Abnormality detection system and molding machine system

Abstract

To provide an abnormality detection system capable of detecting an abnormality in a manufacturing device and diagnosing a state of the manufacturing device without imposing excessive load on a control device performing operation control of the manufacturing device.SOLUTION: An abnormality detection system comprises: an abnormality detection device that detects an abnormality in a manufacturing device; a control device that performs operation control of the manufacturing device and transmits operation data; a sensor that detects a physical quantity related to the operation or a product of the manufacturing device and outputs sensor value data; and a diagnosis device. The abnormality detection device determines whether or not an abnormality has occurred in the manufacturing device on the basis of the operation data transmitted from the control device and the sensor value data output from the sensor and transmits a determination result to the control device. The abnormality detection device also transmits the operation data and the sensor value data to the diagnosis device, and the diagnosis device diagnoses the state of the manufacturing device on the basis of past sensor value data accumulated in a database and the received data, and transmits a diagnosis result to the abnormality detection device. The abnormality detection device transmits the diagnosis result to the control device.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an abnormality detection system, a molding machine system, an abnormality detection device, an abnormality detection method, and a computer program. [Background technology]

[0002] Patent Document 1 discloses a monitoring method for monitoring the vibration of each moving part of an injection molding machine by an acceleration sensor and detecting the state of the molding process and the occurrence of an abnormality in the moving parts. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 5-50480 Summary of the Invention [Problem to be solved by the invention]

[0004] However, Patent Document 1 does not disclose a specific technique for configuring a control device that controls the operation of a manufacturing device such as an injection molding machine and a device that detects abnormalities in the manufacturing device or diagnoses the state of the manufacturing device separately. If the control device is configured to detect and diagnose abnormalities in the manufacturing device, there is a possibility that the operation of the manufacturing device will be adversely affected, such as delays in the operation control process.

[0005] An object of the present disclosure is to provide an anomaly detection system, a molding machine system, an anomaly detection device, an anomaly detection method, and a computer program that are equipped with a control device that controls the operation of a manufacturing equipment, an anomaly detection device, and a diagnostic device, and that are capable of detecting anomalies in the manufacturing equipment and diagnosing the condition of the manufacturing equipment without placing an excessive load on the control device by each device working in cooperation with each other. [Means for solving the problem]

[0006] An anomaly detection system according to the present disclosure is an anomaly detection system including an anomaly detection device that detects an anomaly in a manufacturing device, and a diagnosis device that diagnoses a state of the manufacturing device, the anomaly detection system including a control device that performs operation control of the manufacturing device and transmits operation data indicating the content of the operation control to the anomaly detection device, a sensor that detects a physical quantity related to an operation or product of the manufacturing device and outputs time-series sensor value data indicating the detected physical quantity to the anomaly detection device, and a database that accumulates information including the operation data, the sensor value data, and a state of the manufacturing device, the anomaly detection device including a first communication unit that receives the operation data transmitted from the control device, an acquisition unit that acquires the sensor value data output from the sensor, and a calculation unit that calculates statistics of the sensor value data acquired by the acquisition unit and a database that accumulates information including the calculated statistics. and a processing unit that determines whether or not there is an abnormality in the manufacturing equipment based on a threshold value corresponding to the received operating data, wherein the first communication unit transmits the operating data and the sensor value data to the diagnostic device, and the diagnostic device comprises a second communication unit that receives the operating data and the sensor value data transmitted from the abnormality detection device, and a diagnostic processing unit that diagnoses the condition of the manufacturing equipment based on the operating data and the sensor value data received by the second communication unit, as well as information accumulated in the database, and the second communication unit transmits a diagnosis result by the diagnostic processing unit to the abnormality detection device, and the first communication unit of the abnormality detection device receives the diagnosis result by the diagnostic processing unit and transmits the judgment result by the processing unit, the statistics, and the diagnostic result by the diagnostic processing unit to the control device.

[0007] A molding machine system according to the present disclosure includes the above-described abnormality detection system and a molding machine, and the abnormality detection system is configured to detect an abnormality in the molding machine.

[0008] The anomaly detection device according to the present disclosure is an anomaly detection device that detects an anomaly in a manufacturing apparatus, and includes a communication unit that receives operating data indicating the content of the operating control transmitted from a control device that controls the operation of the manufacturing apparatus, an acquisition unit that acquires time-series sensor value data output from a sensor that detects physical quantities related to the operation or product of the manufacturing apparatus, and a processing unit that calculates statistics of the sensor value data acquired by the acquisition unit and determines whether or not there is an anomaly in the manufacturing apparatus based on the calculated statistics and a threshold value corresponding to the acquired operating data, and the communication unit transmits the operating data and the sensor value data to an external diagnostic device that diagnoses the state of the manufacturing apparatus, receives a diagnostic result transmitted from the diagnostic device, and transmits the determination result by the processing unit, the statistics, and the received diagnostic result to the control device.

[0009] The anomaly detection method according to the present disclosure is an anomaly detection method in which a computer executes a process for detecting an anomaly in a manufacturing apparatus, in which the computer receives operation data indicating the content of the operation control transmitted from a control device that controls the operation of the manufacturing apparatus, acquires time-series sensor value data output from a sensor that detects a physical quantity related to the operation or product of the manufacturing apparatus, calculates statistics of the acquired sensor value data, determines whether or not there is an anomaly in the manufacturing apparatus based on the calculated statistics and a threshold value corresponding to the acquired operation data, transmits the operation data and the sensor value data to an external diagnostic device that diagnoses the state of the manufacturing apparatus, receives a diagnostic result transmitted from the diagnostic device, and executes a process of transmitting a determination result indicating the presence or absence of an anomaly, the statistics, and the received diagnostic result to the control apparatus.

[0010] A computer program according to the present disclosure is a computer program for causing a computer to execute a process of detecting an abnormality in a manufacturing apparatus, and causes the computer to execute the following processes: receive operating data indicating the content of the operating control transmitted from a control device that controls the operation of the manufacturing apparatus, acquire time-series sensor value data output from a sensor that detects a physical quantity related to the operation or product of the manufacturing apparatus, calculate statistics of the acquired sensor value data, determine whether or not an abnormality exists in the manufacturing apparatus based on the calculated statistics and a threshold value corresponding to the acquired operating data, transmit the operating data and the sensor value data to an external diagnostic device that diagnoses the state of the manufacturing apparatus, receive a diagnostic result transmitted from the diagnostic device, and transmit a determination result indicating the presence or absence of an abnormality, the statistics, and the received diagnostic result to the control device. Effect of the Invention

[0011] According to the present disclosure, a control device that controls the operation of a manufacturing equipment, an abnormality detection device, and a diagnostic device are provided, and by each device working together, it is possible to detect abnormalities in the manufacturing equipment and diagnose the condition of the manufacturing equipment without placing an excessive load on the control device. [Brief description of the drawings]

[0012] [Figure 1] 1 is a block diagram showing an example of the configuration of a molding machine system according to a first embodiment of the present invention. [Diagram 2] FIG. 1 is a schematic diagram showing a configuration example of a twin-screw kneading extruder according to the first embodiment. [Diagram 3] 1 is a block diagram showing an example of the configuration of an anomaly detection device according to a first embodiment of the present invention; [Figure 4] 1 is a block diagram showing an example of the configuration of a diagnostic device according to a first embodiment. [Diagram 5] FIG. 2 is a conceptual diagram illustrating an example of a record layout of a database according to the first embodiment. [Figure 6] 4 is a flowchart showing a processing procedure for detecting and diagnosing an abnormality in the twin-screw kneading extruder according to the first embodiment. [Figure 7]4 is a flowchart showing a processing procedure for detecting and diagnosing an abnormality in the twin-screw kneading extruder according to the first embodiment. [Figure 8] 10 is a flowchart showing a procedure for determining an abnormality. [Figure 9] 4 is a flowchart showing a procedure for abnormality diagnosis. [Figure 10] 4 is a flowchart showing a procedure for diagnosing an abnormality in the screw shaft 11. [Figure 11] This is a time-series data image showing the deviations of the two screw shafts. [Figure 12] 1 is a conceptual diagram showing feature amounts and anomaly detection results of a time-series data image. [Figure 13] 13 is a flowchart showing a processing procedure for life diagnosis. [Figure 14] FIG. 1 is a conceptual diagram showing an overview of a method for generating a learning model. [Figure 15] FIG. 1 is a conceptual diagram showing a learning model in a learning phase. [Figure 16] FIG. 1 is a conceptual diagram showing a learning model in a test phase. [Figure 17] 5 is a schematic diagram showing an example of a display of an abnormality determination result, etc. in the control device. FIG. [Figure 18] 11 is a schematic diagram showing an example of a display of an abnormality determination result, etc., on a terminal device. FIG. [Figure 19] 10 is a flowchart showing a feedback process procedure regarding a diagnosis result according to the second embodiment. [Figure 20] 11 is a flowchart showing a feedback process procedure regarding the state of the twin-screw kneading extruder according to the third embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] Specific examples of an anomaly detection system, a molding machine system, an anomaly detection device, an anomaly detection method, and a computer program according to the embodiments of the present invention will be described below with reference to the drawings. Note that the present invention is not limited to these examples, but is intended to include all modifications within the meaning and scope of the claims. In addition, at least some of the embodiments and modifications described below may be combined in any combination.

