DETECTION DEVICE

Abstract

Detection device comprising the following: - a data reference device configured to obtain first surface data and second surface data of a long body having a periodic pattern on its surface, wherein the data reference device is configured to obtain the first surface data and the second surface data at different positions in a longitudinal direction of the long body in a direction orthogonal to the longitudinal direction of the long body; - a storage device (9) configured to store first reference data and second reference data, wherein the first reference data and the second reference data have the same period as the period of the pattern formed on the surface of the long body in a direction orthogonal to the longitudinal direction of the long body, wherein the second reference data is shifted from the phase of the first reference data; - a similarity calculation device (11) configured to calculate a first similarity between the first surface data obtained by means of the data reference device and the first reference data, a second similarity between the first surface data obtained by means of the data reference device and the second reference data, a third similarity between the second surface data obtained by means of the data reference device and the first reference data, and a fourth similarity between the second surface data obtained by means of the data reference device and the second reference data; - a phase calculation device (12) configured to calculate as a first phase a deflection angle of a first similarity vector which has as elements the first similarity and the second similarity calculated by means of the similarity calculation device (11), and to calculate as a second phase a deflection angle of a second similarity vector which has as elements the third similarity and the fourth similarity calculated by means of the similarity calculation device (11); - a period calculation device (13) configured to calculate a period of a pattern formed on the long body, based on the first phase and the second phase calculated by means of the phase calculation device (12); and - a first anomaly detection device (14) configured to calculate an anomaly in the long body on the basis of the period calculated by the period calculation device (13).

Description

Technical area

[0001] The present invention relates to a detection device for detecting a long body. background

[0002] Patent 1 describes a device for testing a rope. The device described in Patent 1 comprises a light source and a light-receiving element. A rope is arranged between the light source and the light-receiving element. In the device described in Patent 1, the diameter of a rope is calculated based on the amount of light received by the light-receiving element. The position of the rope is calculated by matching the distances between the peak values ​​of the calculated diameter with the distances between strands or strands.

[0003] PTL2 discloses a detector equipped with a sensor head, a storage unit, a data processing unit, a similarity detection unit, and a phase calculation unit. The sensor head acquires surface data about an elongated body. The storage unit stores first reference data and second reference data. The data processing unit generates processing data from the surface data. The similarity degree calculation unit calculates a first similarity degree and a second similarity degree. The phase calculation unit calculates the phase of a similarity vector having the first similarity degree and the second similarity degree as components. BibliographyPatent literature [PTL 1] WO 2011 / 108 173 A1 [PTL 2] WO 2016 / 157 290 A1 SummaryTechnical problem

[0004] When the moving speed of a rope fluctuates, the device described in Patent 1 is unable to distinguish between the moving speed of the rope and the fluctuation of a strand pitch of the rope. In other words, the detection method used by the device described in Patent 1 is as follows: When the moving speed of a rope fluctuates, an abnormality that has occurred in the rope, such as a pitch anomaly, cannot be detected.

[0005] The present invention was conceived to solve problems such as the one described above. It is an object of the present invention to provide a detection device capable of detecting an abnormality in a long body, even when the moving speed of the long body fluctuates. Solution to the problem

[0006] The object underlying the invention is achieved by a detection device having the features of independent patent claim 1 and independent patent claim 8, respectively. Advantageous developments of the detection device according to the present invention are specified in dependent claims 2 to 7 and 9 to 11, respectively. Advantageous effects of the invention

[0007] For example, a detection device according to the present invention includes a similarity calculation device, a phase calculation device, a period calculation device, and a first anomaly detection device. The phase calculation device calculates a deflection angle of a first similarity vector as a first phase and calculates a deflection angle of a second similarity vector as a second phase. The period calculation device calculates a period of a pattern formed on a long body based on the first phase and the second phase calculated by the phase calculation device.

[0008] The first anomaly detection device detects an anomaly in the long body based on the period calculated by the period calculation device. With the detection device according to the present invention, an anomaly in a long body can be detected even when the moving speed of the long body fluctuates. Short description of the drawings Fig. 1 is a diagram showing an example of a detection device according to a first embodiment of the present invention. Fig. 2 is a diagram showing a long body from the direction of a Fig. 1 shows arrow A. Fig. 3 is a diagram showing a processing method of light receiving images obtained from the light receiving elements. Fig. Figure 4 is a diagram showing an example of a controller. Fig. 5 is a diagram illustrating a function of a data processing unit. Fig. Figure 6 is a graph showing an example of reference data. Fig. 7 is a diagram illustrating a function of a phase calculation unit. Fig. 8 is a flowchart showing an operation example of the detection device according to the first embodiment of the present invention. Fig. 9 is a diagram illustrating another example of a sensor head. Fig. 10 is a diagram showing a processing method of a light-receiving image acquired from a light-receiving element. Fig. 11 is a diagram illustrating another example of the sensor head. Fig. 12 is a diagram illustrating another example of the sensor head. Fig. 13 is a diagram showing a processing method of data of an image photographed by a camera. Fig. 14 is a diagram showing an example of control according to a second embodiment of the present invention. Fig. 15 is a flowchart showing an operation example of the detection device according to the second embodiment of the present invention. Fig. 16 is a diagram illustrating a function of a selection unit. Fig. 17 is a diagram showing an example of the detection device according to a third embodiment of the present invention. Fig. 18 is a diagram showing long bodies, from the direction of an arrow A, as in Fig. 17 shown. Fig. 19 is a diagram showing a processing method of a light-receiving image acquired from a light-receiving element. Fig. 20 is a diagram showing an example of the control. Fig. 21 is a diagram illustrating a function of the phase calculation unit. Fig. Figure 22 is a diagram showing the hardware configuration of the controller. Description of embodiments

[0009] The present invention will be described with reference to the accompanying drawings. Redundant descriptions are simplified or omitted where appropriate. In the respective drawings, like reference numerals indicate like or corresponding elements. First embodiment

[0010] Fig. Figure 1 is a diagram showing an example of a detection device according to the first embodiment of the present invention. The detection device is a device for detecting an abnormality in a long body. Examples of the long body include a rope 1. Fig. 2 is a diagram showing the long body from the direction of a Fig. 1 shows arrow A.

[0011] To simplify the description, the x-axis, y-axis and z-axis are configured as shown in Fig. 1 and Fig. 2. The y-axis is an axis aligned in the longitudinal direction of the long body. The x-axis is orthogonal to the y-axis and the z-axis. The z-axis is orthogonal to the y-axis and the x-axis. The x-axis, y-axis, and z-axis are configured to represent the coordinates in three-dimensional space. Fig. 2 corresponds to a diagram in which the long body is viewed from the +z direction.

[0012] The long body moves in the longitudinal direction. For example, rope 1 moves in the +y direction or the -y direction. An example of a rope 1 moving in this way is a wire rope used in an elevator. Rope 1 can move in both the +y direction and the -y direction. Note that the long body that is a detection object of the detection device is not limited to rope 1.

[0013] The rope 1 comprises a plurality of strands or strands. The rope 1 is formed by twisting or winding a plurality of strands together. Therefore, the rope 1 has a periodic pattern on its surface. An object whose anomaly is to be detected by the present detection device is a long body with a periodic pattern on its surface. For example, "patterns" include shapes, graphics, colors, and color gradients. Fig. 1 and Fig. 2 shows an example in which the rope 1 is formed by twisting eight strands together. Irregularities formed by twisting the majority of strands together are regularly or evenly distributed on the surface of the rope 1.

