Monitoring system and operation method thereof

Abstract

A monitoring system according to an embodiment disclosed in this document may include: a data management unit configured to acquire driving data of a motor associated with a battery manufacturing apparatus over time and extract a parameter representing a characteristic of the driving data by applying the driving data to an artificial intelligence model; and a controller configured to generate reference information for managing a state of the motor based on the parameter.

Description

Technical Field

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] The embodiments disclosed in this document claim the priority benefit based on Korean Patent Application No. 10-2022-0134711 filed on October 19, 2022, and all contents disclosed in the Korean Patent Application are incorporated as a part of this specification. Technical Field

[0004] Embodiments disclosed in this document relate to monitoring systems and methods of operating the same. Background Art

[0005] The electric vehicle receives power from the outside to charge the battery cell, and then drives the motor using the voltage charged in the battery cell to obtain power. The battery cell of the electric vehicle is manufactured by accommodating an electrode assembly in a battery case and injecting an electrolyte into the battery case.

[0006] If the welded parts of the battery cell are partially or completely separated due to external impact or welding defects, the battery cell may deteriorate or explode. Therefore, the battery process system monitors the load factor of the servo motor included in the battery manufacturing device to quickly diagnose and analyze the failure in the event of a failure in the battery manufacturing process.

[0007] Typically, a battery process system monitors the load factor of a servo motor based on a fixed control line, resulting in problems such as excessive inspection due to failure to update the fixed control line when the battery process conditions change and high management burden on administrators due to control line updates. On the other hand, a method of monitoring the load factor of a servo motor based on a control line of a moving average method automatically updates the control line according to changes in the battery process conditions, resulting in a lower management burden on administrators; however, it cannot detect a gradual increase in the load factor of the servo motor, resulting in a false detection problem. Summary of the invention

[0008] Technical issues

[0009] An object of the embodiments disclosed in this document is to provide a monitoring system and an operating method thereof, which can analyze and control the load factor of a servo motor in real time using a moving average control line of a battery process system.

[0010] The technical purposes of the embodiments disclosed in this document are not limited to the above, and other purposes not described herein are clearly understood by those skilled in the art from the following description.

[0011] Technical Solution

[0012] A monitoring system according to an embodiment disclosed in the present embodiment may include: a data management unit configured to acquire driving data of a motor associated with a battery manufacturing device over time, and extract parameters representing characteristics of the driving data by applying the driving data to an artificial intelligence model; and a controller configured to generate reference information for managing the state of the motor based on the parameters.

[0013] According to one embodiment, the controller may pre-process the driving data based on the parameter and generate the reference information based on the pre-processed driving data.

[0014] According to one embodiment, the data management unit can extract a first interval and a second interval by applying the driving data to the optimizer intelligent model, wherein the first interval is used to distinguish the first driving data obtained during a predetermined time period before a reference time point among the driving data for generating the reference information, and the second interval is used to distinguish the second driving data to be excluded in order to generate the reference information.

[0015] According to one embodiment, the controller may generate the third driving data by excluding the second driving data obtained during the second interval from the first driving data obtained during the first interval among the driving data.

[0016] According to one embodiment, the controller may determine whether a duration corresponding to the third driving data is equal to or longer than a threshold period and generate reference information based on the third driving data in response to the driving data being equal to or longer than the threshold period.

[0017] According to one embodiment, the controller may 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.

[0018] According to one embodiment, the controller may generate a count value based on whether the third driving data exceeds reference information, and determine whether the motor fails based on whether the generated count value exceeds a threshold count value.

[0019] According to one embodiment, the controller may determine that the motor fails and generate a motor abnormality signal based on the count value being greater than a threshold count value.

[0020] According to one embodiment, the controller may determine that the motor is operating normally based on the count value being equal to or less than a threshold count value.

[0021] The operating method of the monitoring system according to the embodiment disclosed in this document may include the following steps: acquiring driving data of a motor associated with a battery manufacturing device over time; extracting parameters representing characteristics of the driving data by applying the driving data to an artificial intelligence model; and generating reference information for managing the state of the motor based on the parameters.

