Elevator long object inspection device

Abstract

Provided is a long object inspection device capable of more accurately detecting abnormality of a long object of an elevator. In an elevator (1), a camera (14) photographs the upper part or the lower part of an elevator car (6) in a manner that a long object and a groove wheel are included in a photographing range. A long object inspection device (13) is provided with a sheave detection unit (18), a long object detection unit (19), and a determination unit (17). The sheave detection unit (18) performs image processing for detecting a portion of the sheave with respect to an image captured by the camera (14). The long object detection unit (19) refers to the position of the portion of the sheave detected by the sheave detection unit (18), and performs image processing for detecting the portion of the long object with respect to the image captured by the camera (14). The determination unit (17) determines the presence or absence of an abnormality in the long object on the basis of the detected state of the long object.

Description

Technical Field

[0001] The present invention relates to a device for inspecting long objects of an elevator. Background Art

[0002] Patent document 1 discloses an example of a long object inspection device for an elevator. The long object inspection device includes a camera, an image processing device, and a determination device. The camera is installed in a car traveling in a hoistway. The camera shoots the top of the car in such a way that the main rope, which is a long object, is included in the image. The image processing device performs image processing to extract the main rope portion of the image captured by the camera. The determination device uses the image processed by the image processing device, that is, the processed image, to determine whether the main rope is hooked on the hooking object in the hoistway.

[0003] Prior art literature

[0004] Patent Literature

[0005] Patent Document 1: Japanese Patent Application Publication No. 2015-20863 Summary of the invention

[0006] Problems to be solved by the invention

[0007] In the elevator shaft, in addition to the main rope and other long objects that are the objects to be judged for abnormalities such as hooking, there are also long objects that are not the objects to be judged for abnormalities. In addition, there are sometimes linear textures and the like on the inner wall of the shaft. Therefore, in the long object inspection device of Patent Document 1, when the long object or the linear texture that is not the object to be judged is mistakenly detected as the long object to be judged, the abnormality such as hooking of the long object may be mistakenly detected.

[0008] The present disclosure relates to solving such a problem. The present disclosure provides a long object inspection device capable of more accurately detecting abnormalities of a long object in an elevator.

[0009] Means for solving problems

[0010] The long object inspection device disclosed in the present invention detects abnormalities of a long object wound on a sheave in a shaft for an elevator car to travel, wherein the long object inspection device comprises: a shooting unit, which shoots one or both of the top and bottom of the car in a manner so that the long object and the sheave are included in the shooting range; an image processing unit, which performs image processing on the image captured by the shooting unit to detect a portion of the long object; and a determination unit, which determines whether the long object has abnormalities based on the state of the long object detected by the image processing unit, the image processing unit comprising: a sheave detection unit, which performs image processing on the image captured by the shooting unit to detect a portion of the sheave; and a long object detection unit, which performs image processing on the image captured by the shooting unit to detect a portion of the long object, with reference to the position of the portion of the sheave detected by the sheave detection unit.

[0011] The long object inspection device disclosed in the present invention detects abnormalities of a long object wound on a sheave in a shaft for traveling of an elevator car, wherein the long object inspection device comprises: an image processing unit, which performs image processing on a portion of the long object detected with respect to an image captured by a photographing unit, wherein the photographing unit photographs one or both of the top and bottom of the car in such a manner that the long object and the sheave are included in a photographing range; and a determination unit, which determines whether the long object has abnormalities based on a state of the long object detected by the image processing unit, wherein the image processing unit comprises: a sheave detection unit, which performs image processing on a portion of the sheave detected with respect to the image captured by the photographing unit; and a long object detection unit, which performs image processing on a portion of the long object detected with respect to the image captured by the photographing unit, with reference to a position of the portion of the sheave detected by the sheave detection unit.

[0012] Effects of the Invention

[0013] According to the long object inspection device disclosed in the present invention, abnormalities of the long objects in the elevator can be detected more accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a side view showing the structure of the elevator according to Embodiment 1.

[0015] Figure 2 This is a block diagram showing the functions of the long object inspection device according to the first embodiment.

[0016] Figure 3 This is a diagram showing an example of an image captured by the camera according to the first embodiment.

[0017] Figure 4 This is a side view showing a state in which an abnormality has occurred in the speed governor rope of the elevator according to Embodiment 1.