[0014] An ideal rope 1 has a cross-sectional shape that is the same at every pitch, which is obtained by dividing a twist pitch by the number of strands. The cross-section described above is a cross-section in the direction orthogonal to the longitudinal direction of the rope 1. The pitch obtained by dividing the twist pitch by the number of strands is a strand pitch, or in other words, a period of the pattern.

[0015] In the example described in the present embodiment, the detection device calculates the period of the pattern formed on the elongated body and determines the presence or absence of an anomaly. For example, the detection device includes a sensor head 2 and a controller 3.

[0016] The sensor head 2 is an example of a device that acquires surface data of the elongated body. "Surface data" refers to data related to the pattern on the surface of the elongated body. In the example described in the present embodiment, the sensor head 2 simultaneously acquires the surface data of two locations on the elongated body. For example, the sensor head 2 acquires data representing irregularities formed on a surface of a region passing as a first position of the rope 1 as first surface data. At the same time, the sensor head 2 acquires data representing irregularities formed on a surface of a region passing as a second position of the rope 1 as second surface data.

[0017] The second position is a position that is different from the first position. For example, the second position is a position at a certain distance from the first position in the y-axis direction. Fig. Figure 1 shows an example in which the sensor head 2 is an optical profile measuring instrument. For example, the sensor head 2 includes a light source 4, a light source 5, a light receiving element 6, and a light receiving element 7.

[0018] The light source 4 irradiates the surface of the rope 1 with light. Fig. 1 and Fig. 2 show an example in which the light source 4 emits a laser beam in a direction orthogonal to the longitudinal direction of the rope 1. The light emitted from the light source 4 impinges on the surface of the area passing the first position in the rope 1. In the Fig. 1 and Fig. In the example shown in Figure 2, the following applies: The light 4 emitted by the light source impinges linearly, from one end on one side to one end on the other side of the rope 1, so that it sweeps over the rope 1.

[0019] Light source 5 irradiates the surface of rope 1 with light. Light source 5 emits light parallel to the light emitted by light source 4. The time when light source 5 emits light is the same as the time when light source 4 emits light. Fig. 1 and Fig. 2 show an example in which the light source 5 emits a laser beam in a direction orthogonal to the longitudinal direction of the rope 1. The light emitted from the light source 5 strikes the surface of the area passing the second position in the rope 1.

[0020] In other words, the light emitted by the light source 5 strikes the rope 1 at a position that is separated by a certain distance in the direction of the y-axis from the position where the light from the light source 4 strikes the rope 1. In the Fig. 1 and Fig. 2, the following applies: The light emitted by the light source 5 impinges linearly, from one end on one side to one end on the other side of the rope 1, so that it sweeps over the rope 1.

[0021] The light receiving element 6 receives the light reflected from the surface of the rope 1 between the light emitted by the light source 4. The light receiving element 6 is arranged obliquely with respect to the direction in which the light source 4 emits the light. The light receiving element 6 receives the light reflected obliquely at a certain angle with respect to the longitudinal direction of the rope 1 between the light from the light source 4 reflected from the surface of the rope 1.

[0022] The light receiving element 7 receives the light reflected from the surface of the rope 1 between the light emitted by the light source 5. The light receiving element 7 is arranged obliquely with respect to the direction in which the light source 5 emits the light. The light receiving element 7 receives the light reflected obliquely at a certain angle with respect to the longitudinal direction of the rope 1 between the light from the light source 5 reflected from the surface of the rope 1. For example, the light receiving element 7 receives the light reflected from the surface of the rope 1 at the same angle as the light received by the light receiving element 6.

[0023] The light a, as in Fig. 1 and Fig. 2 is light emitted toward the rope 1 from the light source 4. For example, light a strikes the surface of the rope 1 at y = L1. Light b and light c are reflected at an angle at which the light is received by the light receiving element 6, namely, light a reflected from the surface of the rope 1. Light b is light reflected from an outermost bulge portion of a strand. Light c is light reflected from a depression portion formed by adjacent strands. When the light receiving element 6 receives light b, light c, and the like, the sensor head 2 acquires data representing a cross-sectional shape of the portion struck by the light from the light source 4 as first surface data.

[0024] The light d, as in Fig. 1 and Fig. 2 is light emitted toward the rope 1 from the light source 5. For example, the light d is incident on the surface of the rope 1 at y = L2. The light e and the light f are reflected at an angle at which the light is received by the light receiving element 7, namely, the light d reflected from the surface of the rope 1. The light e is light reflected from an outermost bulge portion of a strand. The light f is light reflected from a depression portion formed by adjacent strands. When the light receiving element 7 receives the light e, the light f, and the like, the sensor head 2 acquires data representing a cross-sectional shape of the portion struck by the light from the light source 5 as second surface data.

[0025] Fig. Fig. 3 is a diagram showing a processing method of light-receiving images obtained from the light-receiving element 6 and the light-receiving element 7. The upper half of Fig. 3 shows a light receiving image of the light receiving element 6 and a light receiving image of the light receiving element 7. The lower half of Fig. 3 shows first surface data P1 converted from the light-receiving image of the light-receiving element 6, and second surface data P2 converted from the light-receiving image of the light-receiving element 7. The abscissa in the lower half of Fig. Figure 3 shows that each of the first surface data P1 and the second surface data P2 includes a plurality of data elements in the x-direction. The number of data elements included in the surface data is arbitrarily determined.

[0026] In the example described in the present embodiment, the controller 3 detects a distance measurement anomaly that has occurred in the rope 1 based on the first surface data and the second surface data acquired by the sensor head 2. In other words, the controller 3 detects a period anomaly in the pattern formed on the surface of the elongated body. Fig. Figure 1 shows an example in which the controller 3 is connected to the sensor head 2 via a signal line 8. The sensor head 2 and the controller 3 can be arranged within the same housing. Some of the functions contained in the controller 3 can be contained in the sensor head 2.

[0027] Fig. 4 is a diagram showing an example of the controller 3. The controller 3 includes, for example, a storage unit 9, a data processing unit 10, a similarity calculation unit 11, a phase calculation unit 12, a period calculation unit 13, and an anomaly detection unit 14.

[0028] Two pieces of reference data are stored in the storage unit 9. In the following description, one of the pieces of reference data stored in the storage unit 9 is referred to as first reference data. The other piece of reference data stored in the storage unit 9 is referred to as second reference data.

[0029] The data processing unit 10 processes the first surface data received from the sensor head 2 and outputs the processed data as final first surface data. The data processing unit 10 processes the second surface data received from the sensor head 2 and outputs the processed data as final second surface data. In the example described in the present embodiment, the data processing unit 10 forms part of the device that acquires surface data of the long body.

[0030] To detect a distance measurement anomaly in the rope 1, the data obtained by removing specific frequency components from the surface data acquired by the sensor head 2 is desirably adopted as the final surface data. Performing such data processing allows one component of the periodic pattern on the surface of the rope 1 to be enhanced.

[0031] Fig. 5 is a diagram illustrating the function of the data processing unit 10. Fig. 5 shows the first surface data P1 and the second surface data P2 after the data processing has been carried out by the data processing unit 10. For example, the data processing unit 10 obtains the first surface data P1 shown in Fig. 5 by performing a removal process for low frequency components on the first surface data P1 shown in Fig. 3 are shown.

