Monitoring system and method of operation thereof

Abstract

A monitoring system according to one embodiment disclosed in this document may include a data management unit that acquires time-dependent driving data of a motor associated with a battery manufacturing apparatus, applies the driving data to an artificial intelligence model to extract parameters that indicate characteristics of the driving data, and a controller that generates reference information for managing the state of the motor based on the parameters.

Description

[Technical Field]

[0001] The embodiments disclosed in this document claim the benefit of priority based on Korean Patent Application No. 10-2022-0134711, filed October 19, 2022, and all contents disclosed in the documents of this Korean patent application are incorporated herein by reference.

[0002] SUMMARY OF THE INVENTION The embodiments disclosed herein relate to monitoring systems and methods of operation. [Background technology]

[0003] An electric vehicle receives electricity from an external source to charge its battery cells, and then drives a motor with the voltage charged in the battery cells to generate power. Battery cells for electric vehicles are manufactured by placing an electrode assembly in a battery case and injecting an electrolyte into the battery case.

[0004] If a battery cell is partially or entirely separated from a weld due to an external impact or poor welding, the battery cell may deteriorate or explode. Therefore, the battery process system monitors the load rate of the servo motor included in the battery manufacturing equipment in order to quickly diagnose and analyze a malfunction in the battery manufacturing process.

[0005] Generally, battery process systems monitor the load rate of servo motors based on a fixed control line, which means that if the battery process conditions change, the fixed control line cannot be updated, resulting in overdetection or a large workload for managers due to the need to update the control line.On the other hand, a method of monitoring the load rate of servo motors based on a moving average control line reduces the workload for managers as the control line is automatically updated in response to changes in the battery process conditions, but it has the problem of not being able to detect gradual increases in the load rate of servo motors, resulting in underdetection. Summary of the Invention [Problem to be solved by the invention]

[0006] One objective of the embodiments disclosed herein is to provide a monitoring system and an operating method thereof that can analyze and control the load factor of a servo motor in real time using a moving average control line of a battery processing system.

[0007] The technical problems of the embodiments disclosed in this document are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the following description. [Means for solving the problem]

[0008] A monitoring system according to one embodiment disclosed in this document may include a data management unit that acquires time-dependent driving data of a motor associated with a battery manufacturing apparatus, applies the driving data to an artificial intelligence model to extract parameters that indicate characteristics of the driving data, and a controller that generates reference information for managing the state of the motor based on the parameters.

[0009] According to an embodiment, the controller can pre-process the driving data based on the parameters, and generate the reference information based on the pre-processed driving data.

[0010] According to an embodiment, the data management unit applies the driving data to an optimized artificial intelligence model and extracts a first section from the driving data for classifying first driving data that is acquired during a predetermined time before a reference point and is used to generate the reference information, and a second section for classifying second driving data that is excluded from generating the reference information.

[0011] According to an embodiment, the controller can generate third driving data by excluding the second driving data acquired during the second period from the first driving data acquired during the first period.

[0012] According to an embodiment, the controller determines whether the period corresponding to the third driving data is equal to or greater than a threshold period, and if the third driving data is equal to or greater than the threshold period, generates the reference information based on the third driving data.

[0013] According to the embodiment, the controller can calculate an average value and a standard deviation of the third driving data, and generate the reference information based on the average value and the standard deviation.

[0014] According to an embodiment, the controller generates a count value based on whether the third driving data exceeds the reference information, and can determine whether the motor is faulty based on whether the count value exceeds a threshold count value.

[0015] According to an embodiment, the controller may determine that the motor is faulty and generate an abnormality signal for the motor if the count value exceeds the threshold count value. According to the embodiment, the controller can determine that the motor is normal when the count value is equal to or less than the threshold count value.

[0016] An operating method of the monitoring system according to one embodiment disclosed in this document may include the steps of acquiring time-dependent driving data of a motor associated with a battery manufacturing apparatus, applying the driving data to an artificial intelligence model to extract parameters indicative of characteristics of the driving data, and generating reference information for managing the state of the motor based on the parameters.

[0017] According to an embodiment, the step of generating reference information for managing the state of the motor based on the parameters may include a step of preprocessing the driving data based on the parameters, and generating the reference information based on the preprocessed driving data.