[0014] 1 is a block diagram showing an example of the configuration of a molding machine system according to the present embodiment 1. The molding machine system includes a twin-screw kneading extruder 1, a plurality of sensors 2, an abnormality detection device 3, a router 4, a diagnostic device 5, a database 6, and a user terminal device 7. The molding machine system includes the twin-screw kneading extruder 1 and the abnormality detection system, which is configured by the plurality of sensors 2, the abnormality detection device 3, the diagnostic device 5, and the database 6.

[0015] <Twin-screw kneading extruder 1> 2 is a schematic diagram showing a configuration example of the twin-screw kneading extruder 1 according to the present embodiment 1. The twin-screw kneading extruder 1 includes a cylinder 10 having a hopper 10a into which a resin raw material is charged, and two screw shafts 11. The two screw shafts 11 are arranged substantially parallel to each other while meshing with each other and rotatably inserted into holes in the cylinder 10, and transport the resin raw material charged into the hopper 10a in the extrusion direction (to the right in FIGS. 1 and 2) and melt and knead it. The screw shaft 11 is configured by combining and integrating a plurality of types of screw pieces into one screw shaft 11. For example, the screw shaft 11 is configured by combining forward flight pieces of a flight screw shape that transport the resin raw material in a forward direction, reverse flight pieces that transport the resin raw material in a reverse direction, kneading pieces that knead the resin raw material, etc., in an order and positions according to the properties of the resin raw material.

[0016] The twin-screw kneading extruder 1 also includes a motor 12 that outputs a driving force for rotating the screw shaft 11, a reducer 13 that reduces and transmits the driving force of the motor 12, and a control device 14. The screw shaft 11 is connected to the output shaft of the reducer 13. The screw shaft 11 is rotated by the driving force of the motor 12 that is reduced and transmitted by the reducer 13.

[0017] <Sensor 2> The sensor 2 detects physical quantities related to the operation of the twin-screw kneading extruder 1 or the molded product, and outputs time-series sensor value data indicating the detected physical quantities to the anomaly detection device 3. The physical quantities include temperature, position, speed, acceleration, current, voltage, pressure, time, image data, torque, force, strain, power consumption, weight, etc. These physical quantities can be measured using a thermometer, position sensor, speed sensor, acceleration sensor, ammeter, voltmeter, pressure gauge, timer, camera, torque sensor, wattmeter, weight scale, etc. More specifically, the sensor 2 that detects physical quantities related to the operation of the twin-screw kneading extruder 1 includes a vibration sensor such as an acceleration sensor that detects vibration of the reducer 13, a torque sensor that detects the axial torque applied to the screw shaft 11, a thermometer that detects the temperature of the screw shaft 11, a displacement sensor that detects the displacement of the center of rotation of the screw shaft 11, an optical sensor that detects the dimensions of the molded product or strand, an imaging device that images the molded product or strand, a resin pressure sensor that detects the resin pressure, etc. The sensor 2 for detecting a physical quantity related to the molded product is an optical measuring instrument for detecting the dimensions, chromaticity, brightness, etc. of the molded product, an imaging device, a weighing scale for detecting the weight of the molded product, etc.

[0018] <Control device 14> The control device 14 is a computer that controls the operation of the twin-screw kneading extruder 1, and includes a transmitter / receiver unit (not shown) that transmits and receives information to and from the abnormality detection device 3, and a display unit. Specifically, the control device 14 transmits operation data indicating the operation state of the twin-screw kneading extruder 1 to the abnormality detection device 3. The operation data includes, for example, the feeder supply amount (supply amount of resin raw material), the rotation speed of the screw shaft 11, the extrusion amount, the cylinder temperature, the resin pressure, the motor current, and the like. The control device 14 receives the judgment results (to be described later) indicating the presence or absence of an abnormality in the twin-screw kneading extruder 1 transmitted from the abnormality detection device 3, various statistics of the sensor value data, and diagnosis results related to abnormalities, lifespan, etc. of the twin-screw kneading extruder 1, and displays the received judgment results, values ​​and graphs of the statistics, and diagnosis results. In addition, the control device 14 monitors abnormalities in the twin-screw kneading extruder 1 using the judgment results, etc., outputs a warning as necessary, and executes processing to stop the operation of the twin-screw kneading extruder 1.

[0019] <Anomaly detection device 3> 3 is a block diagram showing an example of the configuration of the abnormality detection device 3 according to the present embodiment 1. The abnormality detection device 3 is an edge computer, and includes a processing unit 31, a storage unit 32, a communication unit 33, and an acquisition unit 34, and the storage unit 32, the communication unit 33, and the acquisition unit 34 are connected to the processing unit 31. The edge computer constituting the abnormality detection device 3 is a computer with lower hardware specifications than the diagnostic device 5 described below, and executes simple abnormality detection processing for the twin-screw kneading extruder 1.

[0020] The processing unit 31 has an arithmetic processing circuit such as a CPU (Central Processing Unit), a multi-core CPU, an ASIC (Application Specific Integrated Circuit), and an FPGA (Field-Programmable Gate Array), an internal storage device such as a ROM (Read Only Memory) and a RAM (Random Access Memory), an I / O terminal, etc. The processing unit 31 functions as the anomaly detection device 3 according to the first embodiment by executing a computer program P1 stored in a storage unit 32 described later. Note that each functional unit of the anomaly detection device 3 may be realized by software, or a part or all of it may be realized by hardware.

[0021] The storage unit 32 is a non-volatile memory such as a hard disk, an EEPROM (Electrically Erasable Programmable ROM), a flash memory, etc. The storage unit 32 stores a computer program P1 for causing a computer to execute the anomaly detection method according to the first embodiment.

[0022] The computer program P1 according to the first embodiment may be recorded in a computer-readable manner on the recording medium 30. The storage unit 32 stores the computer program P1 read from the recording medium 30 by a reading device (not shown). The recording medium 30 is a semiconductor memory such as a flash memory. The recording medium 30 may be an optical disk such as a CD (Compact Disc)-ROM, a DVD (Digital Versatile Disc)-ROM, or a BD (Blu-ray (registered trademark) Disc). The recording medium 30 may be a magnetic disk such as a flexible disk or a hard disk, a magnetic optical disk, or the like. The computer program P1 according to the first embodiment may be downloaded from an external server (not shown) connected to a communication network (not shown) and stored in the storage unit 32.

[0023] The communication unit 33 is a communication circuit that transmits and receives information according to a predetermined communication protocol such as Ethernet (registered trademark). The communication unit 33 is connected to the control device 14 via a first communication network such as a LAN, and the processing unit 31 can transmit and receive various information between the control device 14 and the communication unit 33. A router 4 is connected to the first network, and the communication unit 33 is connected to a diagnostic device 5 on the cloud, which is a second communication network, via the router 4. The processing unit 31 can transmit and receive various information to and from the diagnostic device 5 via the communication unit 33 and the router 4.

