Abnormality diagnosis device of hole drilling device, abnormality diagnosis method and abnormality diagnosis program

Abstract

To diagnose such an abnormality as generation of a rifle mark accompanied by wear of a tool.SOLUTION: A hole drilling device comprises at least one tool which is rotatable for forming a hole at a processing object. An abnormality diagnosis device acquires an AE signal from at least one AE sensor which is arranged at least at either processing object or the tool. An abnormality of the tool is determined on the basis of phase information of the AE signal.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to an abnormality diagnosis device, an abnormality diagnosis method, and an abnormality diagnosis program for a hole drilling device. [Background technology]

[0002] Deep hole cutting (BTA) is known for forming deep holes in objects with sufficient thickness. In deep hole cutting, a tool equipped with a cutting blade for cutting and a guide pad for guiding the periphery of the cutting blade is rotated while drilling the object, thereby forming deep holes along the thickness direction.

[0003] An example of a workpiece to be machined by such deep hole cutting is a tube plate used in a steam generator used in a pressurized water reactor (PWR). Patent Document 1 discloses an example of this type of steam generator, and shows, as one of its components, a tube plate for supporting U-shaped tubes through which secondary cooling water flows, which is a heat exchange target for primary cooling water heated by the reactor. The tube plate is constructed by forming a large number of holes by deep hole cutting in a plate-like member having a sufficient thickness, and a large number of U-shaped tubes through which secondary cooling water flows are inserted so as to pass through these holes. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2014-106048 A Summary of the Invention [Problem to be solved by the invention]

[0005] As described above, a tool used in deep hole cutting is provided with cutting blades and guide pads. When a tool is used repeatedly, the cutting blades and guide pads wear out, which may result in machining marks called rifle marks. The machining marks grow larger as the wear progresses, and if the size of the marks exceeds an allowable value, this may cause a decrease in productivity, such as the need to redo machining or the generation of defective products. Conventionally, in order to prevent a decrease in productivity due to the occurrence of such rifle marks, the tools have been inspected after machining and replaced at relatively frequent intervals depending on the inspection results. However, the increased frequency of tool replacement leads to increased costs.

[0006] At least one embodiment of the present disclosure has been made in consideration of the above-mentioned circumstances, and aims to provide an abnormality diagnosis device, an abnormality diagnosis method, and an abnormality diagnosis program for a hole drilling device that can appropriately diagnose abnormalities such as the occurrence of rifle marks due to tool wear. [Means for solving the problem]

[0007] In order to solve the above problem, an abnormality diagnosis device for a hole drilling device according to at least one embodiment of the present disclosure includes: An abnormality diagnosis device for a hole drilling device including at least one rotatable tool for forming a hole in a workpiece, an AE signal acquisition unit for acquiring an AE signal from at least one AE sensor provided on at least one of the workpiece or the tool; an abnormality determination unit for determining an abnormality of the tool based on phase information of the AE signal; Equipped with.

[0008] In order to solve the above problem, a method for diagnosing an abnormality in a hole drilling device according to at least one embodiment of the present disclosure includes: A method for diagnosing an abnormality in a hole drilling device having at least one rotatable tool for forming a hole in a workpiece, comprising: acquiring an AE signal from at least one AE sensor provided on at least one of the workpiece or the tool; determining an abnormality in the tool based on phase information of the AE signal; Equipped with.

[0009] In order to solve the above problem, an abnormality diagnosis program for a hole drilling device according to at least one embodiment of the present disclosure includes: An abnormality diagnosis program for a hole processing device having at least one rotatable tool for forming a hole in a workpiece, comprising: A computer device includes: acquiring an AE signal from at least one AE sensor provided on at least one of the workpiece or the tool; determining an abnormality in the tool based on phase information of the AE signal; It is possible to execute the following. Effect of the Invention

[0010] According to at least one embodiment of the present disclosure, it is possible to provide an abnormality diagnosis device, an abnormality diagnosis method, and an abnormality diagnosis program for a hole drilling device that can appropriately diagnose abnormalities such as the occurrence of rifle marks due to tool wear. [Brief description of the drawings]

[0011] [Figure 1] 1 is a schematic configuration diagram of a hole drilling device according to an embodiment; [Diagram 2] FIG. 2 is an enlarged view of the tool of FIG. 1. [Diagram 3] FIG. 2 is a schematic diagram showing rifle marks that appear on an object to be processed. [Figure 4] 1 is a configuration block diagram of an abnormality diagnosis device according to an embodiment; [Diagram 5] FIG. 11 is a diagram showing an example of a change over time in phase information at a specific rotation order when no abnormality is present and normal. [Figure 6] FIG. 13 is a diagram showing an example of a change over time in phase information at a specific rotation order when an abnormality is present; [Figure 7] 4 is a flowchart illustrating an abnormality determination method according to an embodiment. [Figure 8]13 is a flowchart showing an abnormality diagnosis method according to another embodiment. [Figure 9] FIG. 9 is an explanatory diagram of step S206 in FIG. 8. [Figure 10] 13 is a flowchart showing an abnormality diagnosis method according to another embodiment. [Figure 11] 11 shows example data illustrating the phase acquired in step S306 of FIG. 10, the phase difference calculated in step S307, and the change over time in the degree of concentration calculated in step S308. [Figure 12] This is a modified example of FIG. [Figure 13] This is a schematic diagram showing the shape of a rifle mark estimated when the rotation order at which a characteristic phase trend occurs is "3". [Figure 14] FIG. 13 is a schematic configuration diagram showing a hole drilling device according to another embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, some embodiments of the present disclosure will be described with reference to the accompanying drawings. However, the dimensions, materials, shapes, relative arrangements, etc. of the configurations described as the embodiments or shown in the drawings are merely illustrative examples and are not intended to limit the scope of the present disclosure.