[0018] According to an embodiment, the step of applying the driving data to an artificial intelligence model to extract parameters indicating the characteristics of the driving data may include the step of applying the driving data to an optimized artificial intelligence model and extracting a first section for classifying first driving data from the driving data that is acquired during a predetermined time before a reference point and is used to generate the reference information, and a second section for classifying second driving data that is excluded from generating the reference information.

[0019] According to an embodiment, the step of generating reference information for managing the state of the motor based on the parameters may include a step of generating third driving data by excluding the second driving data acquired during the second interval from the first driving data acquired during the first interval.

[0020] According to an embodiment, the step of generating reference information for managing the state of the motor based on the parameters may include a step of determining whether a period corresponding to the third drive data is equal to or greater than a threshold period, and if the third drive data is equal to or greater than the threshold period, generating the reference information based on the third drive data.

[0021] According to an embodiment, the step of generating reference information for managing the state of the motor based on the parameters may include a step of calculating an average value and a standard deviation of the third driving data, and generating the reference information based on the average value and the standard deviation.

[0022] According to an embodiment, the step of generating reference information for managing the state of the motor based on the parameters may include the steps of generating a count value based on whether the third driving data exceeds the reference information, and determining whether the motor has failed based on whether the count value exceeds a threshold count value.

[0023] According to an embodiment, the step of generating reference information for managing the state of the motor based on the parameters may include a step of determining that the motor is faulty and generating an abnormality signal for the motor if the count value exceeds the threshold count value.

[0024] According to an embodiment, the step of generating reference information for managing the state of the motor based on the parameters may include a step of determining that the motor is normal if the count value is equal to or less than the threshold count value. [Effects of the Invention]

[0025] According to an embodiment of the monitoring system and its operation method disclosed herein, the load factor of a servo motor can be analyzed and controlled in real time using a moving average control line. [Brief explanation of the drawings]

[0026] [Figure 1] 1 is a diagram illustrating an overall battery processing system according to one embodiment disclosed herein; [Figure 2] 1 is a block diagram illustrating a configuration of a monitoring system according to an embodiment disclosed herein. [Figure 3] FIG. 10 illustrates an operation of extracting parameters of a data manager according to an embodiment disclosed herein. [Figure 4] FIG. 10 is a diagram illustrating an operation of extracting third drive data by a controller according to an embodiment disclosed herein. [Figure 5] FIG. 10 illustrates an operation of generating reference information for a controller according to an embodiment disclosed herein. [Figure 6] 1 is a flowchart illustrating a method of operation of a monitoring system according to one embodiment disclosed herein. [Figure 7] FIG. 1 is a block diagram illustrating a hardware configuration of a computing system that implements an embodiment of a monitoring system disclosed herein. DETAILED DESCRIPTION OF THE INVENTION

[0027] Some embodiments disclosed herein will be described in detail below with reference to exemplary drawings. When assigning reference numerals to components in each drawing, it should be noted that the same reference numerals are assigned to the same components as long as possible when they appear in other drawings. Furthermore, when describing the embodiments disclosed herein, if a detailed description of related known structures or functions is deemed to hinder understanding of the embodiments disclosed herein, such detailed description will be omitted.

[0028] In describing components of the embodiments disclosed herein, terms such as first, second, A, B, (a), and (b) may be used. Such terms are merely used to distinguish a component from other components and do not limit the nature, order, or procedure of the components. Furthermore, unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiments disclosed herein belong. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with the context of the relevant art and should not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0029] FIG. 1 is a diagram illustrating an overall battery processing system according to one embodiment disclosed herein. According to various embodiments, the battery may include a battery cell, which is a basic unit of a battery that can charge and discharge electrical energy for use. The battery cell may be, but is not limited to, a lithium-ion (Li-ion) battery, a lithium-ion polymer (Li-ion) battery, a nickel-cadmium (Ni-Cd) battery, a nickel-metal hydride (Ni-MH) battery, or the like. The battery cell may supply power to a target device (not shown). To this end, the battery cell may be electrically connected to the target device. Here, the target device may include an electrical, electronic, or mechanical device that operates by receiving power from a battery pack (not shown) including a plurality of battery cells. For example, the target device may be, but is not limited to, small products such as digital cameras, DVD players, MP3 players, mobile phones, PDAs, portable game devices, power tools, and e-bikes, as well as large products requiring high power output such as electric vehicles and hybrid vehicles, and power storage devices or backup power storage devices that store surplus generated power or renewable energy.

