State estimation device, implementation system, and state estimation method

Abstract

This state estimating device (100) manages the state of a head of equipment in which the head is configured to hold an object and that executes a predetermined work by holding the object with the head. Said device comprises a state estimating unit (120) for estimating the state of the head, the state estimating unit (120) having: a first estimation unit (121) that, on the basis of a characteristic value of the head during the execution of the predetermined work by the equipment, estimates whether the state of the head has changed to a state related to a malfunction; and a second estimation unit (122) that, if it is estimated by the first estimation unit (121) that the state of the head has changed to the state related to the malfunction, estimates whether the head is in a state of malfunction on the basis of a check result different from said characteristic value.

Description

[Technical Field]

[0001] The present disclosure relates to a state estimation device, an implementation system, and a state estimation method. [Background technology]

[0002] Conventionally, equipment such as component mounting systems requires maintenance because it can deteriorate with use or over time. For example, Patent Document 1 discloses a technology in which the vacuum pressure of a vacuum pump in a head is measured by a sensor, and when the measured vacuum pressure reaches a threshold value at which the vacuum pump requires maintenance, it is determined that the vacuum pump requires maintenance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2012-033522 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the threshold value for when head maintenance is considered necessary is an empirically determined value, and depending on the environment in which the equipment equipped with the head is installed, this threshold value may not be an accurate value relative to the sensor measurement value. As a result, there are cases where the sensor measurement value reaches the threshold value but no malfunction is actually detected in the head, and there are also cases where the sensor measurement value does not reach the threshold but a malfunction is actually detected in the head, resulting in a problem of insufficient detection accuracy.

[0005] Therefore, the present disclosure provides a state estimation device and the like that can improve the accuracy of detecting heads that require maintenance. [Means for solving the problem]

[0006] A state estimation device according to one embodiment of the present disclosure is a state estimation device that manages the state of a head in equipment that is equipped with a head configured to hold an object and that holds the object with the head to perform a specified task, and that includes a state estimation unit that estimates the state of the head, and the state estimation unit has: a first estimation unit that estimates whether the state of the head has changed to a state related to a malfunction based on characteristic values ​​of the head when the equipment is performing the specified task; and a second estimation unit that, when the first estimation unit estimates that the state of the head has changed to a state related to a malfunction, estimates whether the head is in a malfunction based on inspection results that differ from the characteristic values.

[0007] These comprehensive or specific aspects may be realized by a system, an apparatus, a method, a recording medium, or a computer program, or may be realized by any combination of a system, an apparatus, a method, a recording medium, and a computer program. [Effects of the Invention]

[0008] According to the state estimation device and the like according to the present disclosure, it is possible to improve the accuracy of detecting heads that require maintenance. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a configuration diagram illustrating an example of a state estimating device according to an embodiment. [Figure 2A] FIG. 2A is a configuration diagram illustrating an example of a mounting system according to an embodiment. [Figure 2B] FIG. 2B is a configuration diagram illustrating an example of a component mounting apparatus according to an embodiment. [Figure 3A] FIG. 3A is a schematic diagram showing the configuration of the head main body according to the embodiment during vacuuming. [Figure 3B] FIG. 3B is a schematic diagram showing the configuration of the head main body according to the embodiment during blowing. [Figure 4] FIG. 4 is a diagram for explaining the operation of the state estimating device according to the embodiment. [Figure 5] FIG. 5 is a diagram for explaining a method for generating the online flow rate decrease determination threshold value. [Figure 6] FIG. 6 is a diagram for explaining a method for generating the offline flow rate decrease determination threshold. [Figure 7] FIG. 7 is a diagram for explaining a method for estimating the cause of a malfunction of the head. [Figure 8] FIG. 8 is a graph showing an example of waveform data of the flow rate in the air path. [Figure 9] FIG. 9 is a sequence diagram illustrating an example of the operation of the state estimating device and the maintenance technician according to the embodiment. [Figure 10] FIG. 10 is a flowchart showing an example of a state estimation method according to another embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] The state estimation device disclosed herein is a state estimation device that manages the state of a head in equipment that is equipped with a head configured to hold an object and that holds the object with the head to perform a specified task, and that includes a state estimation unit that estimates the state of the head, and the state estimation unit has a first estimation unit that estimates whether the state of the head has changed to a state related to a malfunction based on characteristic values ​​of the head when the equipment is performing the specified task, and a second estimation unit that, when the first estimation unit estimates that the state of the head has changed to a state related to a malfunction, estimates whether the head is in a malfunction based on inspection results that differ from the characteristic values.

[0011] According to this method, a first stage of estimation is performed to estimate whether the state of the head has changed to a state related to malfunction based on the characteristic values ​​of the head while the equipment is performing a predetermined task (e.g., production). If the state of the head has changed to a state related to malfunction, a second stage of estimation is performed to estimate whether the head is in a malfunction using inspection results other than the characteristic values ​​used in the first stage of estimation. In this way, two stages of estimation using different values ​​are performed instead of a single estimation, thereby improving the accuracy of detecting heads that require maintenance. Furthermore, the characteristic values ​​of the head are data that can be obtained without making any mechanical modifications to the equipment, etc. Therefore, the accuracy of detecting heads that require maintenance can be improved without making any mechanical modifications to the equipment, etc.

[0012] The characteristic value may be a measurement result of the flow rate or pressure in the air path of the head.

[0013] Measurements of flow rate or pressure in the head's air path can be used to infer whether the head has changed into a condition associated with a malfunction.

[0014] Furthermore, the inspection result that differs from the characteristic value may be an inspection result of the head when the equipment is not performing the predetermined operation.

[0015] According to this, the head inspection results in a special situation where the equipment is not performing the specified work, which is different from the situation in which the equipment in the first-stage estimation was performed, are used for the second-stage estimation, making it possible to perform a more detailed estimation in the second stage than the first-stage estimation.

[0016] The vehicle may further include an output unit that outputs information based on the estimation result of the state estimation unit.

[0017] This allows a response to be made according to the estimation result of the state estimation unit.

[0018] The information may include information indicating that the state of the head has changed to a state related to a malfunction, or information indicating that the head is in a malfunction. For example, the information may be displayed in a form that can be recognized by the equipment connected to the state estimation device or by an operator in the state estimation device.

[0019] This allows a person in charge of maintaining the equipment to recognize that the state of the head has changed to one related to malfunction, or that the head is in a malfunctioning state.

[0020] The information may also include details of maintenance for the head that is estimated to be in a malfunctioning state.

[0021] This allows maintenance to be carried out according to the maintenance content for the head that is estimated to be in a malfunctioning state.

[0022] The information may also be output to an equipment maintenance planning unit that manages a maintenance plan for maintaining the equipment.

[0023] This allows the equipment maintenance planning unit to manage the equipment maintenance plan in accordance with the output information.

[0024] In addition, the information may include information regarding the head that has been selected for inspection based on the estimation result of the first estimation unit, and the output unit may output a maintenance instruction to inspect the head that has been selected for inspection based on a maintenance plan assigned to inspect the head outside of the time period during which the specified work is performed.

[0025] This allows the head to be inspected outside of the time when the equipment is performing a predetermined operation (for example, production).

[0026] The mounting system of the present disclosure includes a head configured to hold an object, a measurement unit that measures characteristic values ​​of the head, and the above-described state estimation device that manages the state of the head.