[0018] Figure 5 This is a diagram showing an example of an image captured by a camera when an abnormality occurs in the speed governor rope of the elevator according to the first embodiment.

[0019] Figure 6 This is a diagram showing a portion where an abnormality has occurred in the speed governor rope of the elevator according to the first embodiment.

[0020] Figure 7 This is a diagram showing a portion where an abnormality has occurred in the speed governor rope of the elevator according to the first embodiment.

[0021] Figure 8 This is a flowchart showing an example of the operation of the long object inspection device according to the first embodiment.

[0022] Fig. 9 This is a hardware configuration diagram of the main parts of the long object inspection device according to the first embodiment.

[0023] Fig.10 This is a block diagram showing the functions of the long object inspection device according to the second embodiment.

[0024] Fig.11 This is a diagram showing an example of a panoramic image of a shaft according to the second embodiment. DETAILED DESCRIPTION

[0025] The method for implementing the object of the present disclosure is described with reference to the accompanying drawings. In each figure, the same or equivalent parts are marked with the same reference numerals, and repeated descriptions are appropriately simplified or omitted. In addition, the object of the present disclosure is not limited to the following embodiments, and any structural element of the embodiment can be deformed or omitted within the scope of the present disclosure.

[0026] Implementation Method 1

[0027] Figure 1 This is a side view showing the structure of the elevator 1 according to the first embodiment.

[0028] The elevator 1 is applied to a building having a plurality of floors. Figure 1 1 shows an example of an elevator 1 viewed from the side. A shaft 2 of the elevator 1 is provided in a building. The shaft 2 is a long space in the vertical direction extending over a plurality of floors. A pit 3 is provided at the lower end of the shaft 2. The elevator 1 has a traction machine 4, a main rope 5, a car 6, a counterweight 7, and a control panel 8.

[0029] The hoisting machine 4 has a sheave and a motor. The sheave of the hoisting machine 4 is connected to the rotating shaft of the motor of the hoisting machine 4. The motor of the hoisting machine 4 is a device that generates a driving force for rotating the sheave of the hoisting machine 4.

[0030] The main rope 5 is wound around the sheave of the hoisting machine 4. The main rope 5 suspends the car 6 in the hoistway 2, thereby supporting the load of the car 6. The main rope 5 suspends the counterweight 7 in the hoistway 2, thereby supporting the load of the counterweight 7. In this example, the main rope 5 is wound around the return pulley 9. The main rope 5 supports the load of the car 6 on one side of the return pulley 9. The main rope 5 supports the load of the car 6 on the other side of the return pulley 9.

[0031] The main rope 5 is moved by the rotation of the sheave of the traction machine 4, whereby the car 6 and the counterweight 7 travel in opposite directions to each other in the hoistway 2. The car 6 is a device for transporting passengers and the like between multiple floors by traveling in the up-and-down direction inside the hoistway 2. The counterweight 7 is a device for balancing the load of the main rope 5 between the car 6 and both sides of the pulleys such as the return pulley 9 around which the main rope 5 is wound.

[0032] The control panel 8 is a device that controls the operation of the elevator 1. The control panel 8 controls, for example, the travel of the car 6. The control panel 8 is equipped with a function of obtaining the position of the car 6 in the hoistway 2, and the like.

[0033] The elevator 1 has a speed governor 10, a speed governor rope 11, and a tensioning pulley 12. The speed governor 10 is a device for suppressing the excessive travel speed of the car 6. The speed governor 10 has a pulley. The speed governor rope 11 is wound around the pulley of the speed governor 10. Both ends of the speed governor rope 11 are attached to the car 6. The speed governor rope 11 is wound around the tensioning pulley 12. The tensioning pulley 12 is a pulley for applying tension to the speed governor rope 11. The tensioning pulley 12 is provided, for example, in the pit 3. The pulley of the speed governor 10 rotates in conjunction with the movement of the car 6 through the speed governor rope 11 connected to the car 6. When the rotation speed of the pulley is excessive, the speed governor 10 suppresses the excessive travel speed of the car 6.