[0024] The acquiring unit 34 is an input interface for receiving signals. The sensor 2 is connected to the acquiring unit 34, and sensor value data, which is time-series data output from the sensor 2, is input to the acquiring unit 34.

[0025] <Diagnostic device 5 and database 6> 4 is a block diagram showing an example of the configuration of the diagnostic device 5 according to embodiment 1. The diagnostic device 5 is a computer, and includes a diagnostic processing unit 51, a storage unit 52, and a communication unit 53. The storage unit 52 and the communication unit 53 are connected to the diagnostic processing unit 51.

[0026] The diagnostic processing unit 51 has an arithmetic processing circuit such as a CPU, a multi-core CPU, a GPU (Graphics Processing Unit), a GPGPU (General-purpose computing on graphics processing units), a TPU (Tensor Processing Unit), an ASIC, an FPGA, an NPU (Neural Processing Unit), an internal storage device such as a ROM and a RAM, an I / O terminal, etc. The diagnostic processing unit 51 functions as the diagnostic device 5 according to the present embodiment 1 by executing a computer program P2 stored in a storage unit 52 described later. Each functional unit of the diagnostic device 5 may be realized by software, or a part or all of the functional units may be realized by hardware.

[0027] The storage unit 52 is a non-volatile memory such as a hard disk, an EEPROM, a flash memory, etc. The storage unit 52 stores a computer program P2 for causing a computer to execute a process for diagnosing the state of the twin-screw kneading extruder 1, such as an abnormality or lifespan of the twin-screw kneading extruder 1, and one or more learning models 54.

[0028] The computer program P2 and the learning model 54 may be recorded in a computer-readable manner on the recording medium 50. The storage unit 52 stores the computer program P2 and the learning model 54 read from the recording medium 50 by a reading device (not shown). The recording medium 50 is a semiconductor memory such as a flash memory. The recording medium 50 may also be an optical disk such as a CD (Compact Disc)-ROM, a DVD (Digital Versatile Disc)-ROM, or a BD (Blu-ray (registered trademark) Disc). The recording medium 50 may also be a magnetic disk such as a flexible disk or a hard disk, a magnetic optical disk, or the like. Furthermore, the computer program P2 and the learning model 54 may be downloaded from an external server (not shown) connected to a communication network (not shown) and stored in the storage unit 52.

[0029] The communication unit 53 is a communication circuit that transmits and receives information according to a predetermined communication protocol such as Ethernet (registered trademark). The communication unit 53 is connected to the anomaly detection device 3, the database 6, and the terminal device 7 via a second communication network, and the processing unit 31 can transmit and receive various information between the anomaly detection device 3, the database 6, and the terminal device 7 via the communication unit 53.

[0030] 5 is a conceptual diagram showing an example of a record layout of the database 6 according to the embodiment 1. The database 6 includes a hard disk and a DBMS (Database Management System), and stores various information related to the operation of the twin-screw kneading extruder 1. For example, a table of the database 6 has a "No." (record number) column, an "operation date and time" column, an "operation data" column, a "screw configuration" column, a "resin type" column, a "sensor value" column, a "status" column, a "quality evaluation" column, and a "lifespan" column.

[0031] The "operation date and time" column stores information indicating the year, month, date and time when the various data stored as records was obtained. The "operation data" column stores information indicating the operation state of the twin-screw kneading extruder 1, such as the feeder supply amount (supply amount of resin raw material), the rotation speed of the screw shaft 11, the extrusion amount, the cylinder temperature, the resin pressure, the motor current, and the like. The “screw configuration” column stores information regarding the type, arrangement, or combination of various screw pieces that configure the screw shaft 11 . The “resin type” column stores information indicating the type of resin raw material supplied to the twin-screw kneading extruder 1. The "sensor value" column stores sensor value data. The "sensor value" column may store numerical data as is, or may store the sensor value data, which is time-series data, as an image represented as a time-series data image, as described later. The "Status" column stores information indicating the state of the twin-screw kneading extruder 1 when the operation control indicated by the operation data is performed and the sensor value data is obtained. For example, information indicating normality, information indicating an abnormal state in which the screw shaft 11 is worn, information indicating an abnormal state in which a resin raw material not suitable for the screw shaft 11 is used, information indicating an overload error, etc. are stored. The "quality evaluation" column stores information indicating the quality evaluation of the molded product produced by the twin-screw kneading extruder 1 when the operation control indicated by the above operation data is performed and the above sensor value data is obtained. For example, information indicating whether the molded product is normal or not, dimensional abnormality, etc. is stored. The "Life" column stores information indicating the life of the twin-screw kneading extruder 1 used to obtain the above information. The life may be the total product life or the remaining life from the time the above data is obtained.

[0032] The various data stored in the database 6 are obtained, for example, by trial operating the twin-screw kneading extruder 1, checking the operating state of the twin-screw kneading extruder 1, and evaluating the molded product. The operating state of the twin-screw kneading extruder 1 may also be simulated by a simulator to prepare the various data. In the first embodiment, the database 6 stores the data obtained by trial or experimentally operating the twin-screw kneading extruder 1, or the data obtained by simulation.

[0033] <Example of anomaly> Abnormalities that can be detected or diagnosed by the abnormality detection device 3 and the diagnosis device 5 include, for example, abnormal vibration of the reducer 13, overload, dimensional abnormalities of the molded product, scratches, cracks, wear, corrosion on the screw shaft 11, performance deterioration or oil abnormalities due to wear of the reducer 13 and its various parts, inefficient operating conditions (increased energy consumption, etc.), operating conditions that lead to quality abnormalities (unreasonable start-up, etc.), strand abnormalities (defects in dimensions, foreign objects, color, coils (twists), etc.), resin viscosity (quality) abnormalities, etc. In the present embodiment 1, the abnormality detection device 3 detects abnormal vibration, overload, dimensional abnormality of the molded product, strand abnormality, resin viscosity abnormality, etc. of the reducer 13 based on the sensor value data. Specifically, the abnormality detection device 3 calculates statistics such as the average, variance, and root mean square of the sensor values, and detects abnormal vibration, overload, dimensional abnormality of the molded product, strand abnormality, resin viscosity abnormality, etc. of the reducer 13 through low-load processing based on the calculated statistics. On the other hand, the diagnostic device 5 diagnoses the presence or absence of abnormalities such as scratches, cracks, wear, and corrosion on the screw shaft 11, performance deterioration or oil abnormalities in the reducer 13, an inefficient operating state, or an operating state that leads to quality abnormalities, using the operation data and sensor value data acquired from the control device 14 via the abnormality detection device 3, as well as past sensor value data stored in the database 6. The diagnostic device 5 also performs processing to identify faulty parts of the reducer 13, such as bearings, gears, etc. The division of roles between the anomaly detection process performed by the anomaly detection device 3 and the diagnosis process performed by the diagnosis device 5 is merely an example, and the anomaly detection device 3 may be configured to execute the process as long as it is capable of performing the process.

[0034] <Overall flow of anomaly detection process> 6 and 7 are flowcharts showing a processing procedure for abnormality detection and diagnosis of the twin-screw kneading extruder 1 according to embodiment 1. The control device 14 transmits operation data indicating the state of operation control of the twin-screw kneading extruder 1 to the abnormality detection device 3 (step S11).

[0035] The processing unit 31 of the abnormality detection device 3 receives the operation data transmitted from the control device 14 at the communication unit 33 (step S12).

[0036] The processing unit 31 acquires the time-series sensor value data output from the sensor 2 (step S13). Then, the processing unit 31 calculates statistics such as the average, variance, and root-mean-square of the sensor value based on the sensor value data (step S14), and executes an abnormality determination process for the twin-screw kneading extruder 1 based on the calculated statistics (step S15).

[0037] Next, the processing unit 31 transmits the determination result indicating the presence or absence of an abnormality in the twin-screw kneading extruder 1 and the statistics calculated in step S14 to the control device 14 via the communication unit 33 (step S16).

[0038] The control device 14 receives the judgment result and the statistics transmitted from the abnormality detection device 3 (step S17), and monitors the operation of the twin-screw kneading extruder 1 based on the received judgment result or the statistics (step S18). For example, when the judgment result indicates a predetermined abnormality, the control device 14 stops the operation control of the twin-screw kneading extruder 1.