[0032] For example, the data processing unit 10 obtains the second surface data P2, which in Fig. 5 by performing a removal process for low frequency components on the second surface data P2 shown in Fig. 3. In the Fig. In the example shown in Figure 5, the effect of a diameter of the rope 1 is removed from the first surface data P1 and the second surface data P2, which are shown in Fig. 3 are shown.

[0033] The function of the data processing unit 10 can be included in the sensor head 2. Furthermore, devices that acquire long-body surface data do not need to include the function of the data processing unit 10. In the example described in the present embodiment, the output from the data processing unit 10 constitutes the final output from a device that acquires long-body surface data. If the data processing unit 10 is not provided, the output from the sensor head 2 constitutes the final output from the device that acquires long-body surface data.

[0034] Light is emitted simultaneously from the light source 4 and the light source 5. The first surface data P1 obtained at time t at position L1 can be expressed as P(t, L1). The second surface data P2 obtained at the same time t at position L2 can be expressed as P(t, L2). The first surface data P(t, L1) and the second surface data P(t, L2) can be expressed by a matrix with n rows and 1 column, as described below. For example, n denotes an integer equal to or greater than 2. Fig. Figure 5 shows an example where n = 150. P(t,L1)=(p1(t,L1)p2(t,L1)⋮pn(t,L1))P(t,L2)=(p1(t,L2)p2(t,L2)⋮pn(t,L2))

[0035] Fig. 6 is a document showing an example of reference data. As described above, the rope 1 has a periodic pattern on its surface. For example, a sine wave having the same period as the period of the pattern formed on the surface of the rope 1 is stored in the storage unit 9 as first reference data. A cosine wave having the same period as the period of the pattern formed on the surface of the rope 1 is stored in the storage unit 9 as second reference data. The first reference data Ref1 and the second reference data Ref2 can be expressed by a matrix with n rows and 1 column, as described below. Fig. Figure 6 shows an example where n = 150. Re f1=(r11r12⋮r1n) Re f2=(r21r22⋮r2n)

[0036] In the present embodiment, an example is described in which the first surface data P(t, L1), the second surface data P(t, L2), the first reference data Ref1, and the second reference data Ref2 are data from multidimensional vectors, or in other words, vectors with n elements. The scalar product of the first reference data Ref1 and the second reference data Ref2 is preferably 0, as in the Fig. Example shown in 6.

[0037] The first reference data Ref1 and the second reference data Ref2, which are in Fig. 6 are sine waves in an orthogonal relationship. However, the inner product of the first reference data Ref1 and the second reference data Ref2 need not be 0. The first reference data Ref1 and the second reference data Ref2 are not limited to the Fig. 6 example shown.

[0038] The similarity calculation unit 11 calculates a similarity between the surface data and the reference data. For example, the similarity calculation unit 11 calculates a first similarity, a second similarity, a third similarity, and a fourth similarity. The first similarity denotes a similarity between the first surface data output from the data processing unit 10 and the first reference data stored in the storage unit 9. The second similarity denotes a similarity between the first surface data output from the data processing unit 10 and the second reference data stored in the storage unit 9.

[0039] The third similarity refers to a similarity between the second surface data output from the data processing unit 10 and the first reference data stored in the storage unit 9. The fourth similarity refers to a similarity between the second surface data output from the data processing unit 10 and the second reference data stored in the storage unit 9.

[0040] For example, the similarity calculation unit 11 calculates, as a first similarity, a correlation coefficient ρ1 (t, L1) between the first surface data P (t, L1) and the first reference data Ref1. The similarity calculation unit 11 calculates, as a second similarity, a correlation coefficient p2 (t, L1) between the first surface data P (t, L1) and the second reference data Ref2. The similarity calculation unit 11 calculates, as a third similarity, a correlation coefficient ρ1 (t, L2) between the second surface data P (t, L2) and the first reference data Ref1. The similarity calculation unit 11 calculates, as a fourth similarity, a correlation coefficient p2 (t, L2) between the second surface data P (t, L2) and the second reference data Ref2.

[0041] Fig. 7 is a diagram illustrating a function of the phase calculation unit 12. The phase calculation unit 12 calculates a deflection angle of a similarity vector S as a phase θ. In the example described in the present embodiment, the phase calculation unit 12 calculates a phase θ(t, L1) of a similarity vector S(t, L1) related to the first surface data. The similarity vector S(t, L1) is a vector having, as elements, the correlation coefficient ρ1(t, L1) and the correlation coefficient p2(t, L1), which are calculated by the similarity calculation unit 11. The phase θ(t, L1) is a deflection angle of the similarity vector S(t, L1).

[0042] Furthermore, the phase calculation unit 12 calculates a phase θ(t, L2) of a similarity vector S(t, L2) associated with the second surface data. The similarity vector S(t, L2) is a vector having, as elements, the correlation coefficient ρ1(t, L2) and the correlation coefficient p2(t, L2), which are calculated by the similarity calculation unit 11. The phase θ(t, L2) is a deflection angle of the similarity vector S(t, L2).

[0043] Fig. Figure 7 shows an example in which the similarity vector S(t, L1) and the similarity vector S(t, L2) are plotted on a plane, with a similarity to the first reference data Ref1 as the abscissa and a similarity to the second reference data Ref2 as the ordinate, which is orthogonal to the abscissa. For example, when the rope 1 moves in the -y direction, the similarity vector S(t, L1) and the similarity vector S(t, L2) rotate in the B direction, as shown in Fig. 7 shown.

[0044] The locus of the similarity vector S(t,L1) is a circular locus with the maximum radius of 1. Similarly, the locus of the similarity vector S(t,L2) is a circular locus with a maximum radius of 1. When the rope 1 moves precisely by a distance corresponding to a strand distance measure, the similarity vector S(t,L1) and the similarity vector S(t,L2) perform a rotation and a rotation, respectively.

[0045] The period calculation unit 13 calculates the period of the pattern formed on the surface of the rope 1. In the example described in the present embodiment, the period corresponds to the strand pitch of the rope 1, as described above. As shown in Fig. As shown in Figure 7, the starting point of the similarity vector S is the origin.

[0046] The endpoint of the similarity vector S is a point that has, as coordinates, the elements of the similarity vector, or, in other words, two similarities calculated by the similarity calculation unit 11. The phase θ represents the direction of the similarity vector S. The period calculation unit 13 can calculate the strand spacing SP of the rope 1 according to the following equation. SP=2π×dydθ=2π(L2−L1)θ(t,L2)−θ(t,L1)

[0047] As shown in the above equation, the period calculation unit 13 calculates the strand pitch SP based on the phase θ (t, L1) and the phase θ (t, L2) calculated by the phase calculation unit 12. For example, the period calculation unit 13 obtains the strand pitch SP by calculating a rate of position change of a phase corresponding to a position change in the y-axis direction.

[0048] The anomaly detection unit 14 detects an anomaly that has occurred in the rope 1. For example, the anomaly detection unit 14 detects a period anomaly in the pattern formed on the long body based on the period calculated by the period calculation unit 13. For example, a reference range for determining that the period of the pattern is normal is stored in advance in the storage unit 9. The anomaly detection unit 14 determines that a distance measure anomaly has not occurred in the rope 1 if the period calculated by the period calculation unit 13 is within the reference range. The anomaly detection unit 14 determines that a distance measure anomaly has not occurred in the rope 1 if the period calculated by the period calculation unit 13 is not within the reference range.