[0027] This makes it possible to provide a mounting system that can improve the accuracy of detecting heads that require maintenance.

[0028] The state estimation method disclosed herein is a state estimation method for managing the state of a head in equipment that is equipped with a head configured to hold an object and that performs a specified task by holding the object with the head, and includes a state estimation step for estimating the state of the head, the state estimation step including a first estimation step for estimating whether the state of the head has changed to a state related to a malfunction based on characteristic values ​​of the head when the equipment is performing the specified task, and a second estimation step for estimating whether the head is in a malfunction based on inspection results that differ from the characteristic values ​​if it is estimated in the first estimation step that the state of the head has changed to a state related to a malfunction.

[0029] This makes it possible to provide a state estimation method that can improve the accuracy of detecting heads that require maintenance.

[0030] The embodiments described below are all comprehensive or specific examples, and the numerical values, shapes, materials, components, arrangement and connection of the components, steps, and order of steps shown in the following embodiments are merely examples and are not intended to limit the present disclosure.

[0031] (Embodiment) Hereinafter, the embodiment will be described with reference to FIGS.

[0032] First, a state estimating device 100 according to an embodiment will be described.

[0033] FIG. 1 is a diagram illustrating an example of a configuration of a state estimating device 100 according to an embodiment.

[0034] The state estimation device 100 is a device that manages the state of a head in a facility. The facility includes a head configured to hold an object (specifically, a head having a head main body configured to hold an object), and the head holds the object to perform a predetermined task, such as a component mounting device. For example, a nozzle is attached to the head (head main body), and the nozzle holds the object. The component mounting device holds components as the object and performs the predetermined task of mounting the held components onto a board or the like (i.e., producing a mounted board or the like). For example, the state estimation device 100 is a computer (such as a server) installed in a facility separate from the facility where the facility is installed, but it may also be installed in the facility where the facility is installed. Furthermore, the state estimation device 100 may be a computer installed in a single housing, or may be divided into two or more housings and realized by two or more computers.

[0035] The state estimation device 100 includes a threshold generation unit 110, a state estimation unit 120, and an output unit 130. The state estimation device 100 is implemented by a computer including a processor, a memory, etc. The threshold generation unit 110, the state estimation unit 120, and the output unit 130 are implemented by the processor operating in accordance with a program stored in the memory.

[0036] The threshold generation unit 110 acquires the equipment log of the head to which the nozzle is attached and generates a first threshold based on a data set consisting of multiple characteristic values ​​of the head included in the equipment log. The data set consists of multiple characteristic values ​​of the head that exceed a reference value. The reference value will be described later. In other words, the equipment log of the head to which the nozzle is attached is data recording the head's operational performance (e.g., errors or events) while the equipment is holding an object with the head and performing a predetermined task (specifically, a mounting task in which a component held by a nozzle attached to the head is mounted on a board, etc.). The head characteristic value is the measurement result of the flow rate or pressure in the head's air path. Specifically, the head characteristic value is the measurement result of the flow rate or pressure in the air path of the nozzle and head main body when air is supplied from the air source at positive or negative pressure. The air source is installed in the component mounting device or external to the component mounting device. Here, the head characteristic value is described as a flow rate value. Since the equipment performing a predetermined task, i.e., during mounting work, can be called "online," the head characteristic value included in the equipment log is called the online flow rate value. The threshold value generating unit 110 acquires chronologically older online flow rate values ​​from the equipment log, and therefore the characteristic values ​​of the head input to the threshold value generating unit 110 are called past online flow rate values.

[0037] The threshold generation unit 110 also acquires an inspection log of a head main body unit without a nozzle attached and generates a second threshold based on multiple characteristic values ​​of the head main body unit included in the inspection log. The multiple characteristic values ​​of the head main body unit are data measured after removing a nozzle from a head with a nozzle attached, whose characteristic value exceeds a reference value. Note that a characteristic value exceeding the reference value means that the characteristic value exceeds the lower or upper limit of the normal range. If the reference value is the lower limit, the characteristic value exceeding the reference value means that the characteristic value is below the reference value. If the reference value is the upper limit, the characteristic value exceeding the reference value means that the characteristic value exceeds the reference value. In other words, the inspection log of a head main body unit without a nozzle attached is a log of data from the head main body unit when the equipment is not holding an object with the head and not performing a specified operation (specifically, not performing mounting work). Here, the data refers to the inspection results when an inspection is performed on the head main body unit. Since equipment not performing a specified operation, i.e., not performing mounting work, can be called "offline," the characteristic value of the head main body unit included in the inspection log is called an offline flow rate value. The threshold value generating unit 110 acquires chronologically older offline flow rate values ​​from the inspection log, and therefore the characteristic values ​​of the head main body input to the threshold value generating unit 110 are called past offline flow rate values.

[0038] Furthermore, the threshold generating unit 110 generates a malfunction factor estimation table that associates the factor estimation values ​​calculated from the inspection log with malfunction factors based on the maintenance record of the head main body.

[0039] The details of the threshold generator 110 will be described later.

[0040] The state estimation unit 120 has a first estimation unit 121, a second estimation unit 122, and a factor estimation unit 123, and estimates the state of the head using the first estimation unit 121, the second estimation unit 122, and the factor estimation unit 123.

[0041] The first estimation unit 121 estimates whether the state of the head has changed to a state related to a malfunction based on the characteristic values ​​of the head when the equipment is performing a predetermined operation. Specifically, the first estimation unit 121 acquires the characteristic values ​​of the head to which the nozzle is attached, and estimates whether the state of the head has changed to a state related to a malfunction based on the acquired characteristic values ​​of the head and the first threshold generated by the threshold generation unit 110. A malfunction-related state is a state that may indicate an abnormality before reaching an abnormal state that would cause production by the equipment to be stopped. The first estimation unit 121 acquires the characteristic values ​​of the head to which a newly output nozzle is attached from the equipment performing the predetermined operation. The characteristic values ​​of the head acquired by the first estimation unit 121 are referred to as the latest online flow rate value in contrast to the past online flow rate value. Details of the first estimation unit 121 will be described later.

[0042] When the first estimation unit 121 estimates that the state of the head has changed to a state related to a malfunction, the second estimation unit 122 estimates whether the head is in a malfunctioning state based on an inspection result that differs from the characteristic value used for the estimation by the first estimation unit 121. Specifically, when the second estimation unit 122 estimates that the state of the head has changed to a state related to a malfunction, the second estimation unit 122 acquires inspection results of the head main body without a nozzle attached, and estimates whether the head is in a malfunctioning state based on the inspection results and a second threshold. A malfunctioning state is a state in which there is a sign of an abnormality before reaching an abnormal state that would cause production by the equipment to be stopped. For example, the second estimation unit 122 estimates whether the nozzle is in a malfunctioning state or whether the head (head main body) is in a malfunctioning state. After the second estimation unit 122 estimates that the state of the head has changed to a state related to a malfunction, the second estimation unit 122 acquires inspection results of the flow rate at the head main body when production by the equipment is stopped, the nozzle is detached from the head main body, and the equipment is not performing a predetermined operation. The inspection result of the head main body acquired by the second estimating unit 122 is called the latest offline flow rate value in contrast to the past offline flow rate value. Details of the second estimating unit 122 will be described later.