[0039] Next, the control device 14 causes the display unit to display the received determination results, statistics, and the like (step S19).

[0040] Next, a description will be given of the process of the anomaly detection device 3 that has transmitted the determination result and the statistics. After completing the process of step S16, the processing unit 31 of the anomaly detection device 3 transmits the operation data received in step S12 and the sensor value data acquired in step S13 to the diagnosis device 5 via the communication unit 33 (step S20).

[0041] The diagnostic device 5 receives the operation data and the sensor value data transmitted from the abnormality detection device 3 (step S21), and the diagnostic processing unit 51 executes an abnormality diagnosis process for the twin-screw kneading extruder 1 using the received operation data and the sensor value data, as well as past sensor value data stored in the database 6 (step S22). The diagnostic processing unit 51 also executes a life diagnosis process for the twin-screw kneading extruder 1 (step S23). The diagnostic processing unit 51 evaluates material fatigue from, for example, the waveform of the shaft torque, and calculates the remaining life.

[0042] After completing the processes of steps S22 and S23, the diagnosis processing unit 51 transmits the diagnosis result to the abnormality detection device 3 through the communication unit 53 (step S24).

[0043] The processing unit 31 of the abnormality detection device 3 receives the diagnosis result transmitted from the diagnosis device 5 (step S25), and transmits the received diagnosis result to the control device 14 via the communication unit 33 (step S26).

[0044] The control device 14 receives the diagnosis result transmitted from the abnormality detection device 3 (step S27), and displays the received diagnosis result on the display unit (step S28).

[0045] In response to a request from the terminal device 7, the anomaly detection device 3 can transmit the anomaly determination result by the processing unit 31, the calculated statistics of the sensor value data, the diagnosis result by the diagnosis processing unit 51, and the like to the terminal device 7. The terminal device 7 displays the received anomaly determination result, statistics, and diagnosis result.

[0046] <Abnormality detection process by abnormality detection device 3> 8 is a flowchart showing the procedure for determining an abnormality. Since the abnormality detection device 3 cannot store a large amount of sensor value data, it executes a process for determining whether or not there is an abnormality in the twin-screw kneading extruder 1 using the operation data and the sensor value data of the day.

[0047] The processing unit 31 specifies a threshold value of a judgment criterion for judging the presence or absence of an abnormality in the twin-screw kneading extruder 1 based on the operation data received in step S12 (step S51). For example, since the shaft torque and the rotation speed of the screw shaft 11 are inversely proportional to each other, the threshold value for detecting a torque overload depends on the rotation speed of the screw shaft 11, which is one of the operation data. In this way, the processing unit 31 can specify a threshold value for detecting an abnormality based on the operation data. Specifically, the anomaly detection device 3 stores a table in which operation data, types of anomalies, and threshold values ​​serving as judgment criteria are associated with each other in the storage unit 32. The processing unit 31 can identify threshold values ​​serving as judgment criteria for various anomalies according to the operation data by referring to the table using the operation data and reading out the threshold values.

[0048] Next, the processing unit 31 determines whether or not there is abnormal vibration in the reducer 13 based on the statistics of the sensor value data indicating the vibration of the reducer 13 and the threshold value specified in step S51 (step S52). Note that the processing unit 31 may be configured to convert the sensor value data indicating the vibration of the reducer 13 into time frequency components and determine whether or not there is abnormality in the reducer 13 based on the data of the time frequency components.

[0049] Furthermore, the processing unit 31 determines the presence or absence of an overload based on statistics of the sensor value data indicating the shaft torque, the temperature of the screw shaft 11, and the displacement of the center of rotation of the screw shaft 11, and the threshold value identified in step S51 (step S53).

[0050] Furthermore, the processing unit 31 judges whether or not the molded product has a dimensional abnormality based on the statistics indicating the dimensions of the molded product and the threshold value specified in step S51 (step S54).

[0051] The processing unit 31 judges the presence or absence of abnormality in the strand based on the statistical amount indicating the difference between the image obtained by capturing the strand and a normal image, and the threshold value specified in step S51 (step S55). The processing unit 31 can measure the strand diameter based on the pixel size (number of pixels) of the image of the strand included in the captured image. The processing unit 31 can also judge the presence or absence of a foreign object by judging the color of the image portion included in the captured image.

[0052] The processing unit 31 determines whether or not there is an abnormality in the resin viscosity based on the time average of the resin pressure detected by the sensor 2, for example, a resin pressure sensor, and the threshold value identified in step S51 (step S56), and ends the abnormality determination process.

[0053] <Abnormality diagnosis process by diagnosis device 5> 9 is a flowchart showing the procedure of the abnormality diagnosis process. The diagnosis processor 51 performs abnormality diagnosis of the screw shaft 11 by inputting sensor value data indicating the displacement of the rotation center of the screw shaft 11 or time-frequency data obtained by time-frequency analysis of the sensor value data to the learning model 54 (step S61). The learning model 54 used in step S61 is a convolutional neural network model that, when a time-series data image, which is an image of sensor value data, is input, extracts features of the time-series data image and outputs the extracted feature amount. The learning model 54 is a model having a feature extraction layer of a CNN (Convolutional Neural Network), for example, a one-class classification model. The diagnosis processing unit 51 can diagnose the presence or absence of an abnormality in the screw shaft 11 by comparing a feature amount obtained from the sensor value data, which is associated with the same or similar operation data as the operation data received in step S12 and is obtained when the twin-screw kneading extruder 1 is operating normally, with a feature amount of the diagnosis target. A method for generating the learning model 54 and a configuration thereof will be described in detail later.

[0054] In addition, the diagnostic processing unit 51 diagnoses the presence or absence of performance deterioration due to wear of the reducer 13 and each part thereof or oil abnormality by inputting vibration sensor data obtained from a sensor 2 installed in the reducer 13, such as a vibration sensor, or time-frequency data obtained by time-frequency analysis of the sensor value data into the learning model 54 (step S62). The learning model 54 used in step S62 is, for example, a support vector machine. The support vector machine is a classifier that learns time frequency components obtained by time frequency analysis of vibration sensor data related to a normal reducer 13 and time frequency components obtained by time frequency analysis of vibration sensor data related to an abnormal reducer 13, and classifies the data into a normal class related to a normal reducer 13 and an abnormal class related to an abnormal reducer 13. The frequency components to be analyzed may be specific frequency components, such as main frequency components obtained from the reducer 13 and fault frequency components that appear when a fault occurs. The diagnosis processing unit 51 identifies, for example, the main frequency components and fault frequency components obtained by time frequency analysis of the vibration sensor data. The diagnosis processing unit 51 then diagnoses whether the reducer 13 is normal or not by inputting the time frequency components of the vibration sensor data, such as the main frequency components and fault frequency components, into the support vector machine. The learning model 54 may be a classifier using a convolutional neural network model. For example, when vibration sensor data or time-frequency data obtained by time-frequency analysis of the sensor value data is input, the learning model 54 may learn to output data indicating an abnormality location and an abnormality level of the reducer 13. The abnormality location is, for example, a bearing, a gear, or the like of the reducer 13.

[0055] In addition, the diagnostic processing unit 51 diagnoses whether or not the system is in an inefficient operating state by inputting sensor value data indicating the axial torque, the displacement of the center of rotation of the screw shaft 11, vibration, etc. obtained from the sensor 2, or time-frequency data obtained by time-frequency analysis of the sensor value data into the learning model 54 (step S63). The learning model 54 used in step S63 is a convolutional neural network model that, when a time-series data image, which is described later and represents sensor value data as an image, is input, extracts features of the time-series data image and outputs the extracted feature amount. The learning model 54 is a model having a feature extraction layer of CNN, for example, a one-class classification model. The diagnostic processing unit 51 can diagnose whether or not the twin-screw kneading extruder 1 is in an inefficient operating state by comparing the characteristic quantities obtained from the sensor value data, which is associated with the same or similar operating data as the operating data received in step S12, and when the twin-screw kneading extruder 1 is in an efficient operating state, with the characteristic quantities of the object to be diagnosed.