[0034] The long object inspection device 13 is applied in the elevator 1. The long object inspection device 13 is a device for detecting abnormalities of the long object in the hoistway 2. The long object set as the abnormality detection object by the long object inspection device 13 is a device that is long in one direction. When there is no abnormality, the length direction of the long object is parallel to the travel direction of the car 6. In this example, the long object moves in the hoistway 2 as the elevator 1 operates. The long object is, for example, the main rope 5 or the speed limiter rope 11. The long object can also be a balance rope not shown in the figure that compensates for the imbalance between the deadweight of the main rope 5 on the side of the car 6 and the deadweight of the main rope 5 on the side of the counterweight 7 caused by the movement of the main rope 5. The long object can also be a control cable for communicating electrical signals or supplying electricity. The long object can also be, for example, a strand rope, or a belt or chain. The long object is wound on a sheave. The sheave is, for example, a pulley of the traction machine 4, a pulley of the speed governor 10, a return pulley 9, a tensioning pulley 12, a deflector pulley or a suspension pulley. The sheave includes, for example, a portion for the long object to pass through or reverse. The long object inspection device 13 has a camera 14 and an information processing device 15.

[0035] The camera 14 is equipped with a function of photographing the inside of the shaft 2. The camera 14 is an example of a photographing unit. The camera 14 photographs the inside of the shaft 2 in such a manner that the long object and the sheave around which the long object is wound are included in the photographing range. In this example, the camera 14 is mounted on the lower side of the floor of the car 6. The camera 14 photographs, for example, the lower part of the car 6. In addition, the camera 14 may also be mounted on the upper side of the ceiling of the car 6. In this case, the camera 14 photographs, for example, the upper part of the car 6. In addition, the camera 14 may also be set in the shaft 2, in which case, the camera 14 photographs, for example, the lower part of the shaft 2.

[0036] The photographing unit may also be a camera that photographs both the upper and lower sides of the car 6. In addition, the photographing unit may also include a plurality of cameras. In this case, the photographing unit may also include a camera that photographs the upper side of the car 6 and a camera that photographs the lower side of the car 6. In addition, the long object inspection device 13 may also use a camera of an external device as a photographing unit. That is, the information processing device 15 of the long object inspection device 13 may also use images captured by a camera of an external device to detect abnormalities in the long object.

[0037] The information processing device 15 is a part in charge of information processing related to the detection of abnormality of the long object. The information processing device 15 is connected to the camera 14 so as to be able to obtain the image captured by the camera 14. The information processing device 15 is installed on the upper part of the car 6, for example.

[0038] Figure 2 This is a block diagram showing the functions of the long object inspection device 13 according to the first embodiment.

[0039] The information processing device 15 has an image processing unit 16 and a determination unit 17. The image processing unit 16 is a part equipped with a function of performing image processing for detecting long objects with respect to the image captured by the camera 14. The image processing unit 16 is equipped with a function of acquiring the image captured by the camera 14. The determination unit 17 is a part equipped with a function of determining whether the long object has an abnormality based on the state of the long object detected by the image processing unit 16. The determination unit 17 is equipped with a function of outputting the determination result of whether there is an abnormality, etc. to the control panel 8. The image processing device has a sheave detection unit 18 and a long object detection unit 19.

[0040] The sheave detection unit 18 is equipped with a function of performing image processing on the image captured by the camera 14 to detect a portion of the sheave around which the long object is wound.

[0041] The sheave detection unit 18 detects the sheave portion by, for example, a template matching method. The sheave detection unit 18 calculates the similarity obtained by comparing each portion of the image captured by the camera 14 with a preset template image representing a correct image of the sheave. At this time, the sheave detection unit 18 detects, for example, a portion whose calculated similarity is greater than a preset similarity threshold as a sheave portion.

[0042] The sheave detection unit 18 may also learn the feature quantity of the image by a machine learning method, and determine the sheave portion by using a discriminator using the feature quantity. The sheave detection unit 18 may use, for example, HOG (Histogram of Oriented Gradients) or SIFT (Scale Invariant Feature Transform) as a feature quantity. The sheave detection unit 18 may use, for example, SVM (Support Vector Machine) as a discriminator. The sheave detection unit 18 may also use a deep learning method to determine the sheave portion.