[0043] The factor estimation unit 123 estimates the malfunction factor of the head main body based on a factor estimation value calculated from the inspection result of the head main body and a malfunction factor estimation table. Details of the factor estimation unit 123 will be described later.

[0044] The output unit 130 outputs information based on the estimation result of the state estimation unit 120.

[0045] The output unit 130 outputs information to a maintenance person, for example, and the information output from the output unit 130 includes maintenance instructions for the maintenance person.

[0046] The information output from the output unit 130 also includes maintenance details for the head that is estimated to be in a malfunctioning state. The information including the maintenance details output from the output unit 130 is output, for example, to an equipment maintenance planning unit that manages equipment maintenance plans. For example, the information may be output to a maintenance device that maintains the equipment. For example, the maintenance details for the head include the serial number of the head to be maintained, the installation location of the head to be maintained, the details of the maintenance work, the person in charge, the maintenance deadline, etc.

[0047] The information output from the output unit 130 may also include information indicating that the state of the head has changed to a state related to a malfunction, or information indicating that the head is in a malfunction. The information including these pieces of information output from the output unit 130 is displayed, for example, in a form recognizable by a facility connected to the state estimation device 100 or by the state estimation device 100, in a form recognizable by a maintenance personnel. For example, based on the estimation result by the first estimation unit 121, text such as "A change in the state of the head has been detected" or "A head inspection is required" may be displayed on a display or the like, output from a speaker or the like, or a buzzer or a lamp known to indicate such information may sound or light up. For example, based on the estimation result by the second estimation unit 122, text such as "The head (head main body) is malfunctioning" or "The nozzle is malfunctioning" may be displayed on a display or the like, output from a speaker or the like, or a buzzer or a lamp known to indicate such information may sound or light up. Furthermore, for example, based on the estimation result by the factor estimation unit 123, text such as "A filter clog has occurred in the head main body" or "An air hose has broken in the head main body" may be displayed on a display or the like, output from a speaker or the like, or a buzzer or lamp known in advance to indicate such content may sound or light up. These displays, speakers, buzzers, etc. may be provided in the facility where the equipment is installed, and these alerts may be notified directly to the maintenance personnel of the facility.

[0048] By outputting information based on the estimation result of the state estimation unit 120, it is possible to take action in accordance with the estimation result of the state estimation unit. For example, it is possible to make a maintenance person of the equipment aware that the state of the head has changed to a state related to a malfunction, or that the head is in a malfunctioning state, and to carry out maintenance according to the maintenance content for the head that is estimated to be in a malfunctioning state or a state related to a malfunction.

[0049] For example, the information output from the output unit 130 may include information about the head that is the subject of inspection based on the estimation result of the first estimation unit 121. The output unit 130 may output a maintenance instruction to inspect the head that is the subject of inspection based on a maintenance plan assigned to a time outside of the time when the equipment performs predetermined operations. The state estimation device 100 can notify a maintenance person that a head inspection is necessary. The time outside of the time when the equipment performs predetermined operations refers, for example, to a time when the equipment is not producing. Since the head inspection requires removing the nozzle, the head inspection is performed when the equipment is not producing. Note that the time when the equipment is not producing may be when the product type being produced is changed. This allows the head that is the subject of inspection to be inspected outside of the time when the equipment performs predetermined operations (e.g., production). Furthermore, the head inspection may be performed outside the equipment. In this case, the head that is the subject of inspection can be inspected when the equipment is producing.

[0050] As described above, the facility may be a component mounting device, and the state estimating device 100 may form a mounting system together with the component mounting device. Here, a mounting system 2 according to an embodiment will be described.

[0051] FIG. 2A is a configuration diagram showing an example of a mounting system 2 according to an embodiment.

[0052] The mounting system 2 is a system for mounting components on a substrate or the like, and includes a component mounting apparatus 300 having a head 310 and a control unit 330, and a state estimation apparatus 100.

[0053] The head 310 has a head main body 311 and a sensor 320, and the nozzle 312 can be attached to the head main body 311. The sensor 320 is an example of a measurement unit that measures characteristic values ​​of the head 310 (head main body 311). The sensor 320 is a flow sensor that measures the flow rate in the air path of the head main body 311. The sensor 320 measures the online flow rate when the component mounting apparatus 300 is performing a predetermined operation with the head 310, to which the nozzle 312 is attached, holding an object, and the offline flow rate when the nozzle 312 is not attached to the head 310 and the component mounting apparatus 300 is not performing the predetermined operation. The head 310 may also have a vacuum sensor 340 that measures the vacuum pressure in the air path of the head main body 311 (see FIG. 2B ).

[0054] The control unit 330 controls the head 310. Details of the control of the head 310 will be described later. The control unit 330 also acquires online flow rate values ​​and offline flow rate values ​​measured by the sensor 320. The control unit 330 outputs the online flow rate values ​​and offline flow rate values ​​to the state estimation device 100 when the state estimation device 100 generates the first threshold value, the second threshold value, and the malfunction cause estimation table. The control unit 330 also outputs the latest online flow rate values ​​and latest offline flow rate values ​​to the state estimation device 100 when the state estimation device 100 estimates the state of the head 310.

[0055] Here, a specific example of the component mounting apparatus 300 will be described with reference to FIG. 2B.

[0056] 2B is a configuration diagram showing an example of a component mounting apparatus 300 according to an embodiment. In FIG. 2B, two axial directions perpendicular to each other in a horizontal plane are shown: the X direction (the direction perpendicular to the paper surface in FIG. 2B) in the board transport direction, and the Y direction (the left-right direction in FIG. 2B) perpendicular to the board transport direction. Also, the Z direction (the up-down direction in FIG. 2B) is shown as a height direction perpendicular to the horizontal plane.

[0057] Component mounting apparatus 300 has the function of mounting components D on board B. Board transport mechanism 12 provided on the upper surface of base 11 transports board B in the X direction and positions and holds it. Head movement mechanism 13 moves head 310, which is attached via plate 13a, in the X and Y directions. A nozzle 312 is attached to the bottom end of head 310.

[0058] A plurality of tape feeders 16 are attached to the top of a carriage 17 coupled to the base 11 on the side of the board transport mechanism 12, lined up in the X direction. Carrier tape 18, which stores components D to be supplied to the component mounting device 300, is wound around and stored on a reel 19 and held on the carriage 17. The carrier tape 18 inserted into the tape feeder 16 is pitch-fed at regular intervals by a tape feeding mechanism 16a built into the tape feeder 16. As a result, the components D stored on the carrier tape 18 are supplied in order to a component supply port 16b provided at the top of the tape feeder 16.

[0059] 2B, component mounting apparatus 300 includes control unit 330 that controls board conveying mechanism 12, head moving mechanism 13, head 310, and tape feeder 16 to perform a component mounting operation. During the component mounting operation, control unit 330 controls head moving mechanism 13 to move head 310 above tape feeder 16, and causes nozzle 312 to vacuum-suck and pick up component D supplied by tape feeder 16 to component supply port 16b (arrow a). Next, control unit 330 controls head moving mechanism 13 to move head 310 holding component D above board B held by board conveying mechanism 12, and causes component D to be mounted at a predetermined component mounting position Ba on board B (arrow b).

[0060] 2B, head 310 is equipped with vacuum sensor 340, which measures the degree of vacuum when nozzle 312 vacuum-sucks component D. The results of measurement of the degree of vacuum of nozzle 312 by vacuum sensor 340 during component holding operation can be used to detect the occurrence of a vacuum error, such as a pick-up failure (pick-up error) or a malfunction of head 310. For example, if nozzle 312 successfully picks up component D, the degree of vacuum will be lower than a predetermined value, and if nozzle 312 is unable to hold component D or picks up component D in an abnormal position, the degree of vacuum will not drop to the predetermined value. Therefore, control unit 330 can detect a vacuum error by determining whether the degree of vacuum measured by vacuum sensor 340 exceeds a predetermined value.

[0061] 2B, a board recognition camera 20 with its optical axis oriented downward is attached to plate 13a. Board recognition camera 20 moves in the X and Y directions together with head 310 by head movement mechanism 13. Board recognition camera 20 moves above tape feeder 16 and captures an image of component D supplied to the supply position of component supply port 16b.

[0062] The control unit 330 performs image recognition on the imaging results and calculates the amount of deviation of the actually supplied component D from the expected normal supply position. Based on the calculated amount of deviation, the control unit 330 corrects the suction position (stop position of the head 310) when the nozzle 312 picks up the component D, or the supply position of the component D on the tape feeder 16. The control unit 330 also performs image recognition on the imaging results and detects supply errors in which the component D cannot be recognized because the component D is not supplied to the component supply port 16b.

[0063] 2B, a component recognition camera 21 with its optical axis facing upward is attached to the upper surface of the base 11 between the board transport mechanism 12 and the tape feeder 16. When the nozzle 312 that picked up the component D passes above, the component recognition camera 21 captures an image of the underside of the component D held by the nozzle 312 (or the nozzle 312 that was unable to hold the component D).

[0064] The control unit 330 performs image recognition on the imaging results to determine whether the orientation of the component D held by the nozzle 312 is abnormal, or whether a recognition error has occurred that prevents the component D that should be held by the nozzle 312 from being recognized. The control unit 330 also performs image recognition on the imaging results to calculate the pickup position deviation amount, which is the deviation of the component D actually picked up by the nozzle 312 from the expected correct pickup position. When mounting the component D at the component mounting position Ba on the board B, the control unit 330 performs mounting position correction and mounting posture correction based on the pickup position deviation amount.

[0065] As described above, the component mounting apparatus 300 is equipped with the tape feeder 16, the head 310, and the nozzle 312. The control unit 330 then transmits to the management computer or the like, the detected occurrence status of pickup errors, supply errors, and recognition errors, the calculated amount of deviation in the supply position, the amount of correction of the pickup position of the component D by the nozzle 312, the amount of deviation in the pickup position, the amount of correction of the mounting position, and the amount of correction of the mounting attitude, in association with the device status (normal, abnormal, etc.). The control unit 330 also transmits to the state estimation apparatus 100 the measurement results of the various sensors provided in each mechanism of the component mounting apparatus 300, such as the degree of vacuum measured by the vacuum sensor 340, in association with the device status (normal, abnormal, etc.), for example.

[0066] Here, the configuration of head main body 311 will be described with reference to FIGS. 3A and 3B.

[0067] FIG. 3A is a schematic diagram showing the configuration of the head main body 311 according to the embodiment during vacuuming.

[0068] FIG. 3B is a schematic diagram showing the configuration of the head main body 311 according to the embodiment during blowing.

[0069] 3A and 3B schematically show a head main body 311, a nozzle 312 attached to the head main body 311, and an air source 319 that supplies air to the head main body 311.

[0070] The head main body 311 includes a nozzle holder 313 , a blow valve 314 , a vacuum valve 315 , a common air path 316 , a blow air path 317 , and a vacuum air path 318 .

[0071] A nozzle 312 is attached to the nozzle holder 313, and a component is picked up by suction through the nozzle 312.

[0072] The air source 319 is a device that supplies air under positive or negative pressure.

[0073] The blow valve 314 is a valve that controls the supply of air to the nozzle holder 313 when the air source 319 supplies air at a positive pressure (that is, during blowing).

[0074] The vacuum valve 315 is a valve that controls the suction of air from the nozzle holder 313 when the air source 319 supplies air at negative pressure (that is, during vacuuming).

[0075] Common air path 316 is an air path through which air from air source 319 passes when air source 319 supplies air at positive pressure, and through which air from nozzle holder 313 passes when air source 319 supplies air at negative pressure. In other words, common air path 316 is a path through which air passes both during blowing and vacuuming.

[0076] The blow air path 317 is an air path through which air from the air source 319 passes when the air source 319 supplies air at positive pressure.

[0077] The vacuum air path 318 is an air path through which air from the nozzle holder 313 passes when the air source 319 supplies air at negative pressure.

[0078] Blow valve 314 and vacuum valve 315 have switching mechanisms for switching air paths between blowing and vacuuming. For example, as shown in Fig. 3A, during vacuuming, the switching mechanisms in blow valve 314 and vacuum valve 315 are controlled so that the vacuum air source is connected to nozzle holder 313 via vacuum air path 318 and common air path 316. For example, as shown in Fig. 3B, during blowing, the switching mechanisms in blow valve 314 and vacuum valve 315 are controlled so that the blow air source is connected to nozzle holder 313 via blow air path 317 and common air path 316.

[0079] When a component is to be picked up by the nozzle 312 attached to the nozzle holder 313, the vacuum valve 315 is controlled to suck air from the nozzle 312 to the air source 319 via the common air path 316 and the vacuum air path 318. When the component is to be released from the nozzle 312 attached to the nozzle holder 313, the vacuum valve 315 is controlled to close the path from the air source 319 to the vacuum air path 318, thereby releasing the component from the nozzle 312. Even when the nozzle 312 is not attached to the nozzle holder 313 and the component cannot be picked up, air is still sucked in to obtain the inspection results of the head main body 311 (i.e., the offline flow rate).

[0080] When air is to be supplied, the blow valve 314 is controlled to supply air from the air source 319 to the nozzle 312 via the blow air path 317 and the common air path 316. Even when the nozzle 312 is not attached to the head main body 311, air is supplied to obtain the inspection results of the head main body 311 (i.e., the offline flow rate).

[0081] Sensor 320 is a flow rate sensor that measures the flow rate in the air paths (specifically, common air path 316 and blow air path 317) when air is supplied from air source 319 to nozzle holder 313, and measures the flow rate in the air paths (specifically, common air path 316 and vacuum air path 318) when air is sucked from nozzle holder 313 to air source 319. In addition, by controlling air source 319, blow valve 314, and vacuum valve 315, the common air path 316, blow air path 317, and vacuum air path 318 can be put into a vacuum state.

[0082] Next, the operation of the state estimating device 100 will be described.

[0083] 4 is a diagram illustrating the operation of the state estimation device 100 according to the embodiment. The phases in which the state estimation device 100 operates include the operation of the threshold generation unit 110 in a learning phase and the operation of the state estimation unit 120 in an estimation phase. The operation of the state estimation device 100 also includes an online flow rate change detection block in which the threshold generation unit 110 generates a first threshold and a first estimation unit 121 performs estimation using the generated first threshold, a flow rate change factor unit estimation block in which the threshold generation unit 110 generates a second threshold and a second estimation unit 122 performs estimation using the generated second threshold, and a head malfunction factor location estimation block in which the threshold generation unit 110 generates a malfunction factor estimation table and a factor estimation unit 123 performs estimation using the generated malfunction factor estimation table.

[0084] First, the online flow rate change detection block will be described.

[0085] In the online flow rate change detection block in the learning phase, the threshold generation unit 110 acquires an equipment log from the component mounting apparatus 300. The equipment log includes past online flow rate values. The past online flow rate values ​​acquired in the online flow rate change detection block are the online flow rate values ​​when the vacuum sensor 340 detected a vacuum error in the air path. The vacuum error threshold used to determine whether the vacuum sensor 340 detects a vacuum error is, for example, an empirically determined value. Depending on the environment in which the component mounting apparatus 300 is installed, the vacuum error threshold may not be an accurate value relative to the measurement value of the vacuum sensor 340. For this reason, even if the measurement value of the vacuum sensor 340 reaches the vacuum error threshold, there may be cases in which the head 310 is not actually malfunctioning, or even if the measurement value of the vacuum sensor 340 does not reach the vacuum error threshold, there may be cases in which the head 310 is actually malfunctioning. This results in a problem of insufficient accuracy in detecting heads 310 requiring maintenance.

[0086] Therefore, the threshold generation unit 110 uses the vacuum error threshold to generate an online flow rate reduction determination threshold that can more accurately estimate a malfunction of the head 310 than the vacuum error threshold. The online flow rate reduction determination threshold is an example of a first threshold, and the vacuum error threshold is an example of a reference value that is compared with the online flow rate value.

[0087] For example, the threshold generator 110 allocates the difference between the online flow rate value of the head 310, calculated by comparing past online flow rate values ​​with the vacuum error threshold, to multiple clusters as learning data, and generates an online flow rate reduction determination threshold based on the online flow rate value in one cluster among the multiple clusters with the smallest difference. The difference between the online flow rate value and the vacuum error threshold is an example of a state change amount. Here, a method for generating the online flow rate reduction determination threshold will be described with reference to FIG. 5.

[0088] Fig. 5 is a diagram for explaining a method for generating the threshold for determining a decrease in online flow rate. Fig. 5 plots the online flow rate values ​​included in the equipment log, with the horizontal axis representing the online flow rate value and the vertical axis representing the difference between the online flow rate value and the vacuum error threshold.

[0089] The threshold generation unit 110 classifies the acquired past online flow rate values. For example, the threshold generation unit 110 applies the k-means algorithm to classify the difference between the online flow rate value and the vacuum error threshold when a vacuum error is detected into two classes. For example, as shown in FIG. 5, the online flow rate values ​​are classified into Class A, in which the difference between the online flow rate value and the vacuum error threshold is large, and Class B, in which the difference between the online flow rate value and the vacuum error threshold is small. When the head 310 does not properly pick up a component, the air paths (specifically, the common air path 316 and the vacuum air path 318) communicate with the outside, so a large amount of air is sucked in, tending to increase the difference. Therefore, Class A is a class in which the head 310 is likely to not properly pick up a component; in other words, a class in which the head 310 itself is likely to be normal. On the other hand, when the head 310 properly picks up a component, the amount of sucked air is small, and the difference tends to be small. Therefore, Class B is a class in which the head 310 is likely to be malfunctioning. Therefore, the threshold generator 110 generates an online flow rate reduction determination threshold based on the online flow rate value in class B where the difference is smallest. Specifically, the threshold generator 110 subtracts 1 from the minimum online flow rate value in class B (point P: -70 in FIG. 5) to set the online flow rate reduction determination threshold (dashed line th: -71 in FIG. 5). When generating the online flow rate reduction determination threshold, the value to be subtracted from the minimum online flow rate value in the class where the difference is smallest is not limited to 1 and can be selected appropriately.

[0090] In the online flow rate change detection block in the estimation phase, the first estimation unit 121 acquires the latest online flow rate value of the head 310 to which the nozzle 312 is attached, and estimates whether the state of the head 310 has changed to a state related to a malfunction based on the acquired online flow rate value and the online flow rate reduction judgment threshold.

[0091] If the latest acquired online flow rate value exceeds the online flow rate decrease determination threshold (specifically, if the absolute value of the online flow rate value is smaller than the absolute value of the online flow rate decrease determination threshold), the first estimation unit 121 estimates that the state of the head 310 has changed to a state related to a malfunction, and the output unit 130 outputs that information. For example, a maintenance person or the like is notified that it is estimated that the state of the head 310 has changed to a state related to a malfunction, and determines whether to inspect the head 310.

[0092] The online flow rate decrease determination threshold is an estimated value derived by classifying past online flow rate values ​​as learning data, and is used to indirectly determine whether or not there is an abnormality such as a clog in the flow system. The online flow rate change detection block uses this online flow rate decrease determination threshold to estimate whether or not the state of the head 310 has changed to a state related to a malfunction, thereby improving the accuracy of detecting heads 310 that require maintenance compared to when a vacuum error threshold is used to estimate whether or not the state of the head 310 has changed to a state related to a malfunction.

[0093] Then, upon receiving the information about the malfunction of the head output from the output unit 130, the maintenance person inspects the head 310. For example, when the component mounting apparatus 300 is not performing a predetermined operation (that is, for example, when production is stopped), the nozzle 312 is removed from the head main body 311, and the head main body 311 without the nozzle 312 attached is inspected. Specifically, the latest offline flow rate of the head main body 311 is measured. The measured latest offline flow rate value is used for estimation by the second estimation unit 122 in the flow rate change factor unit estimation block.

[0094] Next, the flow rate change factor unit estimation block will be described.

[0095] In the flow rate change factor unit estimation block in the learning phase, the threshold value generation unit 110 acquires an inspection log from the component mounting apparatus 300. The inspection log includes past offline flow rate values. The inspection log acquired in the flow rate change factor unit estimation block includes past offline flow rate values ​​when the vacuum sensor 340 detected a vacuum error in the air path.

[0096] For example, the threshold generation unit 110 acquires an inspection log of the head main body 311 to which the nozzle 312 is not attached, and generates an offline flow rate reduction determination threshold based on the past offline flow rate value of the head main body 311 included in the inspection log. The offline flow rate reduction determination threshold is an example of a second threshold. Here, a method for generating the offline flow rate reduction determination threshold will be described with reference to FIG. 6.

[0097] Fig. 6 is a diagram for explaining a method for generating the offline flow rate reduction determination threshold value. Fig. 6 plots combinations of online flow rate values ​​and offline flow rate values ​​included in the equipment log when a vacuum error is detected, with the horizontal axis representing the online flow rate value and the vertical axis representing the offline flow rate value.

[0098] The threshold generation unit 110 calculates a regression line from each plotted point, as shown in FIG. 6. Because there is a correlation between the online flow rate value acquired when a vacuum error occurs and the offline flow rate value acquired when the nozzle 312 is removed from the head 310 and the head main body 311 is inspected at that time, there is also a correlation between the online flow rate reduction determination threshold and the offline flow rate reduction determination threshold. Therefore, based on the calculated regression line, the threshold generation unit 110 sets the offline flow rate value corresponding to the online flow rate reduction determination threshold as the offline flow rate reduction determination threshold. Specifically, as shown in FIG. 6, the threshold generation unit 110 generates the offline flow rate value -90.5 on the vertical axis corresponding to the online flow rate value -71 on the horizontal axis (i.e., the online flow rate reduction determination threshold) on the regression line as the offline flow rate reduction determination threshold.

[0099] In this way, the threshold generation unit 110 generates an offline flow rate reduction judgment threshold based on the online flow rate reduction judgment threshold by linear regression using multiple online flow rate values ​​of the head 310 to which the nozzle 312 is attached and multiple offline flow rate values ​​of the head main body unit 311 corresponding to the multiple online flow rate values ​​as learning data.

[0100] In the flow rate change factor unit estimation block in the estimation phase, when the first estimation unit 121 estimates that the state of the head 310 has changed to a state related to a malfunction, the second estimation unit 122 acquires the inspection result (i.e., the latest offline flow rate value) of the head main body 311 to which the nozzle 312 is not attached, and estimates whether the head 310 is in a malfunctioning state based on the acquired inspection result and the offline flow rate decrease determination threshold. Specifically, the second estimation unit 122 estimates whether the nozzle 312 is in a malfunctioning state or whether the head 310 (head main body 311) is in a malfunctioning state.

[0101] If the inspection result of the head main body 311 exceeds the offline flow rate reduction judgment threshold (specifically, the absolute value of the offline flow rate value is smaller than the absolute value of the offline flow rate reduction judgment threshold), the second estimation unit 122 estimates that the head main body 311 is in a malfunctioning state. This is because, even when the nozzle 312 is detached, the inspection result of the head main body 311 exceeds the offline flow rate reduction judgment threshold, and it can be estimated that the malfunction is not on the nozzle 312 side but on the head main body 311 side. For example, if an air leak or air blockage occurs in the air path of the head main body 311, the inspection result of the head main body 311 exceeds the offline flow rate reduction judgment threshold even when the nozzle 312 is detached, and it can be estimated that the malfunction is on the head main body 311 side. On the other hand, if the inspection result of the head main body 311 does not exceed the offline flow rate reduction judgment threshold, the second estimation unit 122 estimates that the nozzle 312 is in a malfunctioning state. This is because by removing the nozzle 312, it can be assumed that the inspection result of the head main body 311 no longer exceeds the offline flow rate decrease determination threshold, and it can be assumed that the malfunction is not on the head main body 311 side, but on the nozzle 312 side. For example, the output unit 130 outputs that the nozzle 312 is in a malfunction, and a maintenance person for the equipment or the like can perform maintenance on the nozzle 312.

[0102] The offline flow rate reduction determination threshold is an estimated value derived by linear regression using past offline flow rate values ​​as learning data, for indirectly determining whether or not there is an abnormality such as a clog in the flow rate system. The flow rate change factor unit estimation block uses this offline flow rate reduction determination threshold to estimate whether the nozzle 312 or the head main body 311 is in a malfunctioning state, thereby improving the accuracy of the estimation.

[0103] Next, the head malfunction factor location estimation block will be described.

[0104] In the head malfunction factor location estimation block in the learning phase, the threshold generation unit 110 acquires an inspection log from the component mounting device 300. The inspection log includes waveform data of past offline flow rates. For example, the waveform data of past offline flow rates acquired in the head malfunction factor location estimation block includes waveform data of offline flow rates when the vacuum sensor 340 detects a vacuum error in the air path, as well as waveform data of offline flow rates under normal conditions when the vacuum sensor 340 does not detect a vacuum error in the air path. The threshold generation unit 110 generates a malfunction factor estimation table by correlation analysis using the factor estimation value calculated from the inspection log and the malfunction factor of the head main body 311 as learning data. The factor estimation value calculated from the inspection log and the malfunction factor estimation table will be described later.

[0105] In the head malfunction factor location estimation block in the estimation phase, if the second estimation unit 122 estimates that the head main body 311 is in a malfunctioning state, the factor estimation unit 123 uses a malfunction factor estimation table to estimate the malfunction factor of the head main body 311 based on a factor estimation value calculated from the inspection results of the head main body 311. Here, a method for estimating the malfunction factor of the head 310 will be described with reference to FIG. 7.

[0106] Fig. 7 is a diagram for explaining a method for estimating a malfunction factor of the head 310. The upper part of Fig. 7 shows a method for generating a factor estimate calculation mean vector and a factor estimate calculation covariance matrix from the inspection log in the threshold generation unit 110, and the lower part of Fig. 7 shows a method for estimating a malfunction factor of the head main body 311 using a factor estimate calculated based on the factor estimate calculation mean vector and the factor estimate calculation covariance matrix in the factor estimation unit 123.

[0107] 7, the threshold generator 110 acquires from the inspection log a waveform data set of past offline flow rates under normal conditions when the vacuum sensor 340 has not detected any vacuum errors in the air path. A specific example of the waveform data will now be described with reference to FIG.

[0108] FIG. 8 is a graph showing an example of waveform data of the flow rate in the air path. FIG. 8 shows time series data of flow rate values ​​sampled at, for example, 256 points, with the horizontal axis representing time and the vertical axis representing flow rate. As an example, FIG. 8 shows waveform data of the flow rate in the air path when the vacuum valve 315 is repeatedly turned on and off during vacuuming. Note that similar waveform data is also obtained during blowing. In this embodiment, when waveform data of past offline flow rates is obtained, it is obtained as a waveform data set defined by an array as shown in the upper part of FIG. 7.

[0109] The waveform data of the flow rate measured by the sensor 320 has a waveform shape as shown in Fig. 8, making it difficult to compare waveform data with each other. Therefore, as shown in the upper part of Fig. 7, the threshold generation unit 110 converts each of the waveform data sets of the past offline flow rates into principal component vectors by principal component analysis so that the characteristics of each of the waveform data of multiple past offline flow rates can be compared with the characteristics of the waveform data of the latest offline flow rate. Then, the threshold generation unit 110 generates a factor estimate value calculation mean vector and a factor estimate value calculation covariance matrix from the converted principal component vectors.

[0110] The threshold generation unit 110 also calculates the Mahalanobis distance of each principal component vector converted from the waveform data set of past offline flow rates as a factor estimation value. For example, the data for each principal component vector, in other words, each waveform data set, can be determined from the maintenance record to determine the malfunction factor of the head main body 311. Therefore, the factor estimation value of the principal component vector corresponding to each waveform data set of past offline flow rates can be associated with the malfunction factor based on the maintenance record. In this way, the threshold generation unit 110 generates a malfunction factor estimation table that associates the factor estimation value calculated from the inspection log with the malfunction factor based on the maintenance record of the head main body 311.

[0111] As shown in the lower part of FIG. 7 , the factor estimation unit 123 acquires waveform data of the latest offline flow rate and converts it into a principal component vector by principal component analysis. The factor estimation unit 123 then calculates the Mahalanobis distance of the converted principal component vector. The factor estimation unit 123 uses the calculated Mahalanobis distance as a factor estimation value and an index for estimating the malfunction factor of the head main body 311. In this way, the factor estimation value calculated from the inspection results of the head main body 311 is calculated based on the statistical values ​​of the inspection log in which the head main body 311 is determined to be in a normal state (specifically, the mean vector and variance matrix generated from the statistical values ​​of the inspection log). The factor estimation unit 123 estimates the malfunction factor of the head main body 311 by comparing the calculated factor estimation value corresponding to the waveform data of the latest offline flow rate with the malfunction factor estimation table. For example, if a factor estimation value comparable to the calculated factor estimation value is found in the malfunction factor estimation table, the factor estimation unit 123 estimates that the malfunction factor associated with the factor estimation value in the malfunction factor estimation table is the malfunction factor of the head main body 311. For example, the output unit 130 outputs the cause of the malfunction of the head main body 311, and a maintenance person for the equipment can perform maintenance according to the cause of the malfunction of the head main body 311.

[0112] Note that if the calculated factor estimation value is within the unit space (the range of a non-defective product), there is a possibility that the head main body 311 is not in a malfunctioning state, but the first estimation unit 121 estimates that the head 310 is in a malfunctioning state, and the second estimation unit 122 estimates that the head main body 311 is in a malfunctioning state, so there is a possibility that the head main body 311 is in a malfunctioning state. Therefore, if the calculated factor estimation value is within the unit space, the factor estimation unit 123 may estimate that the head main body 311 may be in a malfunctioning state. For example, the output unit 130 outputs a message indicating that the head main body 311 may be in a malfunctioning state, and a maintenance person for the equipment or the like can perform maintenance on the head main body 311.

[0113] The malfunction factor table is a correlation table generated by a correlation analysis between the factor estimation value obtained from the inspection log and the malfunction factors recorded in the maintenance record. The head malfunction factor location estimation block uses this malfunction factor estimation table to estimate the malfunction factor of the head main body 311, thereby improving the accuracy of the estimation.

[0114] Next, the operation between the state estimating device 100 and the maintenance personnel of the facility will be described with reference to FIG.

[0115] FIG. 9 is a sequence diagram showing an example of the operation of the state estimating device 100 and the maintenance technician according to the embodiment.

[0116] First, the state estimation device 100 performs learning in advance using past data (step S101). That is, the state estimation device 100 generates in advance an online flow rate decrease determination threshold, an offline flow rate decrease determination threshold, and a malfunction cause estimation table.

[0117] The state estimating device 100 periodically receives the latest online flow rate value from the component mounting device 300 (step S102), and performs processing in the online flow rate change detection block shown in FIG. 4 (step S103).

[0118] If the latest received online flow rate value does not have an abnormality (i.e., if the latest received online flow rate value does not exceed the online flow rate decrease determination threshold), the state estimation device 100 continues processing in steps S102 and S103.

[0119] If the latest received online flow rate value is abnormal (i.e., if the latest received online flow rate value exceeds the online flow rate decrease determination threshold), the state estimation device 100 issues a flow rate change detection alert (step S104). For example, in this case, the state estimation device 100 outputs information for inspecting the head 310, specifically, an instruction to inspect the head 310. The timing for transmitting the instruction to inspect the head 310 can be determined appropriately.

[0120] The maintenance person receives the instruction to inspect the head 310 (step S105) and inspects the head 310 (step S106). For example, the maintenance person removes the nozzle 312 from the head main body 311 and inspects the head main body 311 (measures the latest offline flow rate).

[0121] The state estimating device 100 receives the latest offline flow rate value from the component mounting device 300 (step S107), and performs processing in the flow rate change factor unit estimating block shown in FIG. 4 (step S108).

[0122] If the latest received offline flow rate value does not contain any abnormality (i.e., if the latest received online flow rate value does not exceed the offline flow rate reduction judgment threshold), the state estimation device 100 estimates that the nozzle 312 is in a malfunctioning state and notifies the nozzle maintenance instruction information, which is information for instructing maintenance of the nozzle 312 (step S109).

[0123] The maintenance person receives the maintenance instruction for the nozzle 312 (step S110) and performs maintenance on the nozzle 312 (step S111). After completing the maintenance on the nozzle 312, the maintenance person registers the completion of the maintenance on the nozzle 312 (step S112).

[0124] On the other hand, if the latest received offline flow rate value is abnormal (i.e., if the latest received online flow rate value exceeds the offline flow rate decrease determination threshold), the state estimation device 100 estimates that the head main body 311 is in a malfunctioning state and performs processing in the head malfunction cause location estimation block shown in Fig. 4 (step S113). The state estimation device 100 estimates the malfunction cause of the head main body 311 and notifies head maintenance instruction information, which is information for instructing maintenance of the head main body 311 (step S114). For example, the head maintenance instruction information includes a maintenance instruction for the head main body 311, specifically the malfunction cause of the head main body 311.

[0125] The maintenance technician receives the maintenance instruction for the head main body 311 (step S115) and performs maintenance on the head main body 311 (step S116). The maintenance instruction for the head main body 311 includes the cause of the malfunction of the head main body 311, so the maintenance technician can efficiently perform maintenance on the head main body 311 by focusing on the location that caused the malfunction. After completing the maintenance of the head main body 311, the maintenance technician registers the completion of the maintenance of the head main body 311 (step S117).

[0126] As described above, a first-stage estimation is performed to estimate whether the state of the head 310 has changed to a state related to a malfunction based on the online flow rate value of the head 310 while the component mounting apparatus 300 is performing a predetermined operation (e.g., production). If the state of the head 310 has changed to a state related to a malfunction, a second-stage estimation is performed to estimate whether the head 310 is in a malfunction using an offline flow rate value different from the characteristic value used in the first-stage estimation. In this way, two-stage estimations using different values ​​are performed instead of a single estimation, thereby improving the accuracy of detecting heads 310 requiring maintenance. For example, the inspection results (specifically, offline flow rate values) of the head 310 in a special situation where the component mounting apparatus 300 is not performing a predetermined operation, which is different from the situation in which the component mounting apparatus 300 performed the first-stage estimation, can be used in the second-stage estimation. This allows for a more detailed second-stage estimation than the first-stage estimation. Furthermore, the online flow rate value of the head 310 is data that can be obtained without any mechanical modifications to the component mounting apparatus 300, etc. Therefore, it is possible to improve the detection accuracy of the head 310 that requires maintenance without making any mechanical modifications to the component mounting apparatus 300 or the like.

[0127] (Other embodiments) The state estimation device 100 and implementation system 2 of the present disclosure have been described above based on the embodiments, but the present disclosure is not limited to the above-described embodiments. As long as they do not deviate from the spirit of the present disclosure, various modifications conceivable by a person skilled in the art to the present embodiments and forms constructed by combining components of different embodiments are also included within the scope of the present disclosure.

[0128] For example, in the above embodiment, the online flow rate value and the offline flow rate value are described as the measurement values ​​of the sensor 320 when the vacuum sensor 340 detects a vacuum error, but this is not limited to this. For example, the sensor 320 may detect an error using a value that is set regardless of the facility environment, and the online flow rate value and the offline flow rate value may be the measurement values ​​of the sensor 320 when the sensor 320 detects such an error.

[0129] For example, in the above embodiment, an example has been described in which the state estimation unit 120 includes the factor estimation unit 123, but the state estimation unit 120 may not include the factor estimation unit 123. In this case, the output unit 130 does not need to output information according to the estimation result of the factor estimation unit 123, and may output only information according to the estimation result of the first estimation unit 121 or the estimation result of the second estimation unit 122.

[0130] For example, in the above embodiment, an example has been described in which the state estimation unit 120 includes the second estimation unit 122, but the state estimation unit 120 may not include the second estimation unit 122. In this case, the output unit 130 may not output information according to the estimation result of the second estimation unit 122, and may output only information according to the estimation result of the first estimation unit 121.

[0131] For example, in the above embodiment, an example has been described in which the output unit 130 outputs information to a maintenance person, but the information does not have to be output to a maintenance person. For example, the output unit 130 may output information only to equipment connected to the state estimation device 100 or to devices in the state estimation device 100 that can be recognized by a maintenance person.

[0132] For example, inspection of head 310 may be performed on component mounting apparatus 300, or head 310 may be removed from component mounting apparatus 300 and inspected on an inspection device for head 310 (e.g., a device that can supply power and air to head 310).

[0133] For example, the present disclosure can be realized not only as the state estimation device 100 but also as a state estimation method including steps (processing) performed by each component of the state estimation device 100.

[0134] FIG. 10 is a flowchart showing an example of a state estimation method according to another embodiment.

[0135] The state estimation method is a state estimation method for managing the state of a head in equipment that is equipped with a head configured to hold an object and that performs a specified task by holding the object with the head, and includes a state estimation step for estimating the state of the head, and as shown in Figure 10, the state estimation step includes a first estimation step (step S11) for estimating whether the state of the head has changed to a state related to a malfunction based on characteristic values ​​of the head when the equipment is performing the specified task, and a second estimation step (step S12) for estimating whether the head is in a malfunctioning state based on inspection results that differ from the characteristic values ​​when it is estimated in the first estimation step that the state of the head has changed to a state related to a malfunction.

[0136] For example, the steps in the state estimation method may be executed by a computer (computer system). The present disclosure can be realized as a program for causing a computer to execute the steps included in the state estimation method. Furthermore, the present disclosure can be realized as a non-transitory computer-readable recording medium, such as a CD-ROM, on which the program is recorded.

[0137] For example, when the present disclosure is realized as a program (software), each step is performed by running the program using hardware resources such as a computer's CPU, memory, input / output circuits, etc. In other words, each step is performed by the CPU acquiring data from memory or input / output circuits, etc., performing calculations on the data, and outputting the calculation results to memory or input / output circuits, etc.

[0138] Furthermore, each of the components included in the state estimating device 100 of the above embodiment may be realized as a dedicated or general-purpose circuit.

[0139] Furthermore, each of the components included in the state estimation device 100 of the above embodiment may be realized as an LSI (Large Scale Integration) which is an integrated circuit (IC).

[0140] Furthermore, the integrated circuit is not limited to an LSI, but may be realized by a dedicated circuit or a general-purpose processor. A programmable FPGA (Field Programmable Gate Array) or a reconfigurable processor in which the connections and settings of circuit cells within the LSI can be reconfigured may also be used.

[0141] Furthermore, if an integrated circuit technology that can replace LSI emerges due to advances in semiconductor technology or other derived technologies, it is natural that each component included in the state estimation device 100 may be integrated into an integrated circuit using that technology.

[0142] In addition, this disclosure also includes forms obtained by making various modifications to the embodiments that a person skilled in the art would think of, and forms realized by arbitrarily combining the components and functions of each embodiment within the scope of the present disclosure. [Industrial Applicability]

[0143] The present disclosure can be used, for example, to manage equipment that holds an object with a head and performs a predetermined task. [Explanation of symbols]

[0144] 2. Mounting system 11 Foundation 12 Substrate transport mechanism 13 Head movement mechanism 13a Plate 16 Tape Feeder 16a Tape feed mechanism 16b Parts supply port 17 Cart 18 Carrier tape 19 reels 20 Circuit Board Recognition Camera 21 Parts Recognition Camera 100 State Estimation Device 110 Threshold generation unit 120 State Estimation Unit 121 1st estimation part 122 Second estimation part 123 Factor Estimation Section 130 Output section 300 Component Mounting Equipment 310 head 311 Head body 312 Nozzle 313 Nozzle holder 314 Blow valve 315 Vacuum valve 316 Common Air Path 317 Blow air path 318 Vacuum air route 319 Air Source 320 Sensors 330 Control Unit 340 Vacuum Sensor

Claims

1. 1. A state estimation device for managing a state of a head in equipment configured to hold an object, the head holding the object and performing a predetermined task, the state estimation device comprising: a state estimation unit that estimates a state of the head; a threshold value generating unit that generates a first threshold value and a second threshold value; The state estimation unit a first estimation unit that estimates whether the state of the head has changed to a state related to a malfunction based on a characteristic value of the head when the equipment is performing the predetermined task and the first threshold value; a second estimation unit that, when the first estimation unit estimates that the state of the head has changed to a state related to a malfunction, estimates whether the head is in a malfunction state based on the test result that differs from the characteristic value and the second threshold value; The threshold generation unit classifying a difference between the characteristic value in the past when a vacuum error in the air path of the head was detected and a vacuum error threshold value for determining whether or not to detect a vacuum error, and generating the first threshold value based on the characteristic value in the class in which the difference is smallest; calculating a regression line from a combination of the characteristic value and the test result from a past time when a vacuum error in the air path was detected, and generating, as the second threshold value, a value of the test result corresponding to the first threshold value on the calculated regression line; State estimator.

2. the characteristic value is a measurement result of a flow rate or a pressure in an air path of the head; The state estimation device according to claim 1 .

3. The inspection result that differs from the characteristic value is an inspection result of the head when the equipment is not performing the predetermined operation. The state estimation device according to claim 1 or 2.

4. an output unit that outputs information based on the estimation result of the state estimation unit; The state estimation device according to claim 1 .

5. the information includes information indicating that the state of the head has changed to a state related to a malfunction, or information indicating that the head is in a malfunctioning state; The state estimation device according to claim 4 .

6. the information is displayed in a form recognizable by an operator on the equipment connected to the state estimation device or on the state estimation device. The state estimation device according to claim 5 .

7. The information includes maintenance details for the head that is estimated to be in a malfunctioning state. The state estimation device according to any one of claims 4 to 6.

8. The information is output to an equipment maintenance planning unit that manages a maintenance plan for maintaining the equipment. The state estimation device according to any one of claims 4 to 7.

9. the information includes information about the head that is the inspection target based on the estimation result of the first estimation unit, the output unit outputs a maintenance instruction to inspect the head that is the inspection target based on a maintenance plan assigned to the head outside of the time period in which the predetermined work is performed. The state estimation device according to any one of claims 4 to 8.

10. a head configured to hold an object; a measurement unit that measures a characteristic value of the head; and a state estimation device according to any one of claims 1 to 9, which manages the state of the head. Implementation system.

11. 1. A state estimation method for managing a state of a head in equipment configured to hold an object, the head holding the object and performing a predetermined task, the method comprising: a state estimating step of estimating a state of the head; a threshold value generating step of generating a first threshold value and a second threshold value; The state estimation step a first estimation step of estimating whether or not the state of the head has changed to a state related to malfunction based on a characteristic value of the head when the equipment is performing the predetermined work and the first threshold value; a second estimation step of estimating whether or not the head is in a malfunctioning state based on an inspection result that differs from the characteristic value and the second threshold value when it is estimated in the first estimation step that the state of the head has changed to a state related to a malfunction, In the threshold generation step, classifying a difference between the characteristic value in the past when a vacuum error in the air path of the head was detected and a vacuum error threshold value for determining whether or not to detect a vacuum error, and generating the first threshold value based on the characteristic value in the class in which the difference is smallest; calculating a regression line from a combination of the characteristic value and the test result from a past time when a vacuum error in the air path was detected, and generating, as the second threshold value, a value of the test result corresponding to the first threshold value on the calculated regression line; State estimation methods.

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