[0056] In addition, the diagnostic processing unit 51 diagnoses whether or not the operating state is one that could lead to quality abnormalities by inputting sensor value data indicating the axial torque, the displacement of the center of rotation of the screw shaft 11, vibration, etc. obtained from the sensor 2, or time-frequency data obtained by time-frequency analysis of the sensor value data into the learning model 54 (step S64). The learning model 54 used in step S64 is a convolutional neural network model that, when a time-series data image, which is described later and represents sensor value data as an image, is input, extracts features of the time-series data image and outputs the extracted feature amount. The learning model 54 is a model having a feature extraction layer of CNN, for example, a one-class classification model. The diagnostic processing unit 51 can diagnose whether or not the twin-screw kneading extruder 1 is in an operating state that could lead to a quality abnormality by comparing the characteristic quantities obtained from the sensor value data, which is associated with the same or similar operating data as the operating data received in step S12 and is in a normal operating state when the twin-screw kneading extruder 1 is not in an operating state that could lead to a quality abnormality, with the characteristic quantities of the object to be diagnosed.

[0057] The diagnostic processing unit 51 may be configured to monitor the operation rate by monitoring the current flow data obtained from the control device 14 via the abnormality detection device 3 and calculating the operation rate for a certain period of time. The diagnostic processing unit 51 may be configured to diagnose the presence or absence of an abnormality in the twin-screw kneading extruder 1 based on the operation rate.

[0058] Below, screw shaft abnormality diagnosis will be described in detail as an example of a one-class classification model. 10 is a flowchart showing a procedure for diagnosing an abnormality in the screw shaft 11. The diagnosis processing unit 51 converts a plurality of sensor value data into one or a plurality of time series data images represented by an image (step S71), and when there are a plurality of converted time series data images, synthesizes the plurality of time series image data into one time series data image (step S72). For example, when the sensor value data is data indicating the displacement in two directions of each of the two screw shafts 11, the diagnosis processing unit 51 synthesizes a first time series data image representing the displacement in two directions of the rotation center axis of the first screw shaft 11 and a second time series data image representing the displacement in two directions of the rotation center axis of the second screw shaft 11.

[0059] FIG. 11 is a time series data image showing the displacement of the two screw shafts 11. The first time series data image shows the displacement of the first screw shaft 11. The first time series data image is a substantially square image, and the displacement amounts in the X-axis and Y-axis directions perpendicular to the rotation center of the screw shaft 11 are plotted as X-axis and Y-axis coordinate values. The first time series data image is a plot of the displacement for three rotations. The second time series data image can be created in the same manner. The synthesized time series data image is a substantially square image twice as large as the first time series data image (four times the area). The time series data image includes the information contained in the original images without changing the aspect ratio of the first and second time series data images. Specifically, the time series data image includes the first and second time series data images arranged side by side on the upper left and right in FIG. 11, and a blank image arranged on the lower side to make the time series data image a substantially square. The arrangement method of the first and second time series data images and the blank image is not particularly limited, and is not particularly limited as long as the first and second time series data images are included as they are.

[0060] Other sensor value data can be similarly converted into time-series data images and synthesized. In the example shown in Fig. 11, a total of eight sensor value data can be represented by an image, but when nine time-series data images of 3 x 3 are synthesized, 18 sensor value data can be represented by one time-series data image.

[0061] Next, the diagnosis processing unit 51 inputs the time-series data image synthesized in step S72 to the learning model 54, thereby calculating the feature amount of the time-series data image (step S73).

[0062] In addition, the diagnostic processing unit 51 reads out from the database 6 sensor value data that is associated with operating data that is identical or similar to the operating data received in step S12 and that is obtained when the twin-screw kneading extruder 1 is operating normally (step S74), converts the data into a time series data image in the same manner (step S75), synthesizes the data (step S76), and inputs the synthesized time series data image into the learning model 54 to calculate the feature quantities of the time series data image during normal operation (step S77).

[0063] Next, the diagnostic processing unit 51 calculates an outlier score for the feature of the time series data image calculated in step S73 (step S78). The outlier score is a numerical value obtained by evaluating the degree of outlier of the feature of the time series data image calculated in step S77 relative to the feature of the time series data image obtained during normal operation of the twin-screw kneading extruder 1 (hereinafter referred to as sample feature). The outlier score is, for example, LOF (Local Outlier Factor). LOF is the local density ld(P) of the feature to be detected as an anomaly and the local density average of the neighborhood group of the feature (the k nearest sample features to the feature).<ld(Q)> Compared to<ld(Q)> / ld(P). The greater the LOF is, the greater the likelihood of it being an outlier. Note that P represents the feature that is the target of anomaly detection, and Q represents the sample feature.

[0064] Then, the processing unit 31 judges whether the calculated outlier score is equal to or greater than a predetermined threshold value, thereby judging whether or not there is an abnormality in the twin-screw kneading extruder 1 (step S79). When the outlier score is the above-mentioned LOF, the threshold value is a value equal to or greater than 1. When the LOF is equal to or greater than the threshold value, the diagnosis processing unit 51 judges that there is an abnormality, and when the LOF is less than the threshold value, it judges that there is a normality.

[0065] The abnormality determination using the LOF described above is one example, and it may be determined whether or not the twin-screw kneading extruder 1 is abnormal by the k-nearest neighbor method or SVM. Also, it may be configured to determine whether or not the twin-screw kneading extruder 1 is abnormal by determining whether or not the feature amount of the abnormality detection target is far from the sample feature amount in normal state by the Hotelling method.

[0066] FIG. 12 is a conceptual diagram showing feature quantities and anomaly detection results of a time-series data image. FIG. 12 shows high-dimensional feature quantities of a time-series data image that have been dimension-reduced to two-dimensional feature quantities and plotted on a two-dimensional plane. The horizontal and vertical axes of the graph shown in FIG. 12 indicate the first and second feature quantities of the dimension-reduced time-series data image. The dimension reduction of the feature quantities can be performed by a dimension reduction algorithm such as t-SNE. As shown in FIG. 12, the learning model 54 is trained so that the local density of the normal feature quantity output from the learning model 54 is high. The dashed circle shows an image of the threshold value. When the statistical distance between the feature amount of the time series data image that is the target of abnormality detection (e.g., a star-shaped hexagonal plot) and the feature amount group of the time series data image in a normal state is short (the local density is relatively high) and the LOF value is small, the twin-screw kneading extruder 1 is estimated to be in a normal state. When the statistical distance between the feature amount of the time series data image that is the target of abnormality detection (e.g., an X-mark plot) and the feature amount group of the time series data image in a normal state is long (the local density is relatively low) and the LOF value is large, the twin-screw kneading extruder 1 is estimated to be in an abnormal state. By diagnosing anomalies using the displacement of the rotational axis of the screw shaft 11, it is possible to diagnose the presence or absence of anomalies such as wear of the screw shaft 11.

[0067] Although the above description has been focused on detection of an abnormality based on the displacement of the screw shaft 11, the present invention is not limited to this. For example, a frequency spectrum can be calculated based on sensor value data indicating the vibration of the reducer 13, a time-series data image representing the frequency spectrum can be generated, and the generated time-series data image can be used to diagnose vibration abnormalities in the reducer 13. In addition, a time-series data image is generated in which the sensor value data indicating the shaft torque applied to the screw shaft 11 is represented as an image, and the presence or absence of an overload can be diagnosed using the generated time-series data image.

[0068] 13 is a flowchart showing the processing procedure of the life diagnosis. The diagnosis processing unit 51 converts a plurality of sensor value data into one or a plurality of time series data images represented by an image (step S91), and when there are a plurality of converted time series data images, synthesizes the plurality of time series image data into one time series data image (step S92). The sensor value data is, for example, data indicating the shaft torque or the displacement of the rotation center of the screw shaft 11. Next, the diagnosis processing unit 51 inputs the time series data image synthesized in step S92 into the learning model 54, thereby calculating the feature amount of the time series data image (step S93).

[0069] In addition, the diagnostic processing unit 51 reads out from the database 6 specific sensor value data, for example sensor value data of a twin-screw kneading extruder 1 that is associated with operating data that is identical or similar to the operating data received in step S12 and has a normal lifespan (step S94), converts it into a time series data image in the same manner (step S95), synthesizes it (step S96), and inputs the synthesized time series data image into the learning model 54 to calculate the feature quantities of the time series data image related to the twin-screw kneading extruder 1 having a normal lifespan (step S97).

