the present invention, by accurately calculating the real moisture content as described above, it is also possible to perform anomaly detection to prevent deviation of the real moisture content from the target moisture content. In this case, by utilizing the present invention, it is expected that anomaly detection will be improved, leading to stable production and quality of raw materials.

[0075] Furthermore, for example, it is possible to statistically manage the ratio of the estimated moisture content after granulation, calculated from the moisture content in the drum mixer and the amount of water sprayed on the drum mixer, to the target moisture content for the granulation process, or the difference from the target moisture content for the granulation process. It is also possible to determine in advance the spread of the ratio to the target moisture content for the granulation process or the difference from the target moisture content for the granulation process during normal operation, and detect an abnormality when a significant deviation is confirmed compared with that spread. Furthermore, if a threshold value can be set for the actual moisture content from a physical perspective, it is also possible to detect an abnormality based on that value.

[0076] The process data editing method, process anomaly detection method, process data editing device, and process anomaly detection device according to the above-described embodiments enable various process data in a production line to be correlated with time delays. As a result, the time delay-corrected and appropriately correlated process data can be used for data analysis, quality control, equipment maintenance, etc., and can be used for overall process monitoring, anomaly detection, optimization, etc.

[0077] Although the present invention has been described above as an embodiment, the present invention is not limited to the description and drawings that form part of the disclosure of the present invention. In other words, other embodiments, examples, and operational techniques that can be made by those skilled in the art based on the present invention are all included in the scope of the present invention.

[0078] DESCRIPTION OF SYMBOLS 1, 1A Information processing device 11 Input unit 12 Operation DB 13, 13A Calculation unit 131 Collection unit (collection means) 132 Editing unit (editing means) 133 Abnormality detection unit (abnormality detection means) 14 Output unit 21 Blending tank 22 Drum mixer 23 Surge hopper (raw material insertion device) 231 Raw material supply port 232 Roll feeder 233 Main / dividing gate 234 Raw material outlet 24 Sintering machine 241 Pallet 25 Ignition furnace 26 Crusher 27 Cooler (cooler) 28 Sieve 29 Blast furnace

Claims

1. A method for editing process data in a production line composed of a plurality of pieces of equipment, the plurality of pieces of equipment being made up of a plurality of pieces of equipment that perform predetermined processing on raw materials and a transport device that connects the plurality of pieces of equipment and transports the raw materials, the method comprising: a collection step in which a collection means provided in a computer collects process data showing the operating status of the plurality of pieces of equipment; and an editing step in which an editing means provided in the computer associates the process data by correcting a time delay in accordance with the type of equipment through which the raw materials pass on the production line.

2. The process data editing method of claim 1, wherein the editing step corrects the time delay based on the transport speed of the raw material by the transport device and the equipment length of the transport device when the transport device is included in the equipment through which the raw material passes.

3. The method for editing process data described in claim 1, wherein the editing step corrects the time delay based on the weight of the accumulated raw materials and the insertion speed of the raw materials at the inlet side of the equipment, or based on the weight of the accumulated raw materials and the discharge speed of the raw materials at the outlet side of the equipment, when the equipment through which the raw materials pass includes a device for depositing the raw materials.

4. A method for detecting an anomaly in a process in a production line composed of a plurality of pieces of equipment, the plurality of pieces of equipment comprising a plurality of devices that perform predetermined processing on raw materials and a transport device that connects the plurality of devices and transports the raw materials, comprising: a collection step in which a collection means included in a computer collects process data indicating the operating status of the plurality of pieces of equipment; an editing step in which an editing means included in the computer corrects a time delay in the process data according to the type of equipment through which the raw materials pass in the production line, and associates the process data; and an anomaly detection step in which an anomaly detection means included in the computer detects an anomaly in the quality or productivity of the raw materials in the production line based on the process data with the time delay corrected.

5. The process abnormality detection method according to claim 4, wherein the manufacturing line is a sintering process line in a steelworks.

6. A process data editing device in a production line composed of a plurality of pieces of equipment, the plurality of pieces of equipment comprising a plurality of devices that perform a predetermined process on raw materials and a transport device that connects the plurality of devices and transports the raw materials, the device including: a collection means that collects process data indicating the operating status of the plurality of pieces of equipment; and an editing means that correlates the process data by correcting a time delay according to the type of equipment through which the raw materials pass on the production line.

7. The process data editing device of claim 6, wherein the editing means corrects the time delay based on the transport speed of the raw material by the transport device and the equipment length of the transport device when the transport device is included in the equipment through which the raw material passes.

8. The process data editing device of claim 6, wherein the editing means corrects the time delay based on the weight of the accumulated raw materials and the insertion speed of the raw materials at the inlet side of the equipment, or based on the weight of the accumulated raw materials and the discharge speed of the raw materials at the outlet side of the equipment, when the equipment through which the raw materials pass includes a device for stacking the raw materials.

9. A process anomaly detection device in a production line composed of a plurality of pieces of equipment, the plurality of pieces of equipment comprising a plurality of devices that perform predetermined processing on raw materials and a transport device that connects the plurality of devices and transports the raw materials, comprising: a collection means for collecting process data indicating the operating status of the plurality of pieces of equipment; an editing means for correlating the process data by correcting a time delay in accordance with the type of equipment through which the raw materials pass on the production line; and anomaly detection means for detecting anomalies in the quality or productivity of the raw materials on the production line based on the process data with the time delay corrected.