[0030] A battery cell may include an electrode assembly, a battery case in which the electrode assembly is housed, and an electrolyte that is injected into the battery case to activate the electrode assembly. The electrode assembly is formed by interposing a separator between a positive electrode plate formed by coating a positive electrode current collector with a positive electrode active material and a negative electrode plate formed by coating a negative electrode current collector with a negative electrode active material. Depending on the type of battery case, the electrode assembly may be manufactured in a jelly roll type, stack type, or the like and housed inside the battery case. The battery case serves as an exterior material that maintains the shape of the battery and protects it from external impacts. Battery cells may be classified into cylindrical, prismatic, and pouch types depending on the type of battery case.

[0031] According to an embodiment, a battery cell may be manufactured through a series of manufacturing processes including an electrode manufacturing process, an assembly process, a chemical formation process, etc. Here, the assembly process may include a process of assembling a positive electrode plate and a negative electrode plate prepared through the electrode manufacturing process and injecting an electrolyte, and may also include a notching process, a winding process, an assembly process, and a packaging process.

[0032] The packaging process can be defined as the process of injecting an electrode assembly and electrolyte into a battery case and sealing it. In the case of a cylindrical battery cell, the electrode assembly is mounted in a cylindrical metal can, the negative electrode tab extending from the negative electrode of the electrode assembly is welded to the bottom of the can, and the positive electrode tab extending from the positive electrode of the electrode assembly with the electrode assembly and electrolyte inside is welded to the top cap of the cap assembly.

[0033] Hereinafter, a case where the battery process system is applied to an assembly process will be described as an example. For example, the battery process system may be used in a packaging process in an assembly process system, but is not limited thereto.

[0034] Referring to FIG. 1, a battery processing system can include a monitoring system 100 and at least one battery manufacturing apparatus 200 . The monitoring system 100 can collect and analyze data of the battery manufacturing equipment 200 operated in the battery process system in real time. The monitoring system 100 can collect and analyze operation data of at least one battery manufacturing equipment 200. The monitoring system 100 can also collect operation data of a process control system (PLC) (not shown) that controls the battery manufacturing equipment 200. Here, the operation data of the battery manufacturing equipment 200 can include an operation record of the battery manufacturing equipment 200.

[0035] For example, the monitoring system 100 can collect and analyze data or graph data generated in the battery manufacturing process system, such as the progress of the process, whether an alarm has occurred, temperature, pressure, and quantity, from the battery manufacturing equipment 200.

[0036] The monitoring system 100 can detect abnormal data among the operation data of the battery manufacturing apparatus 200. The monitoring system 100 can analyze the battery manufacturing apparatus 200 corresponding to the abnormal data.

[0037] The battery manufacturing apparatus 200 may include a first battery manufacturing apparatus, a second battery manufacturing apparatus 220, and a third battery manufacturing apparatus 230. Although Fig. 1 shows three battery manufacturing apparatuses 200, the present invention is not limited to this, and the battery manufacturing apparatus 200 may include n apparatuses (n is a natural number equal to or greater than 1).

[0038] According to the embodiment, the battery manufacturing apparatus 200 can weld a positive electrode tab of an electrode assembly of a battery cell to a top cap of a cap assembly during the packaging process of the battery cell.

[0039] According to an embodiment, the monitoring system 100 can acquire driving data of a motor associated with each of the plurality of battery manufacturing apparatuses 200. Here, the motor may include, for example, a servo motor. A servo motor is a motor that includes a control drive board and is designed to quickly and accurately follow a user's position and speed control commands.