[0070] Next, the diagnostic processing unit 51 calculates an outlier score of the feature amount of the time-series data image calculated in step S97 (step S98). Then, the processing unit 31 judges whether the calculated outlier score is equal to or greater than a predetermined threshold value, thereby judging whether the life of the twin-screw kneading extruder 1 to be diagnosed is normal or not (step S99). When the outlier score is the above-mentioned LOF, the threshold value is a value of 1 or more. When the LOF is equal to or greater than the threshold value, the diagnostic processing unit 51 judges that the life is short, and when the LOF is less than the threshold value, it judges that the life is normal.

[0071] Note that, although an example of diagnosing whether the lifespan is normal or not has been described here, it is possible to determine whether the twin-screw kneading extruder 1 has a specific lifespan by comparing the characteristic quantities of the sensor value data obtained from the twin-screw kneading extruder 1 having a specific lifespan with the characteristic quantities of the current sensor value data.

[0072] <Learning model generation method> A method for generating the learning model 54 used in the diagnostic process by the diagnostic device 5 will be described. Fig. 14 is a conceptual diagram showing an overview of a method for generating the learning model 54. A method for generating the learning model 54 for determining whether the operation of the twin-screw kneading extruder 1 is normal or not will be described. An example in which the diagnosis processing unit 51 of the diagnosis device 5 trains the learning model 54 by machine learning will be described below.

[0073] First, as a learning data set required to generate a one-class classification model, a plurality of time-series data images detected and obtained when the twin-screw kneading extruder 1 is operating normally, and a plurality of arbitrary reference images A, B, and C unrelated to the time-series data images are prepared. The reference images A, B, and C are image data of a plurality of classes selected from a learning data set such as Image Net. In the example shown in FIG. 14, reference images A, B, and C with labels 0, 1, and 2 are prepared.

[0074] Then, the learning model 54, which has a feature extraction layer consisting of a plurality of convolution layers and pooling layers that extract image features, is trained by machine learning on the time-series data image during normal operation and the reference images A, B, and C. Specifically, the diagnosis processing unit 51 trains the learning model 54 so as to output features that can distinguish the features of each image, that is, features that have a high local density of the features of the time-series data image and are highly distinguishable from the features of the reference images A, B, and C, as shown in the right diagram of FIG. The method for generating a one-class classification model will be described in detail below.

[0075] 15 is a conceptual diagram showing a learning model 54 in the learning phase. First, a first neural network (reference network) 54a and a second neural network (secondary network) 54a are prepared.

[0076] The first neural network 54a is a CNN having an input layer, a feature extraction layer, and a classification layer. The feature extraction layer has a repetitive structure of multiple convolution layers and pooling layers. The classification layer has, for example, one or more fully connected layers. The first neural network 54a is pre-trained using a training dataset such as Image Net. The second neural network 54a has the same network configuration as the first neural network 54a, and the various parameters (weighting coefficients) that characterize the feature extraction layer and classification layer are also the same. Hereinafter, in the learning phase of the first and second neural networks 54a, 54a, the various parameters of the feature extraction layer and classification layer share the same values.

[0077] Then, the diagnostic processing unit 51 inputs the reference image A to the first neural network 54a and calculates a descriptive loss, which is a loss function. Meanwhile, the diagnostic processing unit 51 inputs a normal time-series data image to the second neural network 54a and calculates a compactness loss, which is a loss function. The descriptive loss is a loss function that is generally used in learning a classifier, and is, for example, a cross-entropy error. The compact loss is expressed by the following formulas (1), (2), and (3). The compact loss is a value that corresponds to the variance of the output of the second neural network 54a within the batch, that is, in the learning data set.

[0078]

number

[0079] Then, the diagnosis processing unit 51 calculates a total loss based on the descriptive loss and the compact loss. The total loss is expressed by, for example, the following formula (4).

[0080]

number

[0081] The diagnostic processing unit 51 optimizes various parameters of the first and second neural networks 54a, 54a using a backpropagation method or the like so as to reduce the total loss represented by the above formula (4), thereby training the first and second neural networks 54a, 54a. When training the networks, it is advisable to fix various parameters in the front stages and adjust parameters in multiple layers in the rear stages.

[0082] The features output from the feature extraction layers of the first and second neural networks 54a, 54a trained in this manner have a high local density with the features of the time-series data images obtained when the twin-screw kneading extruder 1 is normal, and a low local density with the features of other arbitrary images, as shown in FIG.

[0083] Fig. 16 is a conceptual diagram showing the learning model 54 in the test phase. As shown in Fig. 16, the learning model 54 according to the first embodiment can be configured using the input layer and feature extraction layer of the second neural network 54a trained as described above. Therefore, the classification layer shown in Fig. 15 is not an essential component of the learning model 54 according to the first embodiment. However, the second neural network 54a shown in Fig. 15 can be used as the learning model 54 as it is, and the feature amount output from the feature extraction layer can be used. Note that, although two learning models 54 are illustrated in FIG. 16, this is a conceptual illustration of a state in which a time series data image to be detected and a normal time series data image as a sample are input, and does not indicate the existence of two models.

[0084] In the test phase, the diagnostic processing unit 51 inputs a time series data image obtained under normal conditions as sample data to the learning model 54, thereby outputting the feature quantities of the time series data image under normal conditions. On the other hand, the diagnostic processing unit 51 inputs a time series data image of an abnormality detection target to the learning model 54, thereby outputting the feature quantities of the time series data image. Then, the diagnostic processing unit 51 calculates an outlier score of the feature quantity of the abnormality detection target with respect to the feature quantities of the sample data, and compares the calculated outlier score with a threshold value to determine whether or not the time series data image of the abnormality detection target is abnormal, that is, whether or not the twin-screw kneading extruder 1 is abnormal.

[0085] Note that FIG. 16 shows an example in which sample data is input into the learning model 54 and the features of the sample data are calculated. However, it is also possible to store features of multiple sample data calculated in advance in the memory unit 32, and in the anomaly detection process, to calculate the outlier score using the features of the sample data stored in the memory unit 32.

[0086] In addition, the learning model 54 for diagnosing whether or not there is an abnormality in the twin-screw kneading extruder 1 has been described, but by training the learning model 54 using sensor value data obtained from a twin-screw kneading extruder 1 having a normal life, it is possible to generate a learning model 54 for diagnosing whether or not the twin-screw kneading extruder 1 has a normal life.

[0087] In the above description, an example of classifying normal and abnormal is described, but the learning model 54, which is a plurality of one-class classification models, may be used to discriminate time series image data at typical abnormal times from time series data images at other abnormal times. For example, the anomaly detection device 3 includes a first learning model 54 that has learned a time series data image at normal times, as in the above-described first embodiment. The anomaly detection device 3 also includes a second learning model 54 that has learned a time series data image at normal times and a time series data image at a first abnormal time. The anomaly detection device 3 can discriminate between a time series data image at normal times, a time series data image at a first abnormal time, and time series data images at other abnormal times by using the first learning model 54 and the second learning model 54. In the same manner, the anomaly detection device 3 can be configured to be able to discriminate between two or more time series data images at abnormal times by including three or more learning models 54.

[0088] In addition, although the screw shaft abnormality diagnosis has been described in detail, a learning model 54 that diagnoses whether or not an inefficient operating state is occurring in steps S63 and S64, and whether or not an operating state that may lead to quality abnormalities is occurring, can also be constructed and generated in the same manner.