[0049] Fig. 8 is a flowchart showing an operation example of the detection device according to the first embodiment of the present invention. Fig. Figure 8 shows the processing flow described above. If a distance measurement anomaly is detected by the anomaly detection unit 14, an alarm can be issued by the controller 3.

[0050] With the example described in the present invention, an anomaly in the rope 1 can be detected even when the moving speed of the rope 1 fluctuates. The present detection device also has the advantage of being resistant to interference.

[0051] Other functions that may be provided with the present detection device are described below.

[0052] The controller 3 may further include a speed calculation unit 15 and a position calculation unit 16. The speed calculation unit 15 calculates a speed at which the rope 1 moves. The speed calculation unit 15 may calculate the moving speed V of the rope 1 at a time t according to the following equation. V=SP×12π×dθdt=dθdtdθdy=θ(t,L1)−θ(t−Δt,L1)t−Δtθ(t,L2)−θ(t−Δt,L1)L2−L1

[0053] For example, the Fig. 8 are repeatedly performed at constant intervals. Δt in the above equation denotes a time interval at which the surface data is acquired. As shown in the above equation, the speed calculation unit 15 calculates the moving speed of the rope 1 based on the phase θ (t, L1) and the phase θ (t, L2) calculated by the phase calculation unit 12. For example, the speed calculation unit 15 obtains the moving speed of the rope 1 by calculating the change in the phase θ (t, L1) or the phase θ (t, L2) according to the elapsed time.

[0054] The position calculation unit 16 calculates the position of an anomaly that has occurred in the rope 1. The position calculation unit 16 calculates the above-described position based on the moving speed V of the rope 1, which is calculated by the speed calculation unit 15. For example, the position calculation unit 16 can determine how much the rope 1 has moved from a position where the acquisition of the surface data was started by integrating the moving speed V of the rope 1 calculated by the speed calculation unit 15.

[0055] The position calculation unit 16 calculates the position of a detected anomaly on the rope 1 based on a moving distance at the moment the anomaly is detected by the anomaly detection unit 14. With the calculation method described above, a position can be calculated even if the strand distance of the rope 1 is unknown or fluctuates.

[0056] The controller 3 may also include a shutdown unit or deactivation unit 17. The deactivation unit 17 deactivates the anomaly detection by the anomaly detection unit 14. As shown in Fig. As shown in Figure 7, the locus of the similarity vector S(t, L1) has a circular shape. If irregularities formed by the multiple strands being twisted together are carefully arranged on the surface of the rope 1, the locus of the similarity vector S(t, L1) continuously follows a similar circle with the origin as its center.

[0057] In turn, when an anomaly in signal transmission occurs due to the inclusion of non-negligible large noise or the like in a signal received by the controller 3, the locus of the similarity vector S(t, L1) changes to approach the origin. By presetting a normal range with respect to a norm of the similarity vector S(t, L1), a decrease in the reliability of the acquired surface data can be detected. The normal range is set, for example, to 0.3 to 1. A similar description applies to the similarity vector S(t, L2).

[0058] For example, the deactivation unit 17 deactivates the function of the anomaly detection unit 14 for detecting anomalies based on the norm of the similarity vector S(t, L1). If the norm of the similarity vector S(t, L1) is outside the normal range, the deactivation unit 17 prevents anomalies from being detected by the anomaly detection unit 14. Alternatively, the deactivation unit 17 may deactivate the function of the anomaly detection unit 14 for detecting anomalies based on the norm of the similarity vector S(t, L2).

[0059] For example, if the norm of the similarity vector S(t, L2) is outside the normal range, the deactivation unit 17 prevents anomalies from being detected by the anomaly detection unit 14. Alternatively, the deactivation unit 17 may disable anomaly detection by the anomaly detection unit 14 if both the norm of the similarity vector S(t, L1) and the norm of the similarity vector S(t, L2) are outside the normal ranges.

[0060] While the anomaly detection function of the anomaly detection unit 14 is deactivated by the deactivation unit 17, the position calculation unit 16 can calculate a moving distance of the rope 1 using a strand distance measure SP calculated immediately before. Accordingly, appropriate interpolation can be performed.

[0061] The controller 3 may also include an anomaly detection unit 18.

[0062] The anomaly detection unit 18 detects an anomaly in the rope 1 that differs from the anomaly detected by the anomaly detection unit 14. For example, the anomaly detection unit 18 detects an anomaly in the pattern formed on the surface of the rope 1.

[0063] As described above, the locus of the similarity vector S(t, L1) has a circular shape. If no abnormality in signal transmission has occurred, the locus of the similarity vector S(t, L1) continuously follows a similar circle with the origin as its center, as long as the irregularities formed by twisting the plurality of strands together are carefully arranged on the surface of the rope 1.

[0064] Conversely, if a twist defect occurs due to the strand intervals becoming uneven or the like, the locus of the similarity vector S(t, L1) changes to approach the origin. Therefore, by predetermining a normal range with respect to the norm of the similarity vector S(t, L1), the occurrence of an anomaly in the pattern formed on the surface of the rope 1 can be detected. The normal range is set, for example, to 0.6 to 1. A similar description applies to the similarity vector S(t, L2).

[0065] For example, the anomaly detection unit 18 detects that an anomaly has occurred in the pattern of rope 1 based on the norm of the similarity vector S(t, L1). The anomaly detection unit 18 detects that an anomaly has occurred in the pattern of rope 1 when the norm of the similarity vector S(t, L1) is outside the above-described normal range. With the example described in the present embodiment, the anomaly detection unit 18 detects a shape anomaly in rope 1.

[0066] The anomaly detection unit 18 can detect that an anomaly has occurred in the pattern of rope 1 based on the norm of the similarity vector S(t, L2). For example, the anomaly detection unit 18 detects a shape anomaly in rope 1 when the norm of the similarity vector S(t, L2) is outside the normal range described above. Alternatively, the anomaly detection unit 18 can detect a shape anomaly in rope 1 when both the norm of the similarity vector S(t, L1) and the norm of the similarity vector S(t, L2) are outside the normal ranges.

[0067] When an anomaly in the pattern is detected by the anomaly detection unit 18, data enabling the anomaly to be confirmed may be stored in the storage unit 9. For example, when an anomaly in the pattern is detected by the anomaly detection unit 18, surface data used to detect the anomaly is stored in the storage unit 9. As described later, the sensor head 2 may include a camera for acquiring surface data. When an anomaly in the pattern is detected by the anomaly detection unit 18, data of an image photographed by the camera may be stored in the storage unit 9.

[0068] Fig. Fig. 9 is a diagram illustrating another example of the sensor head 2. For example, the sensor head 2 shown in Fig. 9, the light source 4, the light source 5 and the light receiving element 6. Fig. 9 shows an example in which both the light from the light source 4, which has been reflected from the surface of the rope 1, and the light from the light source 5, which has been reflected from the surface of the rope 1, are received by a single light receiving element 6. In the Fig. In the example shown in Figure 9, the light source 5 desirably projects light onto the rope 1 having a wavelength that differs from the wavelength of the light from the light source 4.

[0069] Fig. Fig. 10 is a diagram showing a processing method of a light-receiving image obtained from the light-receiving element 6. The upper half of Fig. 10 shows a light receiving image of the light receiving element 6. The lower half of Fig. 10 shows first surface data P1 and second surface data P2 converted from the light receiving image of the light receiving element 6. The abscissa in the lower half of Fig. Figure 10 shows that each of the first surface data P1 and the second surface data P2 includes a plurality of data elements in the x-direction. The number of data elements included in the surface data is arbitrarily determined.