[0043] In addition, the sheave detection unit 18 can also detect a mark marked on the sheave and detect the sheave based on the detection result of the mark. The mark marked on the sheave includes, for example, a mark with a sticker showing a preset color or patterned code. The sheave detection unit 18 can also filter out an area including a portion of the sheave on the image captured by the camera 14 based on the category or installation information of the elevator 1. At this time, the sheave detection unit 18 performs image processing to detect the portion of the sheave in the filtered area. The information on the category of the elevator 1 includes, for example, information such as the model or type of the elevator 1. The installation information of the elevator 1 includes information such as the installation position of the sheave. The category or installation information of the elevator 1 can be pre-set in the sheave detection unit 18, or can be obtained by the sheave detection unit 18 from the control panel 8 of the elevator 1, etc.

[0044] The long object detection unit 19 refers to the position of the sheave portion detected by the sheave detection unit 18 and performs image processing for detecting the long object portion on the image captured by the camera 14 .

[0045] The long object detection unit 19 detects the portion of the long object by, for example, an edge detection method. The long object detection unit 19 detects, for example, the edge of the portion of the detected edge that passes through the sheave as the portion of the long object. The long object detection unit 19 determines the portion of the long object by using an edge detector to determine a linear object extending from the portion of the sheave detected by the sheave detection unit 18 as a starting point. The long object, for example, extends linearly in the travel direction of the car 6 from the sheave as a starting point.

[0046] The long object detection unit 19 may also detect the part of the long object by a method of local similarity determination. For example, the long object detection unit 19 takes the part of the sheave detected by the sheave detection unit 18 as the starting point, and sequentially extracts partial images of a preset size from the image captured by the camera 14 along the travel direction of the car 6. The long object detection unit 19 calculates the similarity for each part of the group of close partial images in the extracted partial images. Here, the group of close partial images is, for example, a group of partial images adjacent to each other in the travel direction of the car 6, or a group of partial images whose distance in the travel direction of the car 6 on the image captured by the camera 14 is shorter than a preset distance. The long object detection unit 19 tracks the part with higher similarity more preferentially between the close partial images in the extracted partial images, thereby detecting the part of the long object. As a method of sequentially detecting the part of the long object starting from the position of the sheave, the long object detection unit 19 may also use a tracking filter used in image recognition processing, etc. The long object detection unit 19 detects the long object portion by tracking the long object portion from the position of the sheave using, for example, a particle filter or a Kalman filter.

[0047] The determination unit 17 determines whether the long object is abnormal based on the state of the long object detected by the long object detection unit 19. The determination unit 17 determines whether the long object is abnormal, for example, based on the position, orientation, shape, etc. of the detected long object. The determination unit 17 outputs the determination result related to whether the long object is abnormal, for example, to the control panel 8.

[0048] The determination unit 17 may also calculate the reliability of the detection of the long object by the long object detection unit 19. For example, when the long object detection unit 19 detects the long object using an edge detector, similarity between partial images, or a tracking filter, the determination unit 17 calculates the reliability of the detection based on the strength or likelihood of the edge, etc. The determination unit 17 outputs the information of the calculated reliability, for example, together with the determination result, to the control panel 8.

[0049] In addition, the determination unit 17 may also calculate the position of the abnormal part detected for the long object in the shaft 2. The determination unit 17 may also calculate the position of the abnormal part in the shaft 2 based on the position of the abnormal part on the image. At this time, the determination unit 17 may also calculate the position of the part in the shaft 2 based on the part of the sheave detected by the sheave detection unit 18. In addition, at this time, the determination unit 17 may also use information on the position of the car 6 when the image is captured. The determination unit 17 outputs the information on the position of the calculated abnormal detection part to the control panel 8, for example, together with the determination result.

[0050] The control panel 8 may also control the operation of the elevator 1 according to the determination result input from the determination unit 17. For example, when receiving a determination result that there is no abnormality, the control panel 8 continues the operation of the elevator 1 without stopping. On the other hand, when receiving a determination result that there is an abnormality, the control panel 8 stops the operation of the elevator 1. The control panel 8 may also perform processing corresponding to the reliability of the determination result. For example, when receiving a determination result that there is no abnormality at a reliability higher than a preset threshold, the control panel 8 continues the operation of the elevator 1 without the confirmation of the maintenance personnel and the like. At this time, when the elevator 1 has stopped, the control panel 8 may restart the operation of the elevator 1 without the confirmation of the maintenance personnel and the like. On the other hand, when receiving a determination result that there is no abnormality at a reliability lower than a preset threshold, the control panel 8 may notify the maintenance personnel and the like of the determination result. At this time, the control panel 8 may also stop the operation of the elevator 1 until the maintenance personnel and the like confirm the state of the long object through an on-site visit. The control panel 8 may also notify the reliability calculated by the determination unit 17 or the position of the abnormality detection part together with the determination result.