[0089] Furthermore, although a one-class classification model has been described as an example of the learning model 54, it is also possible to use general neural networks such as CNN, U-Net, RNN (Recurrent Neural Network), other SVMs (Support Vector Machines), Bayesian networks, regression trees, etc. For example, when normal sensor value data and various abnormal sensor value data are accumulated, the state of the twin-screw kneading extruder 1 may be used as training data, a learning data set may be created in which the sensor value data or time-series data image is labeled, and a learning model 54 composed of a CNN or the like may be trained. The state of the twin-screw kneading extruder 1 includes a normal state indicating that the twin-screw kneading extruder 1 is normal, an abnormal state in which the screw shaft 11 is worn, an abnormal state in which a resin raw material that is not suitable for the screw shaft 11 is used, an overload abnormal state in which an overload occurs, an abnormal vibration of the reducer 13, an abnormal state in which the life is short, an abnormal state in which the dimensions of the molded product are abnormal, and the like. The learning model 54 has an input layer, an intermediate layer, and an output layer. The intermediate layer has a plurality of convolution layers and a pooling layer that extract image features. The output layer has a plurality of nodes corresponding to a plurality of states of the twin-screw kneading extruder 1, and outputs the confidence level of the state. When sensor value data or a time-series data image related to the sensor value data is input, the learning model 54 optimizes the weight coefficients of the intermediate layer so that the state of the twin-screw kneading extruder 1 output from the learning model 54 approaches the state indicated by the teacher data. The weight coefficients are, for example, weights (coupling coefficients) between neurons. The method of optimizing the parameters is not particularly limited, but various parameters are optimized using, for example, the steepest descent method, the error backpropagation method, or the like. According to the learning model 54 generated in this manner, the state of the twin-screw kneading extruder 1 can be diagnosed by inputting the time-series data image of the sensor value data obtained from the twin-screw kneading extruder 1 to be diagnosed to the learning model 54. For example, the diagnosis processing unit 51 determines that the twin-screw kneading extruder 1 is in a state corresponding to the node that outputs the highest certainty.

[0090] The learning model 54 may be configured to output more optimal molding conditions or adjustment amounts of the molding conditions when a time-series data image is input. The diagnosis device 5 transmits the molding conditions output from the learning model 54 to the anomaly detection device 3, and the anomaly detection device 3 transmits the received molding conditions to the control device 14.

[0091] <Display process of diagnosis and judgment results> An example of displaying the determination results and statistics in step S19 will be described. FIG. 17 is a schematic diagram showing an example of display of an abnormality judgment result, etc. in the control device 14. The judgment result, etc. display screen 141 displayed on the display unit has a graph display unit 141a, a warning display unit 141b, an AI diagnosis display unit 141c, and a processed value display unit 141d. The graph display unit 141a displays, for example, a graph of the time change in the statistical quantities of shaft torque, vibration, and displacement of the screw shaft 11. The warning display unit 141b displays the received judgment result indicating the presence or absence of an abnormality in the twin-screw kneading extruder 1, and displays the contents of the warning if an abnormality is detected. The AI ​​diagnosis display unit 141c displays the diagnosis result by the diagnostic device 5 described below. The processed value display unit 141d displays the value of the received statistical quantity.

[0092] An example of how the diagnosis result is displayed in step S28 will be described. FIG. 18 is a schematic diagram showing an example of display of an abnormality judgment result, etc. in the terminal device 7. The judgment result display screen 71 has a graph display section 71a, a warning history display section 71b, an AI diagnosis history display section 71c, a processing value display section 71d, and a data selection section 71e. The data selection section 71e accepts the selection of data to be displayed. For example, the data selection section 71e accepts the selection of the operation date and time of the twin-screw kneading extruder 1, the type of statistics to be displayed, and the diagnosis item. When a plurality of twin-screw kneading extruders 1 are connected to the first network, the data selection section 71e may be configured to accept the selection of the twin-screw kneading extruder 1. The terminal device 7 transmits the operation date and time selected by the data selection section 71e, the identification information of the twin-screw kneading extruder 1, etc. to the abnormality detection device 3, and requests the history of abnormality judgment results, the statistics of the sensor value data, and the history of diagnosis results for the selected twin-screw kneading extruder 1 and the operation date and time. The terminal device 7 receives these pieces of information transmitted from the diagnosis device 5 in response to the request, and displays the received information. The graph display section 71a displays the time change of the statistical quantity of the sensor value data in a graph. The warning history display section 71b displays the history of the received judgment results indicating the presence or absence of an abnormality in the twin-screw kneading extruder 1. The AI ​​diagnosis history display section 71c displays the history of the diagnosis results related to the abnormality or lifespan of the twin-screw kneading extruder 1. The processed value display section 71d displays the value of the received statistical quantity.

[0093] <Functions and Effects of the Molding Machine System According to the First Embodiment> As described above, the molding machine system according to the present embodiment 1 is equipped with a control device 14 that controls the operation of the twin-screw kneading extruder 1, an abnormality detection device 3, and a diagnostic device 5, and by each device working together, it is possible to detect abnormalities in the twin-screw kneading extruder 1 and diagnose detailed conditions such as abnormalities and lifespan of the twin-screw kneading extruder 1 without placing an excessive load on the control device 14.

[0094] Furthermore, the abnormality detection device 3 can easily detect an abnormality in the twin-screw kneading extruder 1 by using the sensor value data and the operation data, and transmit the abnormality determination result to the control device 14. The control device 14 can receive the abnormality determination result of the twin-screw kneading extruder 1 simply by transmitting the operation data to the abnormality detection device 3, and can monitor the operation of the twin-screw kneading extruder 1 based on the abnormality determination result.

[0095] Furthermore, the anomaly detection device 3 can calculate statistics of the sensor value data and transmit them to the control device 14, and can display a graph of the statistics and the values ​​of the statistics on the display unit.

[0096] Furthermore, the abnormality detection device 3 can detect abnormal vibrations in the reducer 13, overload on the screw shaft 11, and dimensional abnormalities in the molded product, and transmit such abnormalities to the control device .

[0097] Furthermore, the diagnostic device 5 can diagnose in detail the abnormality and lifespan of the twin-screw kneading extruder 1 based on the current operation data and sensor value data of the twin-screw kneading extruder 1 and the past sensor value data accumulated in the database 6. The diagnostic device 5 can perform state diagnosis of the twin-screw kneading extruder 1 that is impossible with the abnormality detection device 3. The abnormality detection device 3 can obtain a diagnosis result by transmitting the current operation data and sensor value data of the twin-screw kneading extruder 1 to the diagnosis device 5 and requesting a diagnosis, and can transmit the obtained diagnosis result to the control device 14. The control device 14 can receive the diagnosis result by the diagnosis device 5 and display the diagnosis result on the display unit, simply by transmitting the operation data to the abnormality detection device 3.

[0098] Furthermore, the diagnostic device 5 can diagnose the state of the twin-screw kneading extruder 1, such as an abnormality or end of life, by using the learning model 54.

[0099] Furthermore, by configuring the learning model 54 as a one-class classification model, the learning model 54 can be machine-trained using time series data images obtained when the twin-screw kneading extruder 1 is operating normally, without using time series data images of the twin-screw kneading extruder 1 during abnormal times.

[0100] Furthermore, by expressing the sensor value data output from the multiple sensors 2 as one time-series data image and calculating the feature amount of the time-series data image using the learning model 54, it is possible to easily obtain the feature amount accurately representing the state of the twin-screw kneading extruder 1. By using the feature amount obtained in this manner, it is possible to accurately diagnose the state of the twin-screw kneading extruder 1.

[0101] Furthermore, in the first embodiment, an example has been described in which the diagnostic device 5 trains the learning model 54 by machine learning, but the learning model 54 may be trained by machine learning using another external computer or server.

[0102] Furthermore, although this embodiment 1 has mainly described abnormality detection and diagnosis of the twin-screw kneading extruder 1, it may also be configured to perform abnormality detection and diagnosis of molding machines such as injection molding machines and film molding machines, and other manufacturing equipment.

[0103] (Modification) The diagnostic device 5 may be configured to execute a billing process for the diagnostic process using the learning model 54. For example, the diagnostic device 5 may calculate a diagnostic fee according to the number of twin-screw kneading extruders 1, the number of sensors 2, the type of the sensors 2, the amount of data to be diagnosed, the number of learning models 54 to be used, and the like, and store the calculated diagnostic fee, the user ID of the abnormality diagnosis system, the identification ID of the twin-screw kneading extruder 1, the date and time of use, the amount of data, and the diagnostic items in the storage unit 52 in association with each other.