[0070] In the Fig. 9 and Fig. In the example shown in Figure 10, the sensor head 2 does not need to be provided with a plurality of light-receiving elements. Furthermore, a surface data extraction process can be easily performed as long as the wavelength of the light from the light source 4 differs from the wavelength of the light from the light source 5.

[0071] The sensor head 2 is not limited to an optical profile measuring instrument. Fig. 11 and Fig. 12 are diagrams for illustrating another example of the sensor head 2. For example, the sensor head 2 shown in Fig. 11 and Fig. 12, a camera 20. The sensor head 2 can receive, as surface data, data obtained from an image of the surface of the rope 1 photographed by the camera 20.

[0072] Data of an image photographed by the camera 20 does not include information related to height. The sensor head 2 may acquire, as surface data, data representing a color and a gradient of the color given to the surface of the rope 1. For example, the sensor head 2 acquires data representing a color and a gradient of the color given to the surface of the rope 1 within a range expressed by x1 ≤ x ≤ xr and L1 ≤ y ≤ LM on the surface of the rope 1. The data corresponds to M pieces of surface data. The sensor head 2 outputs, as the first surface data and the second surface data, two pieces of surface data predetermined in advance from the M pieces of surface data.

[0073] Fig. 13 is a diagram showing a processing method of data of an image photographed by the camera 20. Fig. 13 shows an example in which, as first surface data Pα, data representing a color and a gradient of the color given to the surface of an area passing a position given by y = Lα on the rope 1 are obtained. In a similar manner, Fig. 13 shows an example in which, as second surface data Pβ, data representing a color and a gradient of the color given to the surface of a region passing a position given by y = Lβ on the rope 1 are acquired. Second embodiment

[0074] In the present embodiment, an example for improving the calculation accuracy of the strand pitch SP will be described. A detection device according to the present embodiment is similar to that in, for example, the example shown in Fig. 11. For example, the detection device comprises the sensor head 2 and the controller 3. For example, the sensor head 2 comprises the camera 20.

[0075] For example, the sensor head 2 obtains, as surface data, data representing a color and a gradient of the color given to the surface of the rope 1. The sensor head 2 obtains a number M of surface data from the data of an image photographed by the camera 20. For example, M denotes a natural number equal to or greater than 3.

[0076] Fig. 14 is a diagram showing an example of the controller 3 according to the second embodiment of the present invention. In the Fig. In the example shown in Figure 14, the controller 3 includes the storage unit 9, the data processing unit 10, a selection unit 19, the similarity calculation unit 11, the phase calculation unit 12, the period calculation unit 13, and the anomaly detection unit 14. The controller 3 need not include the data processing unit 10. The controller 3 may also include the speed calculation unit 15, the position calculation unit 16, the deactivation unit 17, and the anomaly detection unit 18.

[0077] The first reference data Ref1 and the second reference data Ref2 are stored in the storage unit 9. For example, the first reference data Ref1 can be expressed by a matrix with n rows and 1 column. The second reference data Ref2 can be expressed by a matrix with n rows and 1 column.

[0078] The data processing unit 10 processes each piece of surface data received from the sensor head 2 into surface data that can be compared with reference data. For example, the data processing unit 10 performs a bias removal process on each piece of surface data received from the sensor head 2. For example, M pieces of surface data P(t, L1), P(t, L2), ....., P(t, LM) that have undergone the bias removal process are output from the data processing unit 10.

[0079] The surface data P(t,L1) is data representing a color and a chromaticity of the color given to the surface of an area passing at y = L1 at a time t in rope 1. The surface data P(t,L2) is data representing a color and a chromaticity of the color given to the surface of an area passing at y = L2 at time t in rope 1. The surface data P(t,LM) is data representing a color and a chromaticity of the color given to the surface of an area passing at y = LM at time t in rope 1. The values ​​of L1 to LM are predetermined in advance.

[0080] The selection unit 19 selects first surface data P (t, Lα) and second surface data P (t, Lβ) based on predetermined conditions from the M pieces of surface data output from the data processing unit 10.

[0081] The similarity calculation unit 11 calculates a similarity between the surface data selected by the selection unit 19 and the reference data. In other words, the similarity calculation unit 11 calculates a first similarity, a second similarity, a third similarity, and a fourth similarity. The first similarity denotes a similarity between the first surface data selected by the selection unit 19 and the first reference data stored in the storage unit 9. The second similarity denotes a similarity between the first surface data selected by the selection unit 19 and the second reference data stored in the storage unit 9.

[0082] The third similarity refers to a similarity between the second surface data selected by the selection unit 19 and the first reference data stored in the storage unit 9. The fourth similarity refers to a similarity between the second surface data selected by the selection unit 19 and the second reference data stored in the storage unit 9.

[0083] The function of the phase calculation unit 12 is similar to the function disclosed in the first embodiment. The function of the period calculation unit 13 is similar to the function disclosed in the first embodiment. The function of the anomaly detection unit 14 is similar to the function disclosed in the first embodiment.

[0084] Fig. 15 is a flowchart showing an operation example of the detection device according to the second embodiment of the present invention. As stated above, the number of M pieces of surface data is output from the data processing unit 10 (S201). The selection unit 19 selects the first surface data P(t, Lα) and the second surface data P(t, Lβ) from the number of M pieces of surface data output from the data processing unit 10 (S202).

[0085] With respect to each piece of surface data output from the data processing unit 10, the similarity calculation unit 11 calculates, for example, a similarity to the first reference data and a similarity to the second reference data. Fig. Figure 15 shows an example in which a correlation coefficient ρ1 is calculated as the similarity to the first reference data and a correlation coefficient p2 is calculated as the similarity to the second reference data. Next, with respect to each piece of surface data, a similarity vector S is calculated, which has the correlation coefficient ρ1 and the correlation coefficient p2 as elements. Furthermore, with respect to each piece of surface data, a norm of the similarity vector S is calculated.

[0086] For example, the selection unit 19 selects the first surface data P (t, Lα) and the second surface data P (t, Lβ) based on the calculated norm of the similarity vector S. Fig. 16 is a diagram illustrating a function of the selection unit 19. For example, the selection unit 19 selects surface data with the largest norm of the similarity vector S and surface data with the second largest norm of the similarity vector S from the number of M pieces of surface data output from the data processing unit 10 as the first surface data P(t, Lα) and the second surface data P(t, Lβ). In this case, Lα>Lβ.

[0087] When the first surface data P (t, Lα) and the second surface data P (t, Lβ) are selected in S202, processes similar to the processes in S103 to S106 are performed in Fig. 8. In the Fig. In the example shown in Figure 15, ρ1(t, Lα), p2(t, Lα), ρ1(t, β), and p2(t, Lβ) are already calculated in S202. When the first surface data P(t, Lα) and the second surface data P(t, Lβ) are selected by the selection unit 19, the phase calculation unit 12 calculates a phase θ(t, Lα) of a similarity vector S(t, α) (S203).

[0088] The similarity vector S(t, Lα) is a vector having, as elements, the correlation coefficient ρ1(t, Lα) and the correlation coefficient p2(t, Lα), which are calculated by the similarity calculation unit 11. The phase θ(t, Lα) is a deflection angle of the similarity vector S(t, Lα). Furthermore, the phase calculation unit 12 calculates a phase θ(t, Lβ) of a similarity vector S(t, Lβ) (S203). The similarity vector S(t, Lβ) is a vector having, as elements, the correlation coefficient ρ1(t, Lβ) and the correlation coefficient p2(t, Lβ), which are calculated by the similarity calculation unit 11. The phase θ (t, Lβ) is a deflection angle of the similarity vector S (t, Lβ).