[0013] First, a hole drilling device to be diagnosed by an abnormality diagnosis device according to at least one embodiment of the present disclosure will be described. Fig. 1 is a schematic configuration diagram of a hole drilling device 1 according to one embodiment, and Fig. 2 is an enlarged view of a tool 8 in Fig. 1.

[0014] The hole drilling apparatus 1 is an apparatus for forming holes in a workpiece 2. The workpiece 2 may be any structure, but in this example, a plate-like member having a predetermined thickness d is shown as an example, which contains a metal material such as stainless steel or carbon steel in order to have sufficient strength. The thickness d of the workpiece 2 is set using the diameter r of the hole formed by the hole drilling apparatus 1 so that d:r≧10:1 (more preferably 20:1).

[0015] The hole drilling device 1 is a device for performing deep hole cutting processing (BTA processing) for forming deep holes in such a workpiece 2, and includes a bar member 6 that can be rotated by an actuator (not shown), and a tool 8 attached to the tip of the bar member 6. The actuator can be driven by power supplied from the outside, and drives and rotates the bar member 6 together with the tool 8 attached to the tip.

[0016] 2, the tool 8 includes a body 10, a cutting blade 12, and a guide pad 14. The body 10 is a main component of the tool 8, and has an opening 16 along the axial direction at the center. Cutting oil supplied from the outside during cutting passes through the opening 16, allowing the cutting oil to be supplied between the cutting blade 12 that cuts the workpiece.

[0017] The cutting blades 12 are provided at the tip of the main body 10, and are configured to cut the workpiece 2 by rotating the tool 8 due to the rotational power transmitted from the bar member 6. In the example of Fig. 2, a plurality of cutting blades 12 are provided along the radial direction at the tip of the main body 10.

[0018] A plurality of guide pads 14 are provided on the sides of the main body 10. When the tool 8 cuts the workpiece and digs a hole, the guide pads 14 come into contact with the inner surface of the hole, thereby guiding the tool 8 which digs a straight line through the workpiece 2. This makes it possible to precisely form a perfectly circular, relatively deep hole in the workpiece 2.

[0019] A rotation speed sensor 15 for acquiring a one-rotation pulse signal is disposed at a position facing the bar member 6 on the base 7 that rotatably supports the bar member 6 (the rotation speed sensor 15 can acquire a one-rotation pulse signal by detecting the rotation of a reflective sticker attached to the bar member 6, which is the rotation shaft, for example). An AE sensor 18 is provided on at least one of the workpiece 2 or the tool 8. In FIG. 1, an AE sensor 18a is provided at a position facing the bar member 6 on the base 7 as the AE sensor 18, but as will be described later with reference to FIG. 12, an AE sensor 18b may be provided on the back side of the workpiece 2. In other embodiments, the AE sensor may be provided on another surface, such as a side surface, of the workpiece 2. The AE signal generally includes an AE wave having a frequency in the sound wave region of several tens of kHz to several MHz.

[0020] In the hole processing device 1 having the above configuration, when the tool 8 is used repeatedly, the cutting blade 12 and the guide pad 14 are worn, and abnormalities such as machining marks called rifle marks LM may occur. FIG. 3 is a schematic diagram showing rifle marks LM generated on the workpiece 2. A hole formed by a normal tool 8 without wear is a perfect circle, but when at least a part of the cutting blade 12 or the guide pad 14 mounted on the tool 8 is worn, rifle marks LM are generated as periodic machining marks according to the worn parts. The degree of the rifle marks LM increases as the wear progresses, and if the size exceeds an allowable value, it may cause a decrease in productivity, such as redoing the processing or generating defective products. Such abnormalities including the rifle marks LM can be suitably diagnosed by the abnormality diagnosis device 100 described below.

[0021] The abnormality diagnosis device 100 is composed of, for example, a CPU (Central Processing Unit), a RAM (Random Access Memory), a ROM (Read Only Memory), and a computer-readable storage medium. A series of processes for realizing various functions is stored in a storage medium or the like in the form of a program, for example, and the CPU reads this program into the RAM or the like and executes information processing and arithmetic processing to realize various functions. The program may be installed in a ROM or other storage medium in advance, may be provided in a state stored in a computer-readable storage medium, or may be distributed via a wired or wireless communication means. The computer-readable storage medium may be a magnetic disk, a magneto-optical disk, a CD-ROM, a DVD-ROM, a semiconductor memory, or the like.

[0022] 4 is a configuration block diagram of an abnormality diagnosis device 100 according to one embodiment. The abnormality diagnosis device 100 includes an AE signal acquisition unit 102, a filter processing unit 104, a data processing unit 106, a rotation synchronous component calculation unit 108, and an abnormality determination unit 110.

[0023] The AE signal acquisition unit 102 is configured to acquire an AE signal from the AE sensor 18. The AE sensor 18 is configured to detect an AE (Acoustic Emission) signal, and is provided on at least one of the workpiece 2 or the tool 8. As described above, the AE sensor 18 may be provided so as to face the bar member 6, like the AE sensor 18a, or may be disposed on the workpiece 2, like the AE sensor 18b.

[0024] The filter processing unit 104 is configured to perform filtering on the AE signal S acquired by the AE signal acquisition unit 102 and output the filtered AE signal Sf. Specifically, the filter processing unit 104 has a filter with a predetermined frequency component as a pass band. According to verification by the present inventor, it has been found that the AE signal S including phase information for determining the occurrence of a rifle mark has an improved S / N ratio in a high frequency band of, for example, 200 kHz or more. The filter processing unit 104 can effectively improve the accuracy of abnormality determination by applying a filter with a pass band of a frequency band (200 kHz abnormality) in which the S / N ratio is improved in this way to the AE signal S.

[0025] The data processing unit 106 is configured to perform data processing on the AE signal Sf. Specifically, the data processing unit 106 performs predetermined envelope processing, resampling processing, and zero averaging processing as data processing. The envelope processing is performed on the AE signal Sf to output an AE signal Sr (a signal indicating a time change in the amplitude of the AE signal Sf) from which high-frequency components have been removed. The resampling processing performs resampling of the envelope-processed AE signal Sr at a predetermined frequency to output the resampled AE signal Sp. The average value zeroing processing performs processing on the AE signal Sp to set the average value of the AE signal Sp after resampling for each period, which is the synchronous average, to zero, and outputs the average value zeroing-processed AE signal Sz.

[0026] The rotation-synchronous component calculation unit 108 is configured to calculate the rotation-synchronous component by performing frequency analysis on the AE signal Sz using the rotation one pulse signal. The rotation-synchronous component calculation unit 108 converts the AE signal Sz, which is a time series function, into a frequency function expressed as an amplitude for each frequency by performing frequency analysis such as Fourier transform, and outputs a rotation order analysis result F in which the frequency is expressed by the rotation order. The rotation order analysis result F includes phase information at each rotation order of 1x, 2x, 3x, .... In this embodiment, a case is illustrated in which a one-rotation pulse signal is used to calculate the rotation-synchronous component, but a voltage signal or current signal representing the rotation speed may be used instead of or in addition to the one-rotation pulse signal.

[0027] The abnormality determination unit 110 determines an abnormality by analyzing the change over time in the phase information of each rotation order based on the rotation order analysis result F calculated by the rotation synchronous component calculation unit 108. Fig. 5 shows an example of the change over time in the phase information of a specific rotation order when there is no abnormality and it is normal, and Fig. 6 shows an example of the change over time in the phase information of a specific rotation order when there is an abnormality.

[0028] As shown in Fig. 5, when there is no abnormality and normal, the time change of the phase follows a random trend. On the other hand, as shown in the time period from about 80 s to 160 s in Fig. 6, when there is an abnormality, a characteristic trend appears in which the phase follows a substantially constant rate of change over time. This characteristic trend suggests that an abnormality that changes at a predetermined period, such as a rifle mark, has occurred. Therefore, the abnormality determination unit 110 can determine an abnormality based on whether or not there is a characteristic trend in the time change of the phase for each rotation order.

[0029] In one embodiment, the anomaly determination unit 110 may include a display unit for displaying the above-mentioned temporal change in phase. In this case, by displaying the temporal change in phase shown in Fig. 5 or 6 on a display unit such as a monitor, an operator can determine an anomaly based on the presence or absence of a characteristic trend (whether the graph of the temporal change in phase is a so-called striped pattern or not).

[0030] Next, a specific description will be given of an abnormality determination method implemented by the abnormality diagnostic device 100 having the above configuration. Fig. 7 is a flowchart showing an abnormality determination method according to one embodiment.

[0031] First, the abnormality diagnosis device 100 acquires an AE signal S from the AE sensor 18 and acquires a one-rotation pulse signal from the rotation speed sensor 15 (step S100). The AE signal S acquired by the AE signal acquisition unit 102 in step S100 is input to the filter processing unit 104, and becomes an AE signal Sf after filtering (step S101). The AE signal Sf is then input to the data processing unit 106, and envelope processing (step S102), resampling processing (step S103), and average value zeroing processing (step S104) are sequentially performed. To be more specific, in step S102, the AE signal Sf is subjected to envelope processing to obtain an AE signal Sr. In step S103, the AE signal Sr is subjected to resampling processing to obtain an AE signal Sp. In step S104, the AE signal Sp is subjected to average value zeroing processing to obtain an AE signal Sz.

[0032] Next, the rotation-synchronous component calculation unit 108 calculates the rotation-synchronous component by performing frequency analysis on the AE signal Sz using the one-rotation pulse signal (step S105). In step S105, the rotation-synchronous component calculation unit 108 performs frequency analysis such as Fourier transform to convert the AE signal Sz, which is a time-series function, into a frequency function expressed as amplitude for each frequency, and outputs a rotation-order analysis result F in which the frequency is expressed by the rotation order (step S106). In the rotation-order analysis result F, a phase trend graph is created for each rotation order of 1, 2, ....

[0033] Next, the abnormality determination unit 110 determines whether or not there is an abnormality based on the phase information at each rotation order, based on the rotation order analysis result F output from the rotation synchronous component calculation unit 108 (step S107). In step S107, if the temporal change in phase at each rotation order has a distinctive trend (step S107: YES), it is determined that there is an abnormality (step S108). On the other hand, if the temporal change in phase at each rotation order does not have a distinctive trend (step S107: NO), it is determined that there is no abnormality (step S109).

[0034] In another embodiment, the anomaly determination unit 110 may determine the presence or absence of a characteristic trend included in the above-mentioned temporal change in phase by using a quantitative evaluation index. In this case, by using the quantitative evaluation index, anomaly determination can be performed automatically without relying on the subjective judgment of an operator.

[0035] Here, the abnormality diagnosis method using the evaluation index will be specifically described with reference to Fig. 8. Fig. 8 is a flowchart showing an abnormality diagnosis method according to another embodiment, and Fig. 9 is an explanatory diagram of step S206 in Fig. 8. In addition, since steps S200 to S205 in FIG. 8 are the same as steps S100 to S105 in FIG. 7, respectively, a duplicated description will be omitted, and only steps S206 and after will be described.

[0036] The abnormality determination unit 110 performs fitting using a linear regression equation to the phase history (change over time) at each rotation order obtained from the rotation order analysis result F (step S206). The fitting to the linear regression equation in step S206 is performed by performing a linear regression analysis such as the least squares method on each data included in the change over time of the phase. As shown in Fig. 9, in this fitting, a linear function having a predetermined slope is obtained as an approximation equation obtained from each data.

[0037] Next, the abnormality determination unit 110 obtains the amount of deviation from the linear regression equation obtained in step S206 for each data included in the temporal change in phase, and calculates an evaluation index from the sum of the deviations (step S207). In step S207, the evaluation index is obtained as, for example, the sum of squared residuals or the sum of absolute residuals for the approximation line of each data. The data for which the evaluation index is calculated may include not only the data used in the fitting, but also the data not used in the fitting.

[0038] Next, the anomaly determination unit 110 determines an anomaly by comparing the evaluation index calculated in S207 with a preset threshold value (step S208). When no characteristic trend is observed in the temporal change of the phase (i.e., when it is random as shown in FIG. 5), the deviation of each data from the approximation line becomes large, and the evaluation index becomes large. On the other hand, when a characteristic trend is observed in the temporal change of the phase (i.e., when a striped pattern is observed as shown in FIG. 6), the deviation of each data from the approximation line becomes small, and the evaluation index becomes small. In consideration of such a tendency of the evaluation index, the anomaly determination unit 110 determines that an anomaly exists when the evaluation index is equal to or smaller than the threshold value (step S209). On the other hand, the anomaly determination unit 110 determines that an anomaly does not exist when the evaluation index is larger than the threshold value (step S210).

[0039] The threshold value to be compared with the evaluation index can be determined by a test, logical, or simulation method. The threshold value may be set to a different value for each rotation order, or may be set in common to different rotation orders.

[0040] As described above with reference to Fig. 9, when calculating the sum of deviations from the linear regression equation as an evaluation index for performing an anomaly determination in the anomaly determination unit 110, it is necessary to perform a fitting calculation on data related to temporal changes in phase in order to specify the linear regression equation. Such a fitting calculation requires a sufficiently large number of data to accurately calculate the linear regression equation, but this increases the calculation load, which may make it difficult to perform an anomaly determination in real time. Such a problem can be preferably solved by the following embodiment.

[0041] FIG. 10 is a flowchart showing an abnormality diagnosis method according to another embodiment, and FIG. 11 shows example data showing the change over time of the phase acquired in step S306 of FIG. 10, the change over time of the phase difference calculated in step S307, and the change over time of the concentration degree calculated in step S308. In addition, since steps S300 to S305 in FIG. 10 are the same as steps S100 to S105 in FIG. 7, respectively, a duplicated description will be omitted, and only step S306 and thereafter will be described.

[0042] The abnormality determination unit 110 acquires a change in phase over time based on a trend graph of the phase created from the rotation order analysis result F obtained based on the rotation-synchronous component calculated in step S305 (step S306). An example of the change in phase over time is shown in the upper part of Fig. 11. This change in phase over time includes first phase data and second phase data corresponding to different times. For example, the first phase data is phase data corresponding to the current time, and the second phase data is phase data corresponding to a time earlier than the first phase data.

[0043] Next, the abnormality determination unit 110 calculates a change in the phase difference over time from the change in the phase over time acquired in step S306 (step S307). The phase difference is calculated based on the first phase data and the second phase data included in the change in the phase over time. Specifically, the phase difference is calculated as the difference between the first phase data and the second phase data. An example of the change in the phase difference over time is shown in the middle part of FIG.

[0044] Here, if the number of data included in the time change of the phase acquired in step S306 is M, the second phase data is specified as data m before the first phase data. m is a natural number equal to or less than M, and a specific value can be set arbitrarily, but when m is small (i.e., when the first phase data and the second phase data correspond to times close to each other), the influence of noise components, etc. included in the time change of the phase becomes large, and the accuracy is likely to decrease. On the other hand, when m is large (i.e., when the first phase data and the second phase data correspond to times distant from each other), the influence of noise components, etc. included in the time change of the phase can be relatively small, but the phase difference between phase data distant in time is calculated, which is disadvantageous in terms of real-time performance. In this way, it is preferable to set the value of m appropriately, taking into consideration the balance between accuracy and real-time performance.

[0045] Next, the abnormality determination unit 110 calculates a concentration degree in a predetermined period as an evaluation index for the time change of the phase difference calculated in step S307 (step S308). The concentration degree is an index related to the degree of variation of each data included in the time change of the phase difference included in the predetermined period. An example of the concentration degree is shown in the lower part of FIG. 11. In this example, before time ta, each data included in the time change of the phase acquired in step S306 is randomly distributed, and accordingly, each data included in the time change of the phase difference calculated in step S307 is also randomly distributed, so the concentration degree shows a small value (a value close to 0). On the other hand, in the period from time ta to tb, each data included in the time change of the phase acquired in step S306 is distributed in a striped pattern, and therefore, each data included in the time change of the phase difference calculated in step S307 is unevenly distributed in a predetermined range (in the example of FIG. 11, near the phase difference of 0°), so the concentration degree shows a high value (a value close to 1). After time tb, the concentration level indicates a small value (close to 0) as before time ta.

[0046] The calculation of the degree of concentration in step S308 is performed sequentially based on a preset number of data from the most recent data among the temporal changes in phase difference calculated in step S307 (for example, the calculation is performed using the most recent n pieces of data (n is an arbitrary natural number equal to or less than the total number of data included in a predetermined period) from the most recent data. Although a specific calculation method is not limited, for example, the following equation is defined using n pieces of phase difference data θ1, θ2, ... θn included in a predetermined period among the temporal changes in phase difference calculated in step S307. TIFF2024178901000002.tif51170

[0047] Next, the abnormality determination unit 110 determines whether the concentration degree calculated in step S308 is greater than a threshold value (step S309). As a result, if the concentration degree is equal to or greater than the threshold value (step S309: YES), the abnormality determination unit 110 determines that an abnormality exists (step S310). On the other hand, if the concentration degree is less than the threshold value (step S309: NO), the abnormality determination unit 110 determines that no abnormality exists (step S311).

[0048] The threshold value compared with the evaluation index in step S309 can be determined by a trial, logical, or simulation method. For example, the threshold value can be set based on the concentration degree calculated in a situation where an abnormality such as a rifle mark occurs on a trial basis. Specifically, a probability distribution of the concentration degree calculated at this time is created, and the threshold value can be set based on the concentration degree with a probability of a predetermined value (e.g., 99.7%) or more.

[0049] Thus, according to this embodiment, the concentration level in a predetermined period is calculated for the phase difference between two phase data corresponding to different times included in the temporal change of the phase. If the concentration level is equal to or greater than a preset threshold, it is determined that an abnormality exists. Since the concentration level, which is a quantitative index for abnormality determination, is calculated based on two phase data (first phase data and second phase data) included in the temporal change of the phase, the calculation load is smaller than that of the above-mentioned embodiment, and an abnormality determination can be performed in real time with high accuracy.

[0050] Fig. 12 is a modified example of Fig. 1. In this modified example, by providing multiple tools 8, holes can be formed simultaneously at multiple positions on the workpiece. Specifically, the hole drilling device 1 is connected to an actuator (not shown) and includes multiple bar members 6, each of which is rotatable. A tool 8 is provided at the tip of each bar member 6, as in the above-described embodiment.

[0051] In this modification, a plurality of rotation speed sensors 15 are provided at positions facing the plurality of bar members 6. An AE sensor 18a is provided on the base 7 to acquire an AE signal from each bar member 6. The AE signal from the AE sensor 18a is used for abnormality determination as in the abnormality diagnosis method described above, and an individual diagnosis result is obtained. The AE sensor 18a may be provided at a position corresponding to each of the plurality of bar members 6. In this case, it is possible to identify which tool 8 is experiencing wear based on the position of the AE sensor 18a that detected the AE signal determined to indicate an abnormality.

[0052] Furthermore, if multiple bar members 6 rotate synchronously, one rotation speed sensor 15 is sufficient, but if multiple bar members 6 rotate asynchronously, a rotation speed sensor 15 may be provided for each bar member 6, as shown in Figure 12.

[0053] Here, when the abnormality determined by the abnormality determination unit 110 is a relatively severe rifle mark, the AE signal reflecting the abnormality may be detected by a plurality of AE sensors 18 by propagating through each component of the hole drilling device 1. In this case, if the abnormality determination is made based on the AE signal from each AE sensor 18, there is a risk that a common abnormality may be determined in duplicate by different AE sensors 18 as if it were a different abnormality.

[0054] As a means for solving such a problem, the abnormality determination unit 110 may determine an abnormality based on the correlation between each AE signal. In this case, the abnormality determination unit 110 calculates the correlation between the AE signals from the multiple AE sensors 18, and when the correlation is large, determines that the abnormalities determined based on the AE signals from the AE sensors 18 are common. Furthermore, when AE signals with a large correlation are detected by the multiple AE sensors 18, the abnormality determination unit 110 may identify the location of the abnormality based on the AE sensor 18 that detected the AE signal with the large amplitude. In addition, when AE signals with such a high correlation are detected by multiple AE sensors 18, the abnormality judgment unit 110 may use the location where the abnormality has occurred as a criterion for determining whether or not the abnormality has occurred as the location where a characteristic trend (striped pattern) is more prominent or the location where the evaluation index indicates a stronger abnormality.

[0055] The correlation between multiple AE signals may be evaluated based on whether the amplitude histories of the rotation-synchronous components based on each AE signal are similar, or the coherence between each AE signal may be calculated, and if the value is close to 1, it may be evaluated that a common AE signal is being handled.

[0056] In this manner, according to this modification, it is possible to identify not only the presence or absence of an abnormality but also the location of the abnormality by using the multiple AE sensors 18. In particular, when the multiple AE sensors 18 detect AE signals resulting from the occurrence of an abnormality, it is possible to suitably identify the location of the abnormality by distinguishing between overlapping abnormalities based on the correlation between these AE signals.

[0057] In another embodiment, the abnormality determination unit 110 may estimate the shape of the rifle mark LM formed on the workpiece 2 based on the rotation order at which a characteristic phase trend (the stripe pattern exemplified in FIG. 6) occurs. As described above, the temporal change in phase is calculated for each rotation order from the rotation order analysis result F, and an abnormality is determined based on whether each of them has a characteristic phase trend. In this aspect, the shape of the rifle mark LM corresponding to the rotation order is estimated by identifying the rotation order at which the characteristic phase trend occurs.

[0058] When the rotation order is n, the shape of the rifle mark LM is estimated to be an n-sided polygon. Fig. 13 is a schematic diagram showing the shape of the rifle mark LM estimated when the rotation order at which a characteristic phase trend occurs is "3". As shown in Fig. 13, in this case, the shape of the rifle mark LM is estimated to be a triangle.

[0059] In this manner, in this embodiment, by estimating the shape of the rifle mark LM based on the rotational order at which a characteristic phase trend occurs, it is possible to understand the effect that the rifle mark LM has on the workpiece 2 and select an appropriate method of dealing with it.

[0060] 14 is a schematic diagram showing the configuration of a hole drilling device 1 according to another embodiment. In this embodiment, the AE sensor 18 is not provided on the hole drilling device 1 side as in the previously described embodiment, but is provided on the workpiece 2. Even if the AE sensor 18 cannot be provided on the hole drilling device 1 side for some reason, an abnormality can be determined in the same manner as in the previously described embodiment using the AE signal from the AE sensor 18b provided on the workpiece 2.

[0061] This embodiment is also effective in reducing the number of AE sensors 18 to reduce costs when simultaneously drilling a plurality of holes using a hole drilling device 1 that uses a plurality of tools 8 as shown in Fig. 12. In this case, by providing the AE sensor 18b on the workpiece 2 side, if an abnormality occurs in any of the plurality of tools 8, the effect of the abnormality can be detected by the AE sensor 18b provided on the workpiece 2 via the workpiece 2. This makes it possible to suitably identify abnormalities occurring in the plurality of tools 8 without providing the same number of AE sensors 18a as the tools 8.

[0062] Incidentally, in this embodiment as well, it is possible to identify which tool 8 of the hole drilling device 1 has an abnormality by installing a plurality of AE sensors 18b at different positions on the workpiece 2. In this case, the tool 8 in which an abnormality has occurred can be identified based on which AE sensor 18b, among the plurality of AE sensors 18b installed on the workpiece 2, exhibits the AE signal from which the above-mentioned characteristic phase trend appears most strongly.

[0063] In particular, when the depth of the hole formed by the hole machining device 1 is large, if the AE sensor 18b is installed on the surface of the workpiece 2 facing the hole machining device 1, the distance between the tool 8 and the AE sensor 18b increases as machining progresses, and the AE signal detected by the AE sensor 18b gradually weakens. In contrast, as shown in Fig. 14, by installing the AE sensor 18b on the surface of the workpiece 2 facing the hole machining device 1, the distance between the AE sensor 18b and the tool 8 decreases as machining progresses, and the AE signal gradually becomes stronger, thereby effectively improving the accuracy of abnormality determination.

[0064] In another embodiment, the AE sensors 18 (AE sensors 18a and 18b) may be installed on both the hole drilling device 1 and the workpiece 2. In this case, it is possible to more accurately diagnose a relatively mild rifle mark LM that may be overlooked if the AE sensor 18 (either the AE sensor 18a or 18b) is installed on only one of the hole drilling device 1 or the workpiece 2. In addition, by adding the AE signals from the AE sensors 18 installed on the hole drilling device 1 and the workpiece 2, respectively, it is expected that the noise components included in the AE signals can be suppressed and the accuracy of abnormality determination can be improved.

[0065] As described above, according to each of the above embodiments, an AE signal is acquired from the AE sensor 18 provided on the tool 8 or the workpiece 2. This AE signal contains phase information synchronized with the tool 8 that is rotationally driven when forming a hole in the workpiece 2. Therefore, by analyzing the phase information of such an AE signal, it is possible to suitably determine the occurrence of an abnormality such as a rifle mark LM that occurs in synchronization with the rotation of the tool 8. This abnormality determination is performed based on the phase information of the AE signal acquired by the AE sensor 18, and therefore can be performed in real time during processing.

[0066] In addition, within the scope of the present disclosure, the components in the above-described embodiments may be replaced with well-known components as appropriate, and the above-described embodiments may be combined as appropriate.

[0067] The contents described in each of the above embodiments can be understood, for example, as follows.

[0068] (1) An abnormality diagnosis device for a hole drilling device according to one embodiment includes: An abnormality diagnosis device for a hole drilling device including at least one rotatable tool for forming a hole in a workpiece, an AE signal acquisition unit for acquiring an AE signal from at least one AE sensor provided on at least one of the workpiece or the tool; an abnormality determination unit for determining an abnormality of the tool based on phase information of the AE signal; Equipped with.

[0069] According to the above aspect (1), an AE signal is acquired from an AE sensor provided on a tool or a workpiece. This AE signal includes phase information synchronized with the tool that is rotated when forming a hole in the workpiece. Therefore, by analyzing the phase information of such an AE signal, it is possible to suitably determine the occurrence of an abnormality such as a rifle mark that occurs in synchronization with the rotation of the tool. This abnormality determination is performed based on the phase information of the AE signal acquired by the AE sensor, and therefore can be performed in real time during processing.

[0070] (2) In another embodiment, in the above embodiment (1), The phase information includes a temporal change in phase of at least one rotation synchronous component of the AE signal, the rotation synchronous component having a synchronous frequency that is an integer multiple of a rotation frequency of the at least one tool.

[0071] According to the above aspect (2), by using the time change in phase of the rotation-synchronous component that is synchronized with the rotation frequency of the tool as phase information, it is possible to suitably determine abnormalities such as rifle marks that occur in synchronization with the rotation of the tool.

[0072] (3) In another embodiment, in the above embodiment (2), The abnormality determination unit determines the abnormality for each of the rotation synchronous components corresponding to different synchronous frequencies.

[0073] According to the above aspect (3), an abnormality is determined based on the rotation synchronous component for each different synchronous frequency that is an integer multiple of the rotation frequency of the tool, which makes it possible to individually determine various abnormalities having different rotation frequencies.

[0074] (4) In another embodiment, in the above embodiment (2) or (3), The rotation-synchronous component is obtained by performing envelope processing on the AE signal to extract vibration fluctuations, and then subjecting the fluctuations to a Fourier transform.

[0075] According to the above aspect (4), the fluctuation in vibration extracted by performing envelope processing on the AE signal is subjected to a Fourier transform, so that the rotation-synchronous component required for abnormality determination can be suitably obtained.

[0076] (5) In another embodiment, in the above embodiment (4), The envelope processing is performed on the AE signal to which a band-pass filter having a predetermined frequency band as a pass band has been applied.

[0077] According to the above aspect (5), a bandpass filter is applied to the AE signal used for abnormality determination. According to the verification by the present inventor, it has been found that the S / N ratio of the AE signal including the phase information for determining the occurrence of rifle marks is improved in a high frequency band of, for example, 200 kHz or more. By applying a bandpass filter having a passband of the frequency band where the S / N ratio is improved to the AE signal in this way, the accuracy of abnormality determination can be effectively improved.

[0078] (6) In another embodiment, in any one of the above (2) to (5), The abnormality determination unit includes a display unit for displaying a change in the phase over time.

[0079] According to the above aspect (6), the change in phase over time, which is phase information obtained from the AE signal, can be displayed on the display unit. In this case, an operator can appropriately determine an abnormality from the change in phase over time by referring to the display.

[0080] (7) In another embodiment, in any one of the above (2) to (6), The abnormality determination unit determines that the abnormality exists when an evaluation index indicating the degree of deviation of each data included in the temporal change of the phase from an approximation line obtained for the temporal change of the phase becomes equal to or smaller than a predetermined threshold value.

[0081] According to the above aspect (7), an appropriate evaluation index is obtained from the time change of the phase, which is the phase information obtained from the AE signal. This evaluation index is calculated so as to indicate the degree of deviation of each data included in the time change of the phase from the approximation line obtained for the time change of the phase. By comparing the evaluation index calculated in this way with a threshold value, it is possible to quantitatively determine anomalies without relying on the subjective judgment of the operator.

[0082] (8) In another embodiment, in the above embodiment (2), the abnormality determination unit calculates a phase difference between first phase data and second phase data corresponding to different times included in the temporal change of the phase; When the concentration of the phase difference in a predetermined period of time becomes equal to or greater than a preset threshold value, it is determined that the abnormality exists.

[0083] According to the above aspect (8), a concentration degree is calculated for a predetermined period of time for a phase difference between two phase data corresponding to different times included in the temporal change of the phase. If the concentration degree is equal to or greater than a preset threshold, it is determined that an abnormality exists. Since the concentration degree, which is a quantitative index for abnormality determination, is calculated based on two phase data (first phase data and second phase data) included in the temporal change of the phase, the calculation load is small, and an abnormality determination can be performed in real time with high accuracy.

[0084] (9) In another embodiment, in the above embodiment (8), the first phase data is phase data corresponding to a current time; The second phase data is phase data corresponding to a time earlier than the first phase data.

[0085] According to the above aspect (9), it is possible to suitably perform real-time abnormality determination with a small computational load based on the concentration of the phase difference between the first phase data corresponding to the current time and the second phase data corresponding to a time prior to the current time during a predetermined period of time.

[0086] (10) In another embodiment, in the above embodiment (8) or (9), The degree of concentration is calculated sequentially based on a preset number of data items starting from the most recent data among the temporal changes in the phase difference.

[0087] According to the above aspect (10), by calculating the concentration level based on a preset number of latest data of the time change of the phase difference that is calculated sequentially, it is possible to preferably perform real-time abnormality judgment with a small calculation load.

[0088] (11) In another embodiment, in any one of the above (8) to (10), The threshold value is set corresponding to the concentration degree of the phase difference calculated based on the AE signal acquired when the abnormality is present.

[0089] According to the above aspect (11), the threshold value to be compared with the concentration degree for abnormality determination is set in advance as the concentration degree of the phase difference calculated based on the AE signal acquired if there is a concentration degree equal to or greater than the threshold value.

[0090] (12) In another embodiment, in any one of the above (1) to (11), the at least one tool includes a plurality of tools for simultaneously drilling a plurality of the holes in the workpiece; the at least one AE sensor includes a plurality of AE sensors provided corresponding to each of the plurality of tools; The abnormality determination unit determines the abnormality for each of the plurality of AE sensors.

[0091] According to the above aspect (12), when multiple tools are used to simultaneously drill multiple holes, multiple AE sensors are provided corresponding to the multiple tools, respectively. By analyzing the AE signals from these AE sensors, it is possible to distinguish and determine the tool in which the abnormality occurred from the position of the AE sensor that has been determined to be abnormal.

[0092] (13) In another embodiment, in the above embodiment (12), a correlation index calculation unit for calculating a correlation index indicating a correlation between the AE signals from the plurality of AE sensors, The abnormality determination unit determines a location where the abnormality has occurred based on the magnitude of the correlation index.

[0093] According to the above aspect (13), it is possible to identify whether the same anomaly is determined in duplicate from the AE signals obtained by the respective AE sensors, based on the magnitude of correlation between the AE signals obtained by the respective AE sensors.

[0094] (14) In another embodiment, in any one of the above (2) to (13), The abnormality determination unit estimates a contour of the hole based on a rotation order of the rotation-synchronous component determined to have an abnormality.

[0095] According to the above aspect (14), the contour of the hole can be estimated based on the rotational order of the rotation-synchronous component determined to have an abnormality. This makes it possible to understand the effect that an abnormality such as a rifle mark has on the workpiece according to the contour of the hole, and to consider measures such as selecting an appropriate method of dealing with the hole.

[0096] (15) In another embodiment, in any one of the above (1) to (14), The at least one AE sensor is provided on the tool.

[0097] According to the above aspect (15), by providing an AE sensor directly on a tool that is a cause of an abnormality such as a rifle mark, it is possible to accurately determine the abnormality.

[0098] (16) In another embodiment, in any one of the above (1) to (14), The at least one AE sensor is provided on the workpiece.

[0099] According to the above aspect (16), abnormality judgment can be performed based on the AE signal from the AE sensor provided on the workpiece. This makes it possible to perform the same abnormality judgment as described above even when it is not possible to install an AE sensor on the tool side. Also, even when multiple tools are used to simultaneously drill multiple holes, by installing an AE sensor on the workpiece side, it is possible to perform an abnormality judgment using a small number of tools. Using an AE sensor makes it possible to diagnose abnormalities in multiple tools.

[0100] (17) In another embodiment, in any one of the above (1) to (16), The tool comprises: The main body, A cutting blade provided at a tip portion of the main body; A guide pad provided on a side of the main body; Equipped with.

[0101] According to the above aspect (17), it is possible to suitably diagnose an abnormality in a hole drilling device having a tool in which a cutting blade and a guide pad are provided on a main body.

[0102] (18) A method for diagnosing an abnormality in a hole drilling device according to one embodiment includes the steps of: A method for diagnosing an abnormality in a hole drilling device having at least one rotatable tool for forming a hole in a workpiece, comprising: acquiring an AE signal from at least one AE sensor provided on at least one of the workpiece or the tool; determining an abnormality in the tool based on phase information of the AE signal; Equipped with.

[0103] According to the above aspect (18), an AE signal is acquired from an AE sensor provided on a tool or a workpiece. This AE signal includes phase information synchronized with the tool that is rotated when forming a hole in the workpiece. Therefore, by analyzing the phase information of such an AE signal, it is possible to suitably determine the occurrence of an abnormality such as a rifle mark that occurs in synchronization with the rotation of the tool. This abnormality determination is performed based on the phase information of the AE signal acquired by the AE sensor, and therefore can be performed in real time during processing.

[0104] (19) A method for diagnosing an abnormality in a hole drilling device according to one embodiment includes the steps of: An abnormality diagnosis program for a hole processing device having at least one rotatable tool for forming a hole in a workpiece, comprising: A computer device includes: acquiring an AE signal from at least one AE sensor provided on at least one of the workpiece or the tool; determining an abnormality in the tool based on phase information of the AE signal; It is possible to execute the following.

[0105] According to the above aspect (19), an AE signal is acquired from an AE sensor provided on a tool or a workpiece. This AE signal includes phase information synchronized with the tool that is rotated when forming a hole in the workpiece. Therefore, by analyzing the phase information of such an AE signal, it is possible to suitably determine the occurrence of an abnormality such as a rifle mark that occurs in synchronization with the rotation of the tool. This abnormality determination is performed based on the phase information of the AE signal acquired by the AE sensor, and therefore can be performed in real time during processing. [Explanation of symbols]

[0106] 1 Hole processing device 2. Processing object 6 Bar member 7 base 8 Tools 10. Main Unit 12 Cutting blade 14 Guide Pad 15 Rotational speed sensor 16 Opening 18(18a, 18b) AE sensor 100 Abnormality diagnosis device 102 AE signal acquisition section 104 Filter processing section 106 Data Processing Unit 108 Rotation synchronous component calculation unit 110 Abnormality determination section LM Rifle Mark

Claims

1. An abnormality diagnosis device for a hole drilling device including at least one rotatable tool for forming a hole in a workpiece, comprising: an AE signal acquisition unit for acquiring an AE signal from at least one AE sensor provided on at least one of the workpiece or the tool; an abnormality determination unit for determining an abnormality of the tool based on phase information of the AE signal; The abnormality diagnosis device for a hole drilling device is provided.

2. 2. The abnormality diagnosis device for a hole drilling device according to claim 1, wherein the phase information includes a temporal change in the phase of at least one rotational synchronous component of the AE signal having a synchronous frequency that is an integer multiple of a rotational frequency of the at least one tool.

3. The abnormality diagnosis device for a hole drilling device according to claim 2 , wherein the abnormality determination unit determines the abnormality for each of the rotation synchronous components corresponding to different synchronous frequencies.

4. 3. The abnormality diagnosis device for a hole drilling machine according to claim 2, wherein the rotation-synchronous component is determined by performing envelope processing on the AE signal to extract a vibration fluctuation and then subjecting the fluctuation to a Fourier transform.

5. 5. The abnormality diagnosis device for a hole drilling device according to claim 4, wherein the envelope processing is performed on the AE signal to which a band-pass filter having a predetermined frequency band as a pass band has been applied.

6. 3. The abnormality diagnosis device for a hole drilling device according to claim 2, wherein the abnormality determination unit includes a display unit for displaying a change in the phase over time.

7. 3. The abnormality diagnosis device for a hole drilling device according to claim 2, wherein the abnormality determination unit determines that the abnormality exists when an evaluation index indicating a degree of deviation of each data included in the temporal change of the phase from an approximation line obtained for the temporal change of the phase becomes equal to or smaller than a preset threshold value.

8. the abnormality determination unit calculates a phase difference between first phase data and second phase data corresponding to different times included in the temporal change of the phase; 3. The abnormality diagnosis device for a hole drilling device according to claim 2, wherein the presence of the abnormality is determined when a concentration of the phase difference in a predetermined period of time is equal to or greater than a preset threshold value.

9. the first phase data is phase data corresponding to a current time; 9. The abnormality diagnosis device for a hole drilling device according to claim 8, wherein the second phase data is phase data corresponding to a time earlier than a time of the first phase data.

10. 10. The abnormality diagnosis device for a hole drilling device according to claim 8, wherein the concentration degree is sequentially calculated based on a preset number of data items starting from the most recent data among the temporal changes in the phase difference.

11. 10. The abnormality diagnosis device for a hole drilling device according to claim 8, wherein the threshold value is set corresponding to the concentration degree of the phase difference calculated based on the AE signal acquired when the abnormality is present.

12. the at least one tool includes a plurality of tools for simultaneously drilling a plurality of the holes in the workpiece; the at least one AE sensor includes a plurality of AE sensors provided corresponding to each of the plurality of tools; The abnormality diagnosis device for a hole drilling device according to claim 1 , wherein the abnormality determination unit determines the abnormality for each of the plurality of AE sensors.

13. A correlation index calculation unit is further provided for calculating a correlation index indicating a correlation between the AE signals from the plurality of AE sensors, The abnormality diagnosis device for a hole drilling device according to claim 8 , wherein the abnormality determination unit determines a location where the abnormality has occurred based on a magnitude of the correlation index.

14. 3. The abnormality diagnosis device for a hole drilling device according to claim 2, wherein the abnormality determination unit estimates a contour of the hole based on a rotation order of the rotation-synchronous component determined to have the abnormality.

15. The abnormality diagnosis device for a hole drilling device according to claim 1 or 2, wherein the at least one AE sensor is provided on the tool.

16. 3. The abnormality diagnosis device for a hole drilling device according to claim 1, wherein the at least one AE sensor is provided on the workpiece.

17. The tool comprises: The main body, A cutting blade provided at a tip portion of the main body; A guide pad provided on a side of the main body; The abnormality diagnosis device for a hole drilling device according to claim 1 or 2, comprising:

18. A method for diagnosing an abnormality in a hole drilling device having at least one rotatable tool for forming a hole in a workpiece, comprising: acquiring an AE signal from at least one AE sensor provided on at least one of the workpiece or the tool; determining an abnormality in the tool based on phase information of the AE signal; The abnormality diagnosis method for a hole drilling device includes the steps of:

19. 1. An abnormality diagnosis program for a hole drilling device having at least one rotatable tool for forming a hole in a workpiece, comprising: A computer device includes: acquiring an AE signal from at least one AE sensor provided on at least one of the workpiece or the tool; determining an abnormality in the tool based on phase information of the AE signal; An abnormality diagnosis program for hole drilling equipment that can execute the above.

Images