[0051] Figure 3 This is a diagram showing an example of an image captured by the camera 14 according to the first embodiment.

[0052] In this example, the long object inspection device 13 detects an abnormality of the speed governor rope 11 as a long object. The long object inspection device 13 detects an abnormality of the speed governor rope 11 after detecting the tensioning wheel 12 as an example of a sheave. Figure 3 Illustration of structures other than the speed governor rope 11 and the tensioning pulley 12 is omitted.

[0053] In addition, the strip inspection device 13 can also detect the abnormality of the strip for other strips and sheave groups. The strip inspection device 13 is set as the strip and sheave group of the detection object, for example, it can also be the group of the speed governor rope 11 and the pulley of the speed governor 10, the group of the main rope 5 and the sheave of the traction machine 4, and the group of the balance rope and the pulley wound with the balance rope, etc.

[0054] Next, use Figures 4 to 7An example of long object abnormality detection performed by the long object inspection device 13 will be described.

[0055] In this example, the long object inspection device 13 detects the hooking of the speed governor rope 11 wound on the tensioning wheel 12 to the structure or car 6 in the hoistway 2 as an abnormality of the long object. The structure in the hoistway 2 includes, for example, a housing, a bracket, a frame, a beam, a column or a bracket of equipment in the hoistway 2.

[0056] Figure 4 This is a side view showing a state in which an abnormality has occurred in the speed governor rope 11 of the elevator 1 according to the first embodiment.

[0057] exist Figure 1 1 shows an elevator 1 in which the speed governor rope 11 is not abnormal. Figure 4 2 shows the elevator 1 in a state where the speed governor rope 11 is hooked on a structure in the hoistway 2 .

[0058] Figure 5 This is a diagram showing an example of an image captured by the camera 14 when an abnormality occurs in the speed governor rope 11 of the elevator 1 according to the first embodiment.

[0059] exist Figure 3 2 shows an example of a captured image when the speed governor rope 11 is not abnormal. Figure 5 2 shows an example of a captured image when the speed governor rope 11 is hooked on a structure in the hoistway 2. In addition, illustration of the structure in the hoistway 2 is omitted.

[0060] Figure 6 This is a diagram showing a portion where an abnormality has occurred in the speed governor rope 11 of the elevator 1 according to the first embodiment.

[0061] exist Figure 6 An enlarged view of the part where the hook is hooked to the structure is shown in FIG. Figure 6 In the figure, the speed governor rope 11 is shown by a dotted line in a state where it is not hooked to the structure in the hoistway 2. In addition, when no abnormality occurs, the state such as the direction and position of the speed governor rope 11 when it is not hooked is obtained in advance. For example, when the elevator 1 is installed or the elevator 1 is regularly inspected, the state such as the direction and position of the speed governor rope 11 when it is not hooked is obtained in advance. The obtained state is stored in the long object inspection device 13.

[0062] Due to the hooking on the structure, the direction of the speed governor rope 11 extending from the tensioning wheel 12 as the starting point forms an angle D with the direction of the speed governor rope 11 in the unhooked state. The determination unit 17 calculates the angle D based on the detection result of the long object detection unit 19. For example, when the calculated angle D is greater than a preset angle threshold, the determination unit 17 regards that the speed governor rope 11 is hooked on the structure and determines that an abnormality exists.

[0063] Figure 7 This is a diagram showing a portion where an abnormality has occurred in the speed governor rope 11 of the elevator 1 according to the first embodiment.

[0064] exist Figure 7 In FIG. 1 , a plurality of sampling points are shown on the detected long object. The sampling points are sampled at equal intervals in the traveling direction of the car 6, for example.

[0065] The determination unit 17 may also detect an abnormality of the long object based on the linearity of the detected long object. For example, the determination unit 17 may also calculate the direction of the line segment connecting the adjacent sampling points in the travel direction of the car 6, and when the angle of the direction of the adjacent line segment becomes greater than a preset threshold, it is considered that the speed limiter rope 11 is hooked on the structure and an abnormality is determined.

[0066] In addition, the strip inspection device 13 can also detect abnormalities other than hooking of the strip. The strip inspection device 13 can also detect abnormalities such as damage such as cutting of the strip, broken strands, or poor tension. The strip inspection device 13 can also detect the cutting of the strip, etc., based on the continuity of the strip being inspected, etc. The strip inspection device 13 can also detect local damage, etc., based on changes in image similarity along the length direction of the strip being inspected, etc. The strip inspection device 13 can also detect poor tension, etc., based on the linearity of the strip being inspected, etc.

[0067] Next, use Figure 8 An example of the operation of the long object inspection device 13 will be described.

[0068] Figure 8 This is a flowchart showing an example of the operation of the long object inspection device 13 according to the first embodiment.

[0069] For example, when an earthquake is detected in the elevator 1, Figure 8 For example, the process can be performed during the operation of elevator 1. Figure 8 The processing can also be performed at a pre-set regular or irregular time. Figure 8 In addition, it can be processed by the operation of the maintenance personnel or managers, etc. Figure 8 The processing in can also be carried out according to the occurrence of the event detected in elevator 1. Figure 8 in the processing.

[0070] In step S1, the camera 14 captures an image of the shaft 2. Then, the processing of the long object inspection device 13 proceeds to step S2.

[0071] In step S2, the sheave detector 18 performs processing for detecting the sheave from the image captured by the camera 14. The sheave detector 18 outputs information on the sheave portion detected in the image. Then, the processing of the long object inspection device 13 proceeds to step S3.

[0072] In step S3, the long object detector 19 detects the long object portion by referring to the position of the sheave portion output by the sheave detector 18. The long object detector 19 outputs information on the long object portion detected on the image. Then, the long object inspection device 13 proceeds to step S4.

[0073] In step S4, the determination unit 17 determines whether the long object has an abnormality such as hooking based on the state of the long object portion output by the long object detection unit 19. If there is an abnormality, the processing of the long object inspection device 13 proceeds to step S5. On the other hand, if there is no abnormality, the processing of the long object inspection device 13 proceeds to step S6.

[0074] In step S5, the determination unit 17 outputs an abnormality signal to the control panel 8. Then, the processing of the long object inspection device 13 ends.

[0075] In step S6, the determination unit 17 outputs a normal signal to the control panel 8. Then, the processing of the long object inspection device 13 ends.

[0076] As described above, the strip inspection device 13 of embodiment 1 detects abnormalities of the strip wound on the sheave in the hoistway 2. In the elevator 1, the camera 14 is provided on the car 6 traveling in the hoistway 2. The camera 14 photographs one or both of the upper and lower parts of the car 6 in such a manner that the strip and the sheave are included in the photographing range. The strip inspection device 13 includes an image processing unit 16 and a determination unit 17. The image processing unit 16 performs image processing of the portion of the strip detected on the image photographed by the camera 14. The determination unit 17 determines whether the strip has abnormalities according to the state of the strip detected by the image processing unit 16. The image processing unit 16 includes a sheave detection unit 18 and a strip detection unit 19. The sheave detection unit 18 performs image processing of the portion of the sheave detected on the image photographed by the camera 14. The strip detection unit 19 performs image processing of the portion of the strip detected on the image photographed by the camera 14 with reference to the position of the portion of the sheave detected by the sheave detection unit 18, and performs image processing of the portion of the strip detected on the image photographed by the camera 14.

[0077] According to this structure, the part of the long object is detected based on the part of the detected sheave. Therefore, even when the long object or linear texture outside the determination object is located in the hoistway 2, the long object as the determination object of the presence or absence of abnormality can be detected more accurately. Therefore, it is possible to more accurately detect abnormalities such as the long object of the elevator 1 being hooked on the structure. When a disaster such as an earthquake occurs in a place where the elevator 1 is installed, the elevator 1 is sometimes stopped in an emergency. At this time, after the elevator 1 is restored, it is important to confirm whether the long object in the hoistway 2 such as the speed governor rope 11, the main rope 5, the balance rope or the control cable is hooked on the structure in the hoistway 2 or a part of the car 6. On the other hand, it takes a lot of time and cost for maintenance personnel to visit multiple elevators 1 in the area where the earthquake occurred in turn to confirm. In contrast, the hooking of the long object is detected using the camera 14 that shoots the inside of the hoistway 2, thereby reducing the time or cost of responding after the earthquake. In addition, the abnormality of the long object is detected more accurately, so the time or