[0040] The monitoring system 100 can manage, for example, the load factor of a servo motor. The monitoring system 100 can monitor the gradual increase in the load factor of the motor associated with each of the plurality of battery manufacturing apparatuses 200 using a moving average calculation method. When the operating conditions of the plurality of battery manufacturing apparatuses 200 are changed, the monitoring system 100 can automatically calculate the load factor of the battery manufacturing apparatus 200 using the moving average calculation method.

[0041] FIG. 2 is a block diagram showing the configuration of a monitoring system 100 according to an embodiment disclosed herein. Referring to FIG. 2, the monitoring system 100 can include a data manager 110 and a controller 120 .

[0042] The data management unit 110 can acquire driving data according to time of a motor associated with the battery manufacturing apparatus 200. For example, the data management unit 110 can automatically log driving data of a motor associated with the battery manufacturing apparatus 200. For example, the data management unit 110 can set the sampling period for automatic data logging to 0.1 seconds and collect driving data of the motor.

[0043] The data management unit 110 may apply the driving data to an artificial intelligence model to extract parameters that indicate characteristics of the driving data. Here, the parameters may include, for example, a first interval and a second interval. First, the first interval is a value that can distinguish data used to manage the load rate of the motor from the driving data. That is, the first interval is a reference interval that can extract a portion of the driving data and determine the data used to manage the load rate of the motor. For example, if the first interval is one week, the monitoring system 100 may manage the load rate of the motor using driving data acquired in the most recent week from the driving data.

[0044] The second interval is a value that can distinguish data to be excluded from the driving data for managing the motor load rate. That is, the second interval is a reference interval that can extract a portion of the driving data and determine data to be excluded from managing the motor load rate. For example, if the second interval is 12 hours, the monitoring system 100 can exclude the motor load rate from management using driving data acquired during the most recent 12 hours.

[0045] The data management unit 110 may extract parameters indicating characteristics of the driving data by applying the driving data to an optimizer artificial intelligence model. Here, the optimizer artificial intelligence model may extract parameters indicating characteristics of the driving data using a gradient descent method.

[0046] For example, the data management unit 110 can apply the driving data to an optimization artificial intelligence model to extract a first section and a second section that are optimized for managing the load factor of the motor.

[0047] The controller 120 may pre-process the driving data based on the extracted parameters. Specifically, the controller 120 may extract first driving data, which is driving data acquired during a first interval before the current time point, from all the acquired driving data. The controller 120 may also extract second driving data, which is driving data acquired during a second interval before the current time point, from all the acquired driving data. The controller 120 may generate third driving data by excluding the second driving data from the extracted first driving data.

[0048] The controller 120 can generate reference information based on the pre-processed third driving data, where the reference information can include a reference line from which the load factor of the motor can be determined.

[0049] The controller 120 can determine whether a period corresponding to the third driving data is equal to or greater than a threshold period. If the third driving data is equal to or greater than the threshold period, the controller 120 can generate reference information based on the third driving data. For example, if the third driving data generated by preprocessing the driving data is equal to or greater than data acquired over 3.5 days, the controller 120 can generate reference information based on the third driving data.

[0050] The controller 120 can calculate the mean (μ) and standard deviation (σ) of the third driving data. The controller 120 can generate multiple pieces of reference information based on the mean (μ) and standard deviation (σ) of the third driving data. For example, the controller 120 can generate a "μ+5*σ" value as the first reference information by adding a value obtained by multiplying the mean (μ) of the third driving data by five times the standard deviation (σ). Furthermore, for example, the controller 120 can generate a "μ+9*σ" value as the second reference information by adding a value obtained by multiplying the mean (μ) of the third driving data by nine times the standard deviation (σ).

[0051] The controller 120 may generate a count value based on whether the third driving data exceeds the reference information. Specifically, the controller 120 may determine whether the motor is faulty based on whether the count value generated by determining whether the third driving data per unit time exceeds the reference information exceeds a threshold count value. If the generated count value exceeds the threshold count value, the controller 120 may determine that the motor is faulty and generate a motor abnormality signal. Furthermore, the controller 120 may determine that the motor is normal if the count value is equal to or less than the threshold count value.

[0052] FIG. 3 is a diagram illustrating an operation of extracting parameters of a data management unit according to an embodiment disclosed herein. Referring to FIG. 3, in step S101, the data management unit 110 can acquire motor drive data.

[0053] In step S102, the data management unit 110 can classify the motor driving data into test data and training data. In step S103, the data management unit 110 can input training data from the motor drive data into the optimization function. In step S103, the data management unit 110 can set an initial value (Initiator), a threshold (Threshold), and a learning count for the optimization function. In step S103, for example, the data management unit 110 can input an arbitrary parameter value extracted based on the drive data into the optimization function. Here, the arbitrary parameter value can include a first interval, a second interval, or a multiple value of the standard deviation (σ) used in the reference information. For example, the arbitrary parameter value can include 7 days, 14 days, and 21 days as the first interval, 6 hours, 12 hours, and 1 day as the second interval, and can include 3 times, 4 times, 5 times, etc. as the multiple value of the standard deviation (σ) used in the reference information.

[0054] In step S104, the data management unit 110 can calculate the accuracy of the optimization function. In step S104, the data management unit 110 can use the accuracy of the optimization function to determine whether the "Accuracy(i)-Accuracy(i-1)" value is equal to or less than a first threshold (Threshold 1), where "i" refers to the number of times the optimization function has been learned.

[0055] In step S105, the data management unit 110 can use the accuracy of the optimization function to determine whether the "Accuracy(i)" value is equal to or greater than a second threshold (Threshold 2), where "i" refers to the number of times the optimization function has been learned. In step S105, if the "Accuracy(i)" value is equal to or greater than the second threshold (Threshold 2), the data management unit 110 can terminate the operation of the optimization function.

[0056] In step S106, if the "Accuracy(i)-Accuracy(i-1)" value exceeds the first threshold (Threshold 1) or if the "Accuracy(i)" value is less than the second threshold (Threshold 2), the data management unit 110 can determine whether the number of learning times (i) is 1000 or more. In step S106, if the number of learning times (i) is 1000 or more, the data management unit 110 can terminate the operation of the optimization function. In step S107, the data management unit 110 can calculate the final accuracy of the optimization function using the test data.

[0057] FIG. 4 is a diagram illustrating an operation of extracting the third drive data of the controller according to an embodiment disclosed herein. 4, the controller 120 may extract first driving data, which is driving data acquired during a first period before the reference information is generated, from among all driving data. For example, if the first period extracted by the data management unit 110 is 7 days, 14 days, or 21 days, the controller 120 may extract first driving data, which is driving data acquired during 7 days, 14 days, or 21 days before the reference information is generated, from among all driving data.

[0058] In addition, the controller 120 can extract second driving data, which is driving data acquired during a second period before the reference information is generated, from the entire driving data. For example, if the second period extracted by the data management unit 110 is 12 hours, 1 day, 2 days, or 3 days, the controller 120 can extract second driving data, which is driving data acquired during 12 hours, 1 day, 2 days, or 3 days before the reference information is generated, from the entire driving data.

[0059] The controller 120 may generate third driving data by excluding the second driving data from the extracted first driving data. For example, if the first driving data is driving data acquired within 7, 14, or 21 days before the reference information is generated, and the second driving data is driving data acquired within 12 hours, 1 day, 2 days, or 3 days before the reference information is generated, the controller 120 may generate third driving data by excluding the driving data acquired within 12 hours, 1 day, 2 days, or 3 days before the reference information corresponding to the second driving data from the driving data acquired within 7, 14, or 21 days before the reference information corresponding to the first driving data is generated. The controller 120 can generate reference information based on the pre-processed third driving data.

[0060] FIG. 5 is a diagram illustrating an operation of generating reference information of a controller according to an embodiment disclosed herein. 5, in step S201, the controller 120 may pre-process the driving data based on the extracted parameters. In step S201, the controller 120 may generate third driving data by excluding the second driving data from the extracted first driving data.

[0061] In step S202, the controller 120 can determine whether the period corresponding to the third driving data is equal to or greater than the threshold period. In step S203, the controller 120 can determine whether the third driving data is equal to or greater than a threshold period. In step S202, for example, the controller 120 can determine whether the third driving data is equal to or greater than data acquired over 3.5 days.

[0062] In step S203, the controller 120 can generate reference information based on the third driving data. In step S203, the controller 120 can calculate the mean (μ) and standard deviation (σ) of the third driving data. In step S203, the controller 120 can generate a "μ+5*σ" value as the first reference information by adding a value obtained by multiplying the mean (μ) of the third driving data by five times the standard deviation (σ). In step S203, for example, the controller 120 can generate a "μ+9*σ" value as the second reference information by adding a value obtained by multiplying the mean (μ) of the third driving data by nine times the standard deviation (σ).

[0063] In step S204, the controller 120 does not generate reference information if the third driving data is less than the threshold period. In step S205, after generating the reference information, the controller 120 can determine whether or not drive data equal to or greater than the reference information has been acquired. For example, in step S205, the controller 120 can determine whether or not drive data equal to or greater than the reference information has been acquired from drive data acquired in the last 10 minutes.

[0064] In step S206, the controller 120 can generate a count value based on whether the third driving data exceeds the reference information. For example, in step S206, the controller 120 can determine whether the count value of the driving data that is equal to or greater than the first reference information among the third driving data is 10 or greater.

[0065] In step S207, if the controller 120 does not acquire driving data equal to or greater than the reference information after generating the reference information, the controller 120 can determine that the motor is normal.

[0066] In step S208, the controller 120 can determine whether the count value of the driving data that is equal to or greater than the second reference information among the third driving data is 5 or greater.

[0067] In step S209, the controller 120 can determine that the motor is faulty if the count value of the driving data among the third driving data that is equal to or greater than the first reference information is 10 or greater, or if the count value of the driving data among the third driving data that is equal to or greater than the second reference information is 5 or greater.

[0068] As described above, the monitoring system 100 and its operating method according to one embodiment disclosed herein can analyze and control the load factor of a servo motor in real time using a moving average control line.

[0069] In addition, the monitoring system 100 can compare past motor driving data with current motor driving data by generating a control line by excluding driving data acquired during a certain period of time before the reference point from the extracted motor driving data.

[0070] FIG. 6 is a flow chart illustrating a method of operation of a monitoring system according to one embodiment disclosed herein. The operation of the monitoring system 100 will now be described with reference to FIGS.

[0071] The monitoring system 100 is substantially similar to the monitoring system 100 described with reference to FIGS. 1 to 5, and will therefore be described briefly below to avoid duplication.

[0072] Referring to FIG. 3, the operation method of the monitoring system 100 may include a step (S301) of acquiring time-dependent driving data of a motor associated with a battery manufacturing apparatus, a step (S302) of applying the driving data to an artificial intelligence model to extract parameters indicating characteristics of the driving data, and a step (S303) of generating reference information for managing the state of the motor based on the parameters.

[0073] In step S301, the data management unit 110 can acquire driving data according to time of a motor associated with the battery manufacturing apparatus 200. In step S301, for example, the data management unit 110 can automatically log driving data of a motor associated with the battery manufacturing apparatus 200.

[0074] In step S302, the data management unit 110 may apply the driving data to an artificial intelligence model to extract parameters that indicate characteristics of the driving data. Here, the parameters may include, for example, a first interval and a second interval. First, the first interval is a value that can distinguish data from the driving data that is used to manage the motor load rate. That is, the first interval is a reference interval that can extract a portion of the driving data and determine the data that is used to manage the motor load rate. The second interval is a value that can distinguish data from the driving data that is excluded for managing the motor load rate. That is, the second interval is a reference interval that can extract a portion of the driving data and determine the data that is excluded from managing the motor load rate.

[0075] In step S302, the data management unit 110 can apply the driving data to the optimization artificial intelligence model to extract parameters that indicate the characteristics of the driving data. In step S302, for example, the data management unit 110 can apply the driving data to the optimization artificial intelligence model to extract first and second intervals that are optimized for managing the motor load rate.

[0076] In step S303, the controller 120 may pre-process the driving data based on the extracted parameters. Specifically, in step S303, the controller 120 may extract first driving data, which is driving data acquired during a first interval before the current time point, from all the acquired driving data.

[0077] In step S303, the controller 120 can extract second driving data, which is driving data acquired during a second interval before the current time point, from all the driving data. In step S303, the controller 120 can generate third drive data by excluding the second drive data from the extracted first drive data.

[0078] In step S303, the controller 120 can generate reference information based on the pre-processed third driving data, where the reference information can include a reference line that can determine the load factor of the motor.

[0079] In step S303, the controller 120 can determine whether the period corresponding to the third driving data is equal to or greater than a threshold period. In step S303, if the third driving data is equal to or greater than the threshold period, the controller 120 can generate reference information based on the third driving data.

[0080] In step S303, the controller 120 can calculate the mean value (μ) and standard deviation (σ) of the third driving data. In step S303, the controller 120 can generate multiple pieces of reference information based on the mean value (μ) and standard deviation (σ) of the third driving data.

[0081] In step S303, for example, the controller 120 can generate, as the first reference information, a "μ+5*σ" value obtained by adding a value obtained by multiplying the average value (μ) of the third driving data by five times the standard deviation (σ). Also, in step S303, for example, the controller 120 can generate, as the second reference information, a "μ+9*σ" value obtained by adding a value obtained by multiplying the average value (μ) of the third driving data by nine times the standard deviation (σ).

[0082] In step S303, the controller 120 can generate a count value based on whether the third driving data exceeds the reference information. Specifically, in step S303, the controller 120 can determine whether the motor has a fault based on whether the count value generated by determining whether the third driving data per unit time exceeds the reference information exceeds a threshold count value.

[0083] In step S303, if the generated count value exceeds the threshold count value, the controller 120 may determine that the motor is faulty and generate a motor abnormality signal. In step S303, if the count value is equal to or less than the threshold count value, the controller 120 can determine that the motor is normal.

[0084] FIG. 7 is a block diagram showing the hardware configuration of a computing system that implements a monitoring system according to an embodiment disclosed herein. Referring to FIG. 7, a computing system 300 according to one embodiment disclosed herein may include an MCU 310, a memory 320, an input / output I / F 330, and a communication I / F 340.

[0085] The MCU 310 may be a processor that executes various programs stored in the memory 320 (e.g., a program for determining whether at least one motor has failed), processes various data through such programs, and performs the functions of the monitoring system 100 shown in FIG. 1 described above.

[0086] The memory 320 can store various programs related to the operation of the monitoring system 100. The memory 320 can also store operational data of the monitoring system 100.

[0087] A plurality of such memories 320 may be provided as necessary. The memories 320 may be volatile memories or nonvolatile memories. As the volatile memories 320, RAM, DRAM, SRAM, etc. may be used. As the nonvolatile memories 320, ROM, PROM, EAROM, EPROM, EEPROM, flash memory, etc. may be used. The examples of the memories 320 listed above are merely illustrative and are not limited to these examples.

[0088] The input / output I / F 330 can provide an interface that connects input devices (not shown) such as a keyboard, mouse, or touch panel, and output devices such as a display (not shown), to the MCU 310, enabling data to be sent and received.

[0089] The communication I / F 340 is configured to be able to send and receive various data to and from a server, and may be any device that supports wired or wireless communication. For example, programs for resistance measurement and abnormality diagnosis, various data, and the like can be sent and received from a separately provided external server via the communication I / F 340.

[0090] In this way, the computer program according to one embodiment disclosed in this document may be recorded in memory 320 and processed by MCU 310, thereby realizing, for example, a module that performs each function of monitoring system 100 described with reference to Figures 1 and 2.

[0091] The above description merely exemplifies the technical concept of the present disclosure, and various modifications and variations are possible by a person having ordinary knowledge in the technical field to which the present disclosure pertains, without departing from the essential characteristics of the present disclosure.

[0092] Therefore, the embodiments disclosed in this disclosure are intended to illustrate, not limit, the technical idea of ​​the disclosure, and the scope of the technical idea of ​​the disclosure is not limited by such embodiments. The scope of protection of the disclosure should be interpreted by the claims below, and all technical ideas within the equivalent range should be interpreted as being included in the scope of rights of the disclosure. [Explanation of symbols]

[0093] 100 Monitoring System 110 Data Management Department 120 Controller 200 Battery manufacturing equipment 210 1st battery manufacturing equipment 220 Second battery manufacturing equipment 230 Third battery manufacturing equipment 300 Computing Systems 310 MCU 320 memory 330 Input / Output Interface 340 Communication I / F

Claims

1. a data management unit that acquires time-dependent driving data of a motor associated with the battery manufacturing apparatus, and applies the driving data to an artificial intelligence model to extract parameters that indicate characteristics of the driving data; a controller that generates reference information for managing the state of the motor based on the parameters; A monitoring system, including:

2. The monitoring system according to claim 1 , wherein the controller preprocesses the driving data based on the parameters, and generates the reference information based on the preprocessed driving data.

3. 3. The monitoring system of claim 2, wherein the data management unit applies the driving data to an optimizer artificial intelligence model and extracts a first section for classifying first driving data from the driving data that is acquired during a predetermined time before a reference point and is used to generate the reference information, and a second section for classifying second driving data that is excluded from generating the reference information.

4. The monitoring system of claim 3, wherein the controller generates third driving data by excluding the second driving data acquired during the second period from the first driving data acquired during the first period.

5. The monitoring system of claim 4, wherein the controller determines whether a period corresponding to the third driving data is equal to or greater than a threshold period, and if the third driving data is equal to or greater than the threshold period, generates the reference information based on the third driving data.

6. The monitoring system according to claim 5 , wherein the controller calculates an average value and a standard deviation of the third driving data, and generates the reference information based on the average value and the standard deviation.

7. the controller generates a count value based on whether the third driving data exceeds the reference information; 7. The monitoring system according to claim 6, wherein the presence or absence of a malfunction of the motor is determined based on whether or not the count value exceeds a threshold count value.

8. 8. The monitoring system according to claim 7, wherein the controller determines that the motor is faulty when the count value exceeds the threshold count value, and generates an abnormality signal for the motor.

9. 8. The monitoring system according to claim 7, wherein the controller determines that the motor is normal when the count value is equal to or less than the threshold count value.

10. acquiring time-dependent drive data for a motor associated with the battery manufacturing apparatus; applying the driving data to an artificial intelligence model to extract parameters characteristic of the driving data; generating reference information for managing the state of the motor based on the parameters; A method of operating a monitoring system, including:

11. generating reference information for managing the state of the motor based on the parameters, The method of claim 10 , further comprising the steps of pre-processing the driving data based on the parameters, and generating the reference information based on the pre-processed driving data.

12. applying the driving data to an artificial intelligence model to extract parameters that indicate characteristics of the driving data, 12. A method for operating a monitoring system as described in claim 11, comprising applying the driving data to an optimized artificial intelligence model and extracting a first section for classifying first driving data from the driving data that is acquired during a predetermined time before a reference point and is used to generate the reference information, and a second section for classifying second driving data that is excluded from generating the reference information.

13. generating reference information for managing the state of the motor based on the parameters, The method of claim 12, further comprising generating third driving data by excluding the second driving data acquired during the second period from the first driving data acquired during the first period.

14. generating reference information for managing the state of the motor based on the parameters, 14. A method for operating a monitoring system as described in claim 13, comprising a step of determining whether a period corresponding to the third driving data is equal to or greater than a threshold period, and generating the reference information based on the third driving data if the third driving data is equal to or greater than the threshold period.

15. generating reference information for managing the state of the motor based on the parameters, 15. The method of claim 14, further comprising the steps of: calculating a mean value and a standard deviation of the third driving data; and generating the reference information based on the mean value and the standard deviation.

16. generating reference information for managing the state of the motor based on the parameters, generating a count value based on whether the third driving data exceeds the reference information; determining whether or not the motor has a fault based on whether or not the count value exceeds a threshold count value; 16. A method of operating the monitoring system of claim 15, comprising:

17. generating reference information for managing the state of the motor based on the parameters, 17. The method of claim 16, further comprising the step of determining that the motor has failed and generating an abnormality signal for the motor if the count value exceeds the threshold count value.

18. generating reference information for managing the state of the motor based on the parameters, 17. The method of claim 16, further comprising determining that the motor is normal if the count value is equal to or less than the threshold count value.

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