[0022] According to one embodiment, the step of generating reference information for managing the state of the motor based on the parameter may include pre-processing the drive data based on the parameter and generating the reference information based on the pre-processed drive data.

[0023] According to one embodiment, the step of extracting parameters representing characteristics of the driving data by applying the driving data to the artificial intelligence model may include: extracting a first interval and a second interval by applying the driving data to the optimizer intelligent model, the first interval being used to distinguish first driving data obtained during a predetermined time period before a reference time point among the driving data for generating the reference information, and the second interval being used to distinguish second driving data to be excluded in order to generate the reference information.

[0024] According to one embodiment, generating reference information for managing the state of the motor based on the parameter may include generating third drive data by excluding the second drive data obtained during the second interval from the first drive data obtained during the first interval among the drive data.

[0025] According to one embodiment, the step of generating reference information for managing the state of the motor based on the parameters may include: determining whether the duration corresponding to the third drive data is equal to or longer than a threshold period, and generating the reference information based on the third drive data in response to the drive data being equal to or longer than the threshold period.

[0026] According to one embodiment, the step of generating reference information for managing the state of the motor based on the parameter may include 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.

[0027] According to one embodiment, the step of generating reference information for managing the state of the motor based on the parameters may include: generating a count value based on whether the third driving data exceeds the reference information, and determining whether the motor fails based on whether the generated count value exceeds a threshold count value.

[0028] According to one embodiment, the step of generating reference information for managing the state of the motor based on the parameter may include determining that the motor fails and generating a motor abnormality signal based on the count value being greater than the threshold count value.

[0029] According to one embodiment, the step of generating reference information for managing the state of the motor based on the parameter may include determining that the motor operates normally based on the count value being equal to or less than the threshold count value.

[0030] Beneficial Effects

[0031] The monitoring system and the operating method thereof according to the embodiments disclosed in this document can analyze and control the load factor of the servo motor in real time using a moving average control line. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is a schematic diagram illustrating a battery process system according to an embodiment disclosed in this document;

[0033] Figure 2 is a block diagram illustrating a configuration of a monitoring system according to an embodiment disclosed in this document;

[0034] Figure 3 is a diagram illustrating a parameter extraction operation of a data management unit according to an embodiment disclosed in this document;

[0035] Figure 4 is a flowchart illustrating a third driving data extraction operation of the controller according to an embodiment disclosed in this document;

[0036] Figure 5 is a flowchart illustrating a reference information generating operation of a controller according to an embodiment disclosed in this document;

[0037] Figure 6 is a flowchart illustrating a method of operating a monitoring system according to an embodiment disclosed in this document; and

[0038] Figure 7 is a diagram illustrating a hardware configuration of a computing system implementing a monitoring system according to an embodiment disclosed in this document. DETAILED DESCRIPTION

[0039] Hereinafter, the embodiments disclosed in this document will be described in detail with reference to the exemplary drawings. When assigning reference numerals to the components of each drawing, it should be noted that even if the same components are shown in different drawings, they have the same reference numerals as much as possible. Detailed descriptions of well-known structures or functions in conjunction with the embodiments disclosed in this document may be omitted to avoid obscuring the understanding of the embodiments disclosed in this document.

[0040] Terms such as "first", "second", "A", "B", "(a)", and "(b)" may be used to describe components of the embodiments disclosed in this document. These terms are only used to distinguish one component from another, and the properties, order, or sequence of the corresponding components are not limited by the terms. Unless otherwise defined herein, all terms used herein, including technical terms or scientific terms, have the same meanings as those commonly understood by technicians in the field to which the embodiments disclosed in this document belong. It should also be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having meanings consistent with their meanings in the context of the relevant art and should not be interpreted in an idealized or overly formal sense unless expressly defined as such herein.

[0041] Figure 1 is a schematic diagram illustrating a battery process system according to an embodiment disclosed in this document.