[0089] For example, the period calculation unit 13 calculates the strand pitch SP according to the following equation based on the phase θ (t, Lα) and the phase θ (t, Lβ) calculated by the phase calculation unit 12. SP=2π×dydθ=2π(Lβ−Lα)θ(t,Lβ)−θ(t,Lα)

[0090] The anomaly detection unit 14 detects a period anomaly in the pattern formed on the elongated body based on the period calculated by the period calculation unit 13. For example, the anomaly detection unit 14 determines that a distance measurement anomaly has occurred in the rope 1 when the period calculated by the period calculation unit 13 is outside the reference range.

[0091] With the example described in the present invention, an abnormality in the rope 1 can be detected even when the moving speed of the rope 1 fluctuates. With the example described in the present embodiment, the calculation accuracy of the strand pitch SP can be improved.

[0092] To reduce the effect of noise, the selection unit 19 may select the first surface data P(t, Lα) and the second surface data P(t, Lβ) using a method different from the method described above. For example, the selection unit 19 first selects surface data whose norm of the similarity vector S is equal to or greater than a reference value from the number of M pieces of surface data output from the data processing unit 10.

[0093] The reference value is stored in advance in the storage unit 9. For example, the reference value is 0.3. The selection unit 19 selects, as the first surface data P(t, Lα) and the second surface data P(t, Lβ), two pieces of surface data that maximize the angle formed between similarity vectors S from the pieces of surface data whose norm of the similarity vector S is equal to or greater than the reference value.

[0094] Any features disclosed in the first embodiment may also be used as features not disclosed in the present embodiment. Third embodiment

[0095] As described above, an example of a long body that is to be a detection object of the present detection device is a wire rope used in an elevator. An elevator car is suspended in a shaft, for example, with a plurality of wire ropes. When an elevator car is suspended with a plurality of wire ropes, the same tension is desirably applied to each of the wire ropes.

[0096] For example, if a large tension acts on only a single wire rope, elongation occurs in the wire rope. As the wire rope elongates, the strand pitch increases. In other words, a pitch anomaly occurs in the wire rope. In the present embodiment, an example is described in which a plurality of long bodies are detection objects of the detection device.

[0097] Fig. 17 is a diagram showing an example of the detection device according to a third embodiment of the present invention. Fig. 18 is a diagram showing the long body from the direction of a Fig. 17 shows arrow A. Fig. 18 shows an example in which a rope 21 is arranged parallel to the rope 1.

[0098] The rope 21 moves in the longitudinal direction, similar to rope 1. For example, the rope 21 moves in the +y direction or in the -y direction. The rope 21 can move in both the +y direction and the -y direction. The rope 21 has a plurality of strands or strands. The rope 21 is formed by twisting or winding the plurality of strands together. The rope 21 has the same pattern on its surface as the pattern formed on the surface of the rope 1.

[0099] For example, the detection device comprises the sensor head 2 and the controller 3. For example, the sensor head 2 comprises the light source 4 and the light receiving element 6. In the example described in the present embodiment, the light source 4 simultaneously emits light toward both the surface of the rope 1 and the surface of the rope 21. Fig. 17 and Fig. 18 show an example in which the light source 4 emits a laser beam in a direction orthogonal to the longitudinal direction of the rope 1 and to the longitudinal direction of the rope 21.

[0100] The light emitted from the light source 4 strikes the rope 1 and the rope 21 at the same height. In the Fig. 17 and Fig. In the example shown in Figure 18, the following applies: The light 4 emitted by the light source impinges linearly from one end on one side to one end on the other side of the rope 1, so that it sweeps over the rope 1. Similarly, the light emitted from the light source 4 impinges linearly from one end on one side to one end on the other side of the rope 21, so that it sweeps over the rope 21.

[0101] The light-receiving element 6 receives the light reflected from the surface of the rope 1 between the light emitted by the light source 4. The light-receiving element 6 also receives the light reflected from the surface of the rope 21 between the light emitted by the light source 4. The light-receiving element 6 is arranged obliquely with respect to the direction in which the light source 4 emits the light.

[0102] The light receiving element 6 receives the light reflected obliquely at a certain angle with respect to the longitudinal direction of the rope 1, between the light from the light source 4 reflected from the surface of the rope 1. Similarly, the light receiving element 6 receives the light reflected obliquely at a certain angle with respect to the longitudinal direction of the rope 1, between the light from the light source 4 reflected from the surface of the rope 21.

[0103] The light a, as in Fig. 17 and Fig. 18 is light emitted toward the rope 1 from the light source 4. For example, the light a strikes the surface of the rope 1 at y = L1. The light b and the light c are reflected at an angle at which the light is received by the light receiving element 6, namely, the light a reflected from the surface of the rope 1. When the light receiving element 6 receives the light b, the light c, and the like, the sensor head 2 acquires data representing a cross-sectional shape of the area struck by the light from the light source 4 in the rope 1 as first surface data.

[0104] Similarly, the light g, as in the Fig. 17 and Fig. 18, light emitted toward the rope 21 from the light source 4. For example, the light g strikes the surface of the rope 21 at y = L1. The light h and the light i are reflected at an angle at which the light is received by the light receiving element 6, namely, the light g reflected from the surface of the rope 21. When the light receiving element 6 receives the light h, the light i, and the like, the sensor head 2 acquires data representing a cross-sectional shape of the area struck by the light from the light source 4 in the rope 21 as second surface data.

[0105] Fig. Fig. 19 is a diagram showing a processing method of a light-receiving image obtained from the light-receiving element 6. The upper half of Fig. 19 shows a light receiving image of the light receiving element 6. The lower half of Fig. 19 shows first surface data P3 and second surface data P4 converted from the light receiving image of the light receiving element 6.

[0106] The abscissa in the lower half of Fig. Figure 19 shows that each of the first surface data P3 and the second surface data P4 includes a plurality of data elements in the x-direction. The number of data elements included in the surface data is arbitrarily determined.

[0107] Fig. 20 is a diagram showing an example of the controller 3. The controller 3 includes, for example, the storage unit 9, the data processing unit 10, the similarity calculation unit 11, the phase calculation unit 12, and the anomaly detection unit 14. The controller 3 may not include the data processing unit 10. The controller 3 may also include the shutdown unit or deactivation unit 17 and the anomaly detection unit 18.

[0108] The first reference data Ref1 and the second reference data Ref2 are stored in the storage unit 9. For example, the first reference data Ref1 can be expressed by a matrix with n rows and 1 column. The second reference data Ref2 can be expressed by a matrix with n rows and 1 column.

[0109] The data processing unit 10 processes, for example, the first surface data received from the sensor head 2 and outputs the processed data as final first surface data. The data processing unit 10 processes, for example, the second surface data received from the sensor head 2 and outputs the processed data as final second surface data.

[0110] The similarity calculation unit 11 calculates a similarity between the surface data and the reference data. For example, the similarity calculation unit 11 calculates, as a first similarity, a correlation coefficient ρ1 (t, L1 (P3)) between the first surface data P3 (t, L1) output from the data processing unit 10 and the first reference data Ref1. The similarity calculation unit 11 calculates, as a second similarity, a correlation coefficient p2 (t, L1 (P3)) between the first surface data P3 (t, L1) output from the data processing unit 10 and the second reference data Ref2.