[0104] (Embodiment 2) The molding machine system according to the second embodiment differs from the second embodiment in that it receives user feedback on the diagnosis result and updates the learning model 54 or the diagnosis criteria. Since the other configurations of the molding machine system are similar to those of the molding machine system according to the first embodiment, the same reference numerals are used for the similar parts and detailed description is omitted.

[0105] 19 is a flowchart showing a feedback process procedure for the diagnosis result according to the embodiment 2. The abnormality detection device 3 receives the appropriateness of the diagnosis result or the correct state of the twin-screw kneading extruder 1 via the control device 14 (step S211). The processing unit 31 of the abnormality detection device 3 transmits feedback information including the operation data and sensor value data on which the diagnosis result to be evaluated is obtained, and data indicating the correct state of the twin-screw kneading extruder 1, to the diagnosis device 5 (step S212). The above-mentioned contents of the feedback information are merely examples. For example, when the diagnosis ID, the operation data, the sensor value data, and the diagnosis result are stored in the database 6, the abnormality detection device 3 may transmit feedback information including the diagnosis ID attached to the received diagnosis result and the correct state of the twin-screw kneading extruder 1 to the diagnosis device 5. The abnormality detection device 3 may be configured to transmit feedback information including the diagnosis ID and the appropriateness of the diagnosis result to the diagnosis device 5.

[0106] The diagnostic device 5 receives the feedback information transmitted from the abnormality detection device 3 (step S213) and stores the received feedback information in the database 6 (step S214). That is, the operational data, the sensor values, and the correct state of the twin-screw kneading extruder 1 are stored. Then, the diagnostic processing device additionally trains the learning model 54 based on the feedback information, or updates the diagnostic criteria (step S215).

[0107] For example, if the diagnosis result of an abnormality is incorrect and the twin-screw kneading extruder 1 is normal, the sensor value data with the incorrect diagnosis result can be added to the learning dataset as normal data, and the learning model 54 can be further trained, thereby improving the accuracy of feature extraction by the learning model 54.

[0108] Also, the threshold value for diagnosing an abnormality of the twin-screw kneading extruder 1 may be changed based on the calculated score value. For example, if the diagnosis result of an abnormality is incorrect and the twin-screw kneading extruder 1 is normal, the diagnosis processing unit 51 increases the threshold value. If the diagnosis result of a normality is incorrect and the twin-screw kneading extruder 1 is abnormal, the diagnosis processing unit 51 decreases the threshold value.

[0109] The learning model 54 and the diagnostic criteria may be changed by receiving and accumulating multiple pieces of feedback information.

[0110] According to the molding machine system of the second embodiment, the diagnostic accuracy of the diagnostic device 5 can be further improved by feedback from the user.

[0111] (Embodiment 3) The molding machine system according to the third embodiment differs from the third embodiment in that operational data, sensor value data, and data indicating the state of the twin-screw kneading extruder 1 obtained on-site are accumulated in a database 6, and a learning model 54 is additionally trained. Since the other configurations of the molding machine system are similar to those of the molding machine system according to the first embodiment, similar parts are given the same reference numerals and detailed descriptions are omitted.

[0112] 20 is a flowchart showing a feedback process procedure regarding the state of the twin-screw kneading extruder 1 according to the third embodiment. The abnormality detection device 3 receives the state of the twin-screw kneading extruder 1 via the control device 14 (step S311). That is, the abnormality detection device 3 receives the current state of the twin-screw kneading extruder 1 evaluated by the user. Then, the processing unit 31 of the abnormality detection device 3 transmits information including the operation data, the sensor value data, and the state of the twin-screw kneading extruder 1 to the diagnosis device 5 (step S312). In addition, the abnormality detection device 3 may receive information such as the configuration of the screw shaft 11 of the twin-screw kneading extruder 1, the type of resin raw material, the quality evaluation of the molded product, and the estimated life span, and transmit this information to the diagnosis device 5.

[0113] The diagnostic device 5 receives information including the operation data, the sensor value data, the state of the twin-screw kneading extruder 1, and other information transmitted from the abnormality detection device 3 (step S313), and stores the received information in the database 6 (step S314). That is, the diagnostic processing unit 51 stores the operation data, the sensor value data, the state of the twin-screw kneading extruder 1, and other information. Then, the diagnostic processing device additionally trains the learning model 54 using a learning data set including the sensor value data based on the user evaluations accumulated in the database 6 (step S315).

[0114] According to the anomaly detection device 3 according to the third embodiment, the learning model 54 is additionally trained by reflecting the user's evaluation, so that the diagnostic accuracy of the diagnostic device 5 can be further improved.

[0115] A data set for supervised learning can be created based on the information thus transmitted from the anomaly detection device 3. As described in the first embodiment, the diagnosis device 5 may be configured to generate a learning model 54 that outputs information related to the state of the twin-screw kneading extruder 1, or to perform additional learning, when sensor value data or a time-series data image is input, using the data set for supervised learning. [Explanation of symbols]

[0116] 1 Twin screw extruder 2 Sensors 3. Anomaly detection device 4. Router 5. Diagnostic Equipment 6 Database 7 Terminal Equipment 10 Cylinders 11 Screw shaft 12 Motor 13 Reducer 14 Control device 31 Processing section 33 Communications Department 34 Acquisition Department 51 Diagnostic processing unit 53 Communications Department 54 Learning Model 54a Neural Networks P1, P2 computer program

Claims

1. An anomaly detection system including an anomaly detection device that detects an anomaly in a manufacturing device and a diagnosis device that diagnoses a state of the manufacturing device, A control device that controls the operation of the manufacturing device and transmits operation data indicating the content of the operation control to the abnormality detection device; a sensor that detects a physical quantity related to an operation of the manufacturing apparatus or a product, and outputs time-series sensor value data indicating the detected physical quantity to the anomaly detection device; a database that stores information including the operation data, the sensor value data, and the status of the manufacturing equipment; Equipped with The abnormality detection device includes: A first communication unit that receives the operation data transmitted from the control device; an acquisition unit that acquires the sensor value data output from the sensor; a processing unit that calculates statistics of the sensor value data acquired by the acquisition unit, and determines whether or not there is an abnormality in the manufacturing apparatus based on the calculated statistics and a threshold value corresponding to the received operation data; Equipped with The first communication unit is transmitting the driving data and the sensor value data to the diagnostic device; The diagnostic device comprises: a second communication unit that receives the operation data and the sensor value data transmitted from the abnormality detection device; a diagnostic processing unit that diagnoses a state of the manufacturing apparatus based on the operation data and the sensor value data received by the second communication unit and information stored in the database; Equipped with The second communication unit is A diagnosis result by the diagnosis processing unit is transmitted to an external terminal device. Anomaly detection system.

2. The state of the manufacturing equipment stored in the database includes information regarding the life of the manufacturing equipment or a part of the manufacturing equipment, and information regarding an abnormality of the manufacturing equipment or the part, The diagnostic processing unit: Diagnosing the lifespan or abnormality of the manufacturing equipment or parts The anomaly detection system according to claim 1 .

3. The diagnostic device comprises: a learning model that outputs a feature amount of the manufacturing device or the product when the sensor value data is input; The diagnostic processing unit: The state of the manufacturing equipment is diagnosed by comparing a feature value obtained by inputting the received sensor value data into the learning model with a feature value obtained by inputting the sensor value data stored in the database into the learning model. The anomaly detection system according to claim 1 or 2.

4. The second communication unit is receiving feedback information indicating whether the diagnosis result by the diagnosis processing unit is appropriate or a correct diagnosis result; The diagnostic processing unit: Based on the received feedback information, the learning model is additionally trained, or the diagnostic criterion based on the feature amount is updated. The anomaly detection system according to claim 3 .

5. The first communication unit or the second communication unit, receiving information indicating a state of the manufacturing equipment corresponding to the operation data and the sensor value data; The abnormality detection device or the diagnosis device is The received state is stored in the database in association with the driving data and the sensor value data. The anomaly detection system according to any one of claims 1 to 4.

6. The anomaly detection system according to any one of claims 1 to 5, Molding machine and Equipped with The abnormality detection system is configured to detect an abnormality in the molding machine. Molding machine system.

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