[0042] According to various embodiments, the battery may include a battery cell that is a basic unit of a battery that can be charged and discharged to use electrical energy. The battery cell may be a lithium ion (Li-iOn) battery, a lithium ion polymer (Li-iOn polymer) battery, a nickel cadmium (Ni-Cd) battery, or a nickel metal hydride (Ni-MH) battery, and is not limited to these. 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, P-DVDs, MP3Ps, mobile phones, PDAs, portable game devices, power tools, and E-bikes, as well as large products that require high power such as electric vehicles or hybrid vehicles, power storage devices for storing excess generated power or renewable energy, or backup power storage devices.

[0043] The battery cell may include an electrode assembly, a battery case in which the electrode assembly is housed, and an electrolyte injected into the battery case to activate the electrode assembly. The electrode assembly is formed by inserting a separator between a cathode plate formed by coating a cathode current collector with a cathode active material and an anode plate formed by coating an anode current collector with an anode active material, and the electrode assembly may be manufactured in a roll-type or a stacked type and housed in the battery case, depending on the type of the battery case. The battery case is used as an external material to maintain the shape of the battery and protect the battery from external impacts, and the battery cell may be classified into a cylindrical type, a prismatic type, and a pouch type according to the type of the battery case.

[0044] According to an embodiment, a battery cell may be manufactured through a series of manufacturing processes, including electrode manufacturing, assembly process, and chemical process. Here, the assembly process includes assembling the positive electrode plate and the negative electrode plate produced by the electrode manufacturing process and injecting electrolyte, and may include a slotting process, a winding process, an assembly process, and a packaging process.

[0045] The packaging process can be defined as the process of injecting and sealing the electrode assembly and electrolyte in the battery case. For cylindrical battery cells, the electrode assembly is installed 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 is welded to the top cap of the cap assembly while the electrode assembly and electrolyte are in place.

[0046] Hereinafter, an exemplary case where the battery process system is applied to an assembly process is described. For example, the battery process system can be used in a packaging process of an assembly process system, but is not limited thereto.

[0047] Reference Figure 1 The battery process system may include a monitoring system 100 and at least one battery manufacturing device 200 .

[0048] The monitoring system 100 may collect and analyze data from the battery manufacturing device 200 operating in the battery process system in real time. The monitoring system 100 may collect and analyze operation data from at least one battery manufacturing device 200. In addition, the monitoring system 100 may collect operation data from a process control device (PLC) (not shown) that controls the battery manufacturing device 200. Here, the operation data of the battery manufacturing device 200 may include an operation record of the battery manufacturing device 200.

[0049] For example, the monitoring system 100 may collect and analyze data or graphic data generated within the battery manufacturing process system, such as process progress status, alarm occurrence, temperature, pressure, quantity, etc., from the battery manufacturing apparatus 200 .

[0050] The monitoring system 100 may detect abnormal data within the operation data of the battery manufacturing apparatus 200. The monitoring system 100 may analyze the battery manufacturing apparatus 200 corresponding to the abnormal data.

[0051] 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. Figure 1 Three battery manufacturing apparatuses 200 are depicted in FIG. 1 , but this is not limitative, and the battery manufacturing apparatus 200 may include n (where n is a natural number greater than or equal to 1) apparatuses.

[0052] According to an embodiment, the battery manufacturing apparatus 200 may weld a positive electrode tab of an electrode assembly of a battery cell and a top cap of a cap assembly during a packaging process of the battery cell.

[0053] According to an embodiment, the monitoring system 100 may acquire driving data of motors associated with a plurality of battery manufacturing devices 200. Here, the motor may include, for example, a servo motor. The servo motor is a motor designed to quickly and accurately follow a user's position or speed control command, including a control drive board.

[0054] The monitoring system 100 can, for example, manage the load factor of the servo motor. The monitoring system 100 can use a moving average calculation method to monitor the gradual increase in the load factor of the motor associated with the plurality of battery manufacturing devices 200. When the operating conditions of the plurality of battery manufacturing devices 200 change, the monitoring system 100 can use a moving average calculation method to automatically calculate the load factor of the battery manufacturing device 200.

[0055] Figure 2 is a block diagram illustrating a configuration of a monitoring system 100 according to an embodiment disclosed in this document.

[0056] Reference Figure 2 , the monitoring system 100 may include a data management unit 110 and a controller 120 .

[0057] The data management unit 110 may acquire time-based driving data of a motor associated with the battery manufacturing apparatus 200. For example, the data management unit 110 may automatically record driving data of a motor associated with the battery manufacturing apparatus 200. For example, the data management unit 110 may collect driving data of the motor by setting an automatic data recording sampling interval of 0.1 seconds.

[0058] The data management unit 110 may apply the drive data to the artificial intelligence model to extract parameters representing the characteristics of the drive data. Here, the parameters may include, for example, a first interval and a second interval. First, the first interval is a value for distinguishing data to be used for managing the load factor of the motor. That is, the first interval is used as a reference interval for determining the data to be used for managing the load factor of the motor by extracting some drive data. For example, in the case where the first interval is one week, the monitoring system 100 may use the drive data obtained within the past week among the drive data to manage the load factor of the motor.

[0059] The second interval is a value for distinguishing data to be excluded from the drive data for managing the load factor of the motor. That is, the second interval is used as a reference interval for determining data to be excluded from managing the load factor of the motor by extracting some drive data. For example, in the case where the second interval is 12 hours, the monitoring system 100 can manage the load factor of the motor by excluding the drive data obtained within the last 12 hours from the drive data.

[0060] The data management unit 110 may apply the driving data to the optimizer artificial intelligence model to extract parameters representing characteristics of the driving data. Here, the optimizer artificial intelligence model may use a gradient descent method to extract parameters representing characteristics of the driving data.

[0061] For example, the data management unit 110 may extract the first interval and the second interval optimized for the load factor management of the motor by applying the driving data to the optimizer artificial intelligence model.

[0062] The controller 120 may pre-process the drive data based on the extracted parameters. Specifically, the controller 120 may extract the first drive data (which is the drive data acquired during the first interval before the current point) from the entire drive data acquired. In addition, the controller 120 may extract the second drive data (which is the drive data acquired during the second interval before the current point) from the entire drive data. The controller 120 may generate the third drive data by excluding the second drive data from the extracted first drive data.

[0063] The controller 120 may generate reference information based on the preprocessed third driving data. Here, the reference information may include reference information that may be used to determine a load factor of the motor.

[0064] The controller 120 may determine whether the period corresponding to the third driving data is equal to or longer than the threshold period. When the third driving data exceeds the threshold period, the controller 120 may generate reference information based on the third driving data. For example, when the third driving data generated by preprocessing the driving data is equal to or greater than the data acquired within 3.5 days, the controller 120 may generate reference information based on the third driving data.

[0065] The controller 120 may calculate the average value μ and the standard deviation σ of the third drive data. The controller 120 may generate a plurality of reference information based on the average value μ and the standard deviation σ of the third drive data. For example, the controller 120 may generate the first reference information of "μ+5*σ" by adding the value obtained by multiplying the standard deviation σ by 5 to the average value μ of the third drive data. In addition, for example, the controller 120 may generate the second reference information of "μ+9*σ" by adding the value obtained by multiplying the standard deviation σ by 9 to the average value μ of the third drive data.

[0066] The controller 120 may generate a count value based on whether the third drive data exceeds the reference information. Specifically, the controller 120 may determine whether the count value generated based on whether the third drive data exceeds the reference information is greater than a threshold count value to determine whether the motor fails. When the generated count value is greater than the threshold count value, the controller 120 may determine that the motor fails and may generate a motor abnormality signal. In addition, when the count value is equal to or less than the threshold count value, the controller 120 may determine that the motor is operating normally.

[0067] Figure 3 is a diagram illustrating a parameter extraction operation of a data management unit according to an embodiment disclosed in this document.

[0068] Reference Figure 3 In step S101 , the data management unit 110 may acquire driving data of the motor.

[0069] In step S102 , the data management unit 110 may classify the driving data of the motor into test data and training data.

[0070] In step S103, the data management unit 110 may input the training data among the driving data of the motor into the optimization function. In step S103, the data management unit 110 may set the initial value (Initiator), threshold (Threshold) and learning rate of the optimization function. In step S103, for example, the data management unit 110 may input the parameter values ​​randomly extracted based on the driving data into the optimization function. Here, the randomly extracted parameter values ​​may include multiples of the standard deviation σ to be used in the first interval, the second interval or the reference information. For example, the randomly extracted parameter values ​​may include 7 days, 14 days and 21 days of the first interval, 6 hours, 12 hours and 1 day of the second interval, and multiples of the standard deviation σ such as 3 times, 4 times and 5 times of the reference information.

[0071] In step S104, the data management unit 110 may calculate the accuracy of the optimization function. In step S104, the data management unit 110 may use the accuracy of the optimization function to determine whether "accuracy (i) - accuracy (i-1)" is equal to or less than a first threshold value (threshold 1). Here, "i" represents the number of learning iterations of the optimization function.

[0072] In step S105, the data management unit 110 may use the accuracy of the optimization function to determine whether "Accuracy (i)" is equal to or greater than a second threshold value (threshold 2). Here, "i" represents the number of learning iterations of the optimization function. In step S105, when "Accuracy (i)" is equal to or greater than the second threshold value (threshold 2), the data management unit 110 may terminate the operation of the optimization function.

[0073] In step S106, when "accuracy (i) - accuracy (i-1)" is greater than the first threshold (threshold 1) or when "accuracy (i)" is less than the second threshold (threshold 2), the data management unit 110 may determine whether the learning rate i is equal to or greater than 1000. In step S106, when the learning rate i is greater than or equal to 1000, the data management unit 110 may terminate the operation of the optimization function.

[0074] In step S107 , the data management unit 110 may use the test data to calculate the final accuracy of the optimization function.

[0075] Figure 4 is a flowchart illustrating a third driving data extracting operation of the controller according to an embodiment disclosed in this document.

[0076] Reference Figure 4 , the controller 120 may extract the first driving data (which is the driving data acquired during the first interval before the reference information generation point) from the entire driving data. For example, when the first interval extracted by the data management unit 110 is 7 days, 14 days, or 21 days, the controller 120 may extract the first driving data acquired during each 7-day, 14-day, or 21-day period before the reference information generation point from the entire driving data.

[0077] In addition, the controller 120 may extract the second driving data obtained during the second interval before the reference information generation point from the entire driving data. For example, when the second interval extracted by the data management unit 110 is 12 hours, 1 day, 2 days, or 3 days, the controller 120 may extract the second driving data obtained during each 12 hour, 1 day, 2 day, or 3 day period before the reference information generation point from the entire driving data.

[0078] The controller 120 may generate the third driving data by excluding the second driving data from the extracted first driving data. For example, when the first driving data is obtained during 7 days, 14 days, or 21 days before the reference information generation point, and the second driving data is obtained during 12 hours, 1 day, 2 days, or 3 days before the reference information generation point, the controller 120 may generate the third driving data by excluding the driving data obtained during 12 hours, 1 day, 2 days, or 3 days before the reference information generation point from the first driving data obtained during 7 days, 14 days, or 21 days before the time point when the reference information corresponding to the second driving data is generated.

[0079] The controller 120 may generate reference information based on the preprocessed third driving data.

[0080] Figure 5 is a flowchart illustrating a reference information generating operation of a controller according to an embodiment disclosed in this document.

[0081] Reference Figure 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.

[0082] In step S202 , the controller 120 may determine whether a period corresponding to the third driving data is equal to or longer than a threshold period.

[0083] In step S203, the controller 120 may determine whether the third driving data exceeds a threshold period. In step S202, for example, the controller 120 may determine whether the third driving data is equal to or greater than data obtained during a period of 3.5 days.

[0084] In step S203, the controller 120 may generate reference information based on the third drive data. In step S203, the controller 120 may calculate the average value μ and the standard deviation σ of the third drive data. In step S203, the controller 120 may generate first reference information of "μ+5*σ" by adding a value obtained by multiplying the standard deviation σ by 5 to the average value μ of the third drive data. In step S203, the controller 120 may generate second reference information of "μ+9*σ" by adding a value obtained by multiplying the standard deviation σ by 9 to the average value μ of the third drive data.

[0085] In step S204 , when the third driving data does not exceed the threshold period, the controller 120 may not generate reference information.

[0086] In step S205, the controller may determine whether the driving data obtained after the reference information is generated exceeds the reference information. In step S205, for example, the controller 120 may determine whether the driving data obtained during the last 10 minutes exceeds the reference information.

[0087] In step S206, the controller 120 may generate a count value based on whether the third driving data exceeds the reference information. For example, in step S206, the controller 120 may determine whether the count value of the driving data exceeding the first reference information among the third driving data is equal to or greater than 10.

[0088] In step S207 , when driving data exceeding the reference information has not been obtained after the reference information is generated, the controller 120 may determine that the motor operates normally.

[0089] In step S208 , the controller 120 may determine whether a count value of driving data exceeding the second reference information among the third driving data is equal to or greater than 5.

[0090] In step S209, when the count value of the driving data exceeding the first reference information among the third driving data is equal to or greater than 10, or when the count value of the driving data exceeding the second reference information among the third driving data is equal to or greater than 5, the controller 120 may determine that the motor fails.

[0091] As described above, the monitoring system 100 and the operating method thereof according to the embodiment disclosed in this document can analyze and control the load factor of the servo motor in real time using a moving average control line.

[0092] Furthermore, the monitoring system 100 may compare past driving data and current driving data of the motor by creating a management line by excluding driving data acquired during a predetermined period before a reference time point from the extracted motor driving data.

[0093] Figure 6 is a flow chart illustrating a method of operating a monitoring system according to an embodiment disclosed in this document.

[0094] In the following, reference is made to Figures 1 to 5 The operation method of the monitoring system 100 is described.

[0095] Since the monitoring system 100 can be used with reference Figures 1 to 5 The monitoring systems 100 described are substantially the same, so a brief description is provided below to avoid redundancy in the explanation.

[0096] Reference Figure 3The operation of the monitoring system 100 may include: acquiring driving data of a motor associated with operating a battery manufacturing device over time in step S301; extracting parameters representing characteristics of the driving data by applying the driving data to an artificial intelligence model in step S302; and generating reference information for managing the state of the motor based on the parameters in step S303.

[0097] In step S301, the data management unit 110 may acquire time-based driving data of a motor associated with the battery manufacturing apparatus 200. In step S301, for example, the data management unit 110 may automatically record driving data of a motor associated with the battery manufacturing apparatus 200.

[0098] In step S302, the data management unit 110 may apply the drive data to the artificial intelligence model to extract parameters representing the characteristics of the drive data. Here, the parameters may include, for example, a first interval and a second interval. First, the first interval is a value for distinguishing data to be used for managing the load factor of the motor. That is, the first interval is used as a reference interval for determining the data to be used for managing the load factor of the motor by extracting some drive data. The second interval is a value for distinguishing data to be excluded from the drive data in order to manage the load factor of the motor. That is, the second interval is used as a reference interval for determining the data to be excluded from the load factor of the motor by extracting some drive data.

[0099] In step S302, the data management unit 110 may apply the driving data to the optimizer artificial intelligence model to extract parameters representing characteristics of the driving data. In step S302, for example, the data management unit 110 may extract first and second intervals optimized for load factor management of the motor by applying the driving data to the optimizer artificial intelligence model.

[0100] In step S303, the controller 120 may pre-process the driving data based on the extracted parameters. In step S303, in detail, the controller 120 may extract first driving data (which is driving data acquired during a first interval before the current point) from the acquired entire driving data.

[0101] In step S303 , the controller 120 may extract second driving data (which is driving data acquired during a second interval before the current point) from the entire driving data.

[0102] In step S303 , the controller 120 may generate third driving data by excluding the second driving data from the extracted first driving data.

[0103] In step S303, the controller 120 may generate reference information based on the preprocessed third driving data. Here, the reference information may include reference information that can be used to determine the load factor of the motor.

[0104] In step S303, the controller 120 may determine whether a period corresponding to the third driving data is equal to or longer than a threshold period. In step S303, when the third driving data exceeds the threshold period, the controller 120 may generate reference information based on the third driving data.

[0105] In step S303, the controller 120 may calculate an average value μ and a standard deviation σ of the third driving data. In step S303, the controller 120 may generate a plurality of reference information based on the average value μ and the standard deviation σ of the third driving data.

[0106] In step S303, for example, the controller 120 may generate first reference information of "μ+5*σ" by adding a value obtained by multiplying the standard deviation σ by 5 to the average value μ of the third drive data. In step S303, the controller 120 may also generate second reference information of "μ+9*σ" by adding a value obtained by multiplying the standard deviation σ by 9 to the average value μ of the third drive data.

[0107] In step S303, the controller 120 may generate a count value based on whether the third driving data exceeds the reference information. In step S303, in detail, the controller 120 may determine whether the count value generated based on whether the third driving data exceeds the reference information is greater than a threshold count value to determine whether the motor fails.

[0108] In step S303 , when the generated count value is greater than the threshold count value, the controller 120 may determine that the motor fails and may generate a motor abnormality signal.

[0109] In step S303 , when the count value is equal to or less than the threshold count value, the controller 120 may determine that the motor operates normally.

[0110] Figure 7 is a diagram illustrating a hardware configuration of a computing system implementing a monitoring system according to an embodiment disclosed in this document.

[0111] Reference Figure 7 , a computing system 300 according to an embodiment disclosed in this document may include an MCU 310 , a memory 320 , an input and output I / F 330 , and a communication I / F 340 .

[0112] The MCU 310 may be a program for executing various programs (eg, a program for diagnosing a fault of at least one motor) stored in the memory 320, processing various data using the programs, and executing Figure 1 A processor that monitors the functionality of system 100 is shown.

[0113] The memory 320 may store various programs related to the operation of the monitoring system 100. In addition, the memory 320 may store operation data of the monitoring system 100.

[0114] The memory 320 may be provided in plurality as required. The memory 320 may be a volatile memory or a nonvolatile memory. The memory 320 as a volatile memory may be a RAM, a DRAM, a SRAM, etc. The memory 320 as a nonvolatile memory may be a ROM, a PROM, an EAROM, an EPROM, an EEPROM, a flash memory, etc. The memory 320 is not limited to the listed examples and is not limited to these examples.

[0115] The input and output I / F 330 is an interface connecting an input device (not shown) such as a keyboard, a mouse, or a touch panel, an output device such as a display (not shown), and the MCU 310 to transmit and receive data.

[0116] The communication I / F 340 may be a component capable of communicating various data with a server, including various devices supporting wired or wireless communication. For example, through the communication I / F 340, a program for resistance measurement and abnormality diagnosis and various data may be sent to and received from a separately established external server.

[0117] In this way, the computer program according to the embodiment disclosed in this document can be recorded in the memory 320 and processed by the MCU 310 to be implemented as executing, for example, referring to Figure 1 and Figure 2 Modules that describe various functions of the monitoring system 100.

[0118] The above description is merely an illustrative example of the technical concept of the present disclosure, and those skilled in the art to which the present disclosure pertains will be able to make various modifications and changes without departing from the subject matter of the present disclosure.

[0119] Therefore, the embodiments disclosed in the present disclosure are not intended to limit but to describe the technical concept of the present disclosure, and the scope of the technical concept of the present disclosure is not limited by the embodiments. The protection scope of the technical concept of the present disclosure should be interpreted by the attached claims, and all technical concepts within their equivalent scope should be interpreted as within the scope of the rights of the present disclosure.

Claims

1. A monitoring system, the monitoring system include: a data management unit configured to acquire drive data of a motor associated with a battery manufacturing device over time, and extract parameters representing characteristics of the drive data by applying the drive data to an artificial intelligence model; as well as A controller is configured to generate reference information for managing a state of the motor based on the parameters.

2. The monitoring system according to claim 1, in, The controller is configured to pre-process the driving data based on the parameter and generate the reference information based on the pre-processed driving data.

3. The monitoring system according to claim 2, in, The data management unit is configured to extract a first interval and a second interval by applying the driving data to an optimizer intelligent model, wherein the first interval is used to distinguish first driving data obtained during a predetermined time period before a reference time point among the driving data for generating the reference information, and the second interval is used to distinguish second driving data to be excluded in order to generate the reference information.

4. The monitoring system according to claim 3, in, The controller is configured to generate third drive data by excluding the second drive data obtained during the second interval from the first drive data obtained during the first interval among the drive data.

5. The monitoring system according to claim 4, in, The controller is configured to determine whether a duration corresponding to the third driving data is equal to or longer than a threshold period and generate the reference information based on the third driving data in response to the driving data being equal to or longer than the threshold period.

6. The monitoring system according to claim 5, in, The controller is configured to 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.

7. The monitoring system according to claim 6, in, The controller is configured to generate a count value based on whether the third driving data exceeds the reference information, and determine whether the motor fails based on whether the generated count value exceeds a threshold count value.

8. The monitoring system according to claim 7, in, The controller is configured to determine that the motor is faulty and generate a motor abnormality signal based on the count value being greater than the threshold count value.

9. The monitoring system according to claim 7, in, The controller is configured to determine that the motor is operating properly based on the count value being equal to or less than the threshold count value.

10. A method for operating a monitoring system, the method for operating the monitoring system The following steps are involved: acquiring driving data over time of a motor associated with a battery manufacturing device; extracting parameters representing characteristics of the drive data by applying the drive data to an artificial intelligence model; as well as Reference information for managing a state of the motor is generated based on the parameters.

11. The method for operating the monitoring system according to claim 10, in, The step of generating reference information for managing the state of the motor based on the parameter includes preprocessing the drive data based on the parameter and generating the reference information based on the preprocessed drive data.

12. The method for operating the monitoring system according to claim 11, in, The step of extracting parameters representing characteristics of the driving data by applying the driving data to the artificial intelligence model includes: extracting a first interval and a second interval by applying the driving data to the optimizer intelligent model, wherein the first interval is used to distinguish first driving data obtained during a predetermined time period before a reference time point among the driving data for generating the reference information, and the second interval is used to distinguish second driving data to be excluded in order to generate the reference information.

13. The method for operating the monitoring system according to claim 12, in, The step of generating reference information for managing the state of the motor based on the parameter includes generating third drive data by excluding the second drive data obtained during the second interval from the first drive data obtained during the first interval among the drive data.

14. The method for operating the monitoring system according to claim 13, in, The step of generating reference information for managing the state of the motor based on the parameters includes: determining whether a duration corresponding to the third drive data is equal to or longer than a threshold period, and generating the reference information based on the third drive data in response to the drive data being equal to or longer than the threshold period.

15. The method for operating the monitoring system according to claim 14, in, The step of generating reference information for managing the state of the motor based on the parameter includes 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. The method for operating the monitoring system according to claim 15, in, Generating reference information for managing the state of the motor based on the parameter includes generating a count value based on whether the third driving data exceeds the reference information, and determining whether the motor fails based on whether the generated count value exceeds a threshold count value.

17. The method for operating the monitoring system according to claim 16, in, The step of generating reference information for managing the state of the motor based on the parameter includes determining that the motor fails based on the count value being greater than the threshold count value and generating a motor abnormality signal.

18. The method for operating the monitoring system according to claim 16, in, The step of generating reference information for managing the state of the motor based on the parameter includes determining that the motor is operating normally based on the count value being equal to or less than the threshold count value.

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