[0111] The similarity calculation unit 11 calculates, as a third similarity, a correlation coefficient ρ1 (t, L1 (P4)) between the second surface data P4 (t, L1) output from the data processing unit 10 and the first reference data Ref1. The similarity calculation unit 11 calculates, as a fourth similarity, a correlation coefficient p2 (t, L1 (P4)) between the second surface data P4 (t, L1) output from the data processing unit 10 and the second reference data Ref2.

[0112] Fig. 21 is a diagram illustrating a function of the phase calculation unit 12. The phase calculation unit 12 calculates a deflection angle of a similarity vector S as a phase θ. In the example described in the present embodiment, the phase calculation unit 12 calculates a phase θ3 (t, L1) of a similarity vector S3 (t, L1) related to the first surface data. The similarity vector S3 (t, L1) is a vector having, as elements, the correlation coefficient ρ1 (t, L1 (P3)) and the correlation coefficient p2 (t, L1 (P3)), which are calculated by the similarity calculation unit 11.

[0113] The phase θ3(t, L1) is a deflection angle of the similarity vector S3(t, L1). Furthermore, the phase calculation unit 12 calculates a phase θ4(t, L1) of a similarity vector S4(t, L1) related to the second surface data. The similarity vector S4(t, L1) is a vector having, as elements, the correlation coefficient ρ1(t, L1(P4)) and the correlation coefficient p2(t, L1(P4)), which are calculated by the similarity calculation unit 11. The phase θ4(t, L1) is a deflection angle of the similarity vector S4(t, L1).

[0114] When an elevator car is suspended in a shaft by rope 1 and rope 21, the speed at which rope 1 moves and the speed at which rope 21 moves are the same. If the strand pitch of rope 1 and the strand pitch of rope 21 are the same, the angle formed by the similarity vector S3(t,L1) and the similarity vector S4(t,L1) is constant, regardless of the speed described above. The angle formed by the similarity vector S3(t,L1) and the similarity vector S4(t,L1) is a difference, or in other words, a phase difference, between the phase θ4(t,L1) and the phase θ3(t,L1).

[0115] In the example described in the present embodiment, the anomaly detection unit 14 detects that an anomaly has occurred in the rope 1 or the rope 21 based on the phase θ4 (t, L1) and the phase θ3 (t, L1) calculated by the phase calculation unit 12. For example, the anomaly detection unit 14 detects a period anomaly in the pattern formed on the surface of the rope 1 or in the pattern formed on the surface of the rope 21. For example, a reference range for determining that the period of the pattern is normal is stored in advance in the storage unit 9.

[0116] The anomaly detection unit 14 determines that a distance measurement anomaly has not occurred in either rope 1 or rope 21 if the difference between the phase θ4 (t, L1) and the phase θ3 (t, L1) is within the reference range. The anomaly detection unit 14 determines that a distance measurement anomaly has occurred in any of rope 1 and rope 21 if the difference between the phase θ4 (t, L1) and the phase θ3 (t, L1) is not within the reference range.

[0117] In the example described in the present embodiment, the detection device can detect a plurality of long bodies as detection objects. Furthermore, in the example described in the present invention, the occurrence of an abnormality can be detected even when the moving speed of the rope 1 and the rope 21 fluctuates.

[0118] In the example described in the present embodiment, if the controller 3 further includes the anomaly detection unit 18, the anomaly detection unit 18 detects that an anomaly has occurred in the pattern of the rope 1 based on the norm of the similarity vector S3 (t, L1). For example, the anomaly detection unit 18 detects that an anomaly has occurred in the pattern of the rope 1 when the norm of the similarity vector S3 (t, L1) is outside the normal range.

[0119] Furthermore, the anomaly detection unit 18 detects that an anomaly has occurred in the pattern of the rope 21 based on the norm of the similarity vector S4 (t, L1). For example, the anomaly detection unit 18 detects that an anomaly has occurred in the pattern of the rope 21 when the norm of the similarity vector S4 (t, L1) is outside the normal range.

[0120] In the present embodiment, an example is described in which the light emitted from the light source 4 impinges on the rope 1 and the rope 21 at the same height. If the sensor head 2 is capable of acquiring pieces of surface data with different heights, the following applies: Even in the example described in the present embodiment, the controller 3 may further include the period calculation unit 13, the speed calculation unit 15, and the position calculation unit 16.

[0121] In such a case, for example, the following applies: The sensor head 2 has the light source 4, the light source 5, the light-receiving element 6, and the light-receiving element 7. The light source 4 irradiates the surface of the rope 1 with light. The light-receiving element 6 receives the light reflected from the surface of the rope 1 between the light emitted by the light source 4. The light source 5 irradiates the surface of the rope 21 with light.

[0122] The light emitted by the light source 5 impinges on the rope 21 at a position separated by a certain distance in the y-axis direction from the position where the light from the light source 4 impinges on the rope 1. The light receiving element 7 receives the light reflected from the surface of the rope 21 between the light emitted by the light source 5. The sensor head 2 may include the camera 20.

[0123] In addition, the speed calculation unit 15 calculates the moving speed of the rope 1 and the rope 21 based on the phase θ (t, L1) and the phase θ (t, L2) calculated by the phase calculation unit 12. In this example, L2 denotes the height at which the light from the light source 5 strikes the rope 21. The position calculation unit 16 calculates the position of a detected anomaly on the rope 1 or the rope 21 based on a moving distance at the moment the anomaly is detected by the anomaly detection unit 14.

[0124] Any of the features disclosed in the first or second embodiment may also be used as features not disclosed in the present embodiment. Fourth embodiment

[0125] An elevator to which the present detection device is applicable includes a controller for detecting the speed of the elevator car. For example, the controller comprises a controller rope, a controller pulley, and an encoder. The controller rope is wound around the controller pulley and moves along with the elevator car. In other words, when the car moves, the controller rope also moves. Furthermore, the controller pulley rotates as the controller rope moves. The encoder outputs a rotation signal according to the rotation direction and rotation speed of the controller pulley. The rotation signal output by the encoder is used to control the car.

[0126] In the examples described in the first to third embodiments, the speed calculation unit 15 calculates a moving speed V of the rope 1. When the elevator car is suspended by the rope 1, the moving speed V of the rope 1 coincides with the moving speed of the car. Therefore, in the elevator, the speed of the car can be detected using the speed calculation unit 15 instead of the controller. In such a case, the elevator does not need to have the controller. Alternatively, in the elevator, the speed of the car can be detected using the speed calculation unit 15 together with the controller.

[0127] With a controller, a detection error can occur due to slippage generated between the controller rope and the controller pulley. With a controller, a detection error can occur due to wear on the controller pulley. However, with the present detection device, surface data can be obtained in a contactless manner. Therefore, the car speed can be accurately detected. Furthermore, the elevator configuration can be simplified by eliminating the need for a controller.

[0128] Each of the units designated by reference numerals 9 to 19 represents a function included in the controller 3. Fig.Figure 22 is a diagram showing the hardware configuration of the controller 3. For example, the controller 3 includes, as hardware resources, a circuit including a processor 22 and a memory 23. The function contained in the memory unit 9 is realized by means of the memory 23. The controller 3 realizes the function of each of the units designated by reference numerals 10 to 19 by causing the processor 22 to execute a program stored in the memory 23.

[0129] The processor 22 is also referred to as a CPU (central processing unit), a central processor, a processing device, an arithmetic device, a microprocessor, a microcomputer, or a digital signal processor (DSP). The memory 23 can be a semiconductor memory, a magnetic disk, a flexible disk, an optical disk, a compact disc, a mini disc, or a DVD. Examples of semiconductor memories that can be used include RAM, ROM, flash memory, EPROM, and EEPROM.

[0130] Some or all of the functions included in the controller 3 can be implemented in hardware. The hardware used to implement the functions of the controller 3 can be a single circuit, a composite circuit, a programmed processor, a parallel-programmed processor, an ASIC, an FPGA, or a combination thereof. Industrial applicability

[0131] The detection device according to the present invention can be used as a device for detecting a long body having a periodic pattern on its surface. List of reference symbols 1 rope 2 sensor head 3 Control 4 Light source 5 Light source 6 Light receiving element 7 Light receiving element 8 Signal line 9 Storage unit 10 Data processing unit 11 Similarity calculation unit 12 phase calculation unit 13 Period calculation unit 14 Anomaly detection unit 15 Speed ​​calculation unit 16 Position calculation unit 17 Deactivation unit 18 Anomaly detection unit 19 Selection unit 20 Camera 21 rope 22 processors 23 storage

Claims

[1] Detection device comprising: - a data acquisition device configured to acquire first surface data and second surface data of an elongated body having a periodic pattern on its surface, the data acquisition device being configured to acquire the first surface data and the second surface data at different positions in a longitudinal direction of the elongated body in a direction orthogonal to the longitudinal direction of the elongated body; - a storage device (9) configured to store first reference data and second reference data, the first reference data and the second reference data having the same period as the period of the pattern formed on the surface of the elongated body in a direction orthogonal to the longitudinal direction of the elongated body, the second reference data being shifted from the phase of the first reference data; - a similarity calculation device (11) configured to calculate a first similarity between the first surface data obtained by means of the data obtaining device and the first reference data, to calculate a second similarity between the first surface data obtained by means of the data obtaining device and the second reference data, to calculate a third similarity between the second surface data obtained by means of the data obtaining device and the first reference data, and to calculate a fourth similarity between the second surface data obtained by means of the data obtaining device and the second reference data; - a phase calculation device (12) configured to calculate, as a first phase, a deflection angle of a first similarity vector having the first similarity and the second similarity calculated by the similarity calculation device (11) as elements, and to calculate, as a second phase, a deflection angle of a second similarity vector having the third similarity and the fourth similarity calculated by the similarity calculation device (11) as elements; - a period calculating means (13) configured to calculate a period of a pattern formed on the elongated body on the basis of the first phase and the second phase calculated by the phase calculating means (12); and - a first anomaly detection device (14) configured to calculate an anomaly in the long body based on the period calculated by the period calculation device (13). [2] A detection device according to claim 1, wherein the data obtaining means is configured to obtain a plurality of pieces of surface data of a long body. [3] A detection device according to claim 2, wherein said selecting means (19) selects, as said first surface data and said second surface data, such surface data having the largest norm and surface data having the second largest norm of a similarity vector having a similarity with said first reference data and a similarity with said second reference data as elements, from the elements of surface data acquired by said data obtaining means. [4] A detection device according to claim 2, wherein said selecting means (19) selects, as said first surface data and said second surface data, two pieces of surface data which maximize the angle formed between similarity vectors of pieces of surface data whose norm of a similarity vector having a similarity with said first reference data and a similarity with said second reference data as elements is equal to or larger than a reference value among the pieces of surface data acquired by said data acquiring means. [5] Detection device according to one of claims 1 to 4, further comprising: - a speed calculation device (15) configured to calculate the speed of the elongated body based on the first phase and the second phase calculated by the phase calculation device (12); and - a position calculation device (16) configured to calculate a position of the anomaly on the long body detected by the first anomaly detection device (14) based on the speed calculated by the speed calculation unit (15). [6] The detection device according to any one of claims 1 to 5, further comprising second anomaly detection means (18) configured to detect that an anomaly has occurred in the pattern of the long body based on at least one of a norm of the first similarity vector having the first similarity and the second similarity calculated by the similarity calculation means (11) as elements, and a norm of the second similarity vector having the third similarity and the fourth similarity calculated by the similarity calculation means (11) as elements. [7] Detection device according to one of claims 1 to 6, wherein the data acquisition device comprises: - a first light source (4) configured to irradiate the long body with light; - a second light source (5) configured to irradiate the elongated body with light of a wavelength different from the wavelength of the light from the first light source (4); and - a light receiving element (6) configured to receive light from the first light source (4) reflected by the long body and light from the second light source (5) reflected by the long body. [8] Detection device comprising: - a data acquisition device configured to acquire first surface data of a first elongated body having a periodic pattern on its surface and second surface data of a second elongated body having the same periodic pattern on its surface as that formed on the surface of the first elongated body, the data acquisition device being configured to acquire the first surface data in a direction orthogonal to a longitudinal direction of the first elongated body and to acquire the second surface data in a direction orthogonal to the longitudinal direction of the second elongated body; - a storage device (9) configured to store first reference data and second reference data, the first reference data and the second reference data having the same period as the period of the pattern formed on the surface of the first long body in a direction orthogonal to the longitudinal direction of the first long body, the second reference data having a phase shifted from the phase of the first reference data; - a similarity calculation device (11) configured to calculate a first similarity between the first surface data obtained by means of the data acquisition device and the first reference data, to calculate a second similarity between the first surface data obtained by means of the data acquisition device and the second reference data, to calculate a third similarity between the second surface data obtained by means of the data acquisition device and the first reference data, and to calculate a fourth similarity between the second surface data obtained by means of the data acquisition device and the second reference data; - a phase calculation device (12) configured to calculate, as a first phase, a deflection angle of a first similarity vector having the first similarity and the second similarity calculated by the similarity calculation device (11) as elements, and to calculate, as a second phase, a deflection angle of a second similarity vector having the third similarity and the fourth similarity calculated by the similarity calculation device (11) as elements; and - a first anomaly detection device (14) configured to detect that an anomaly has occurred in the first long body or in the second long body based on the first phase and the second phase calculated by the phase calculation device (12). [9] The detection device according to claim 8, further comprising second anomaly detection means (18) configured to detect that an anomaly has occurred in the pattern of the first long body based on a norm of the first similarity vector having the first similarity and the second similarity calculated by the similarity calculation means (11) as elements, and detect that an anomaly has occurred in the pattern of the second long body based on a norm of the second similarity vector having the third similarity and the fourth similarity calculated by the similarity calculation means (11) as elements. [10] Detection device according to claim 8 or 9, wherein the data obtaining device comprises: - a light source (4) configured to irradiate the first long body and the second long body with light; and - a light receiving element (6) configured to receive light from the light source (4) reflected by the first long body and light from the light source reflected by the second long body. [11] The detection device according to any one of claims 1 to 10, further comprising deactivation means (17) configured to deactivate the anomaly detection by the first anomaly detection means (14) based on at least one of a norm of the first similarity vector having the first similarity and the second similarity calculated by the similarity calculation means (11) as elements, and a norm of the second similarity vector having the third similarity and the fourth similarity calculated by the similarity calculation means (11) as elements, when the norm of the first similarity vector and / or the norm of the second similarity vector are outside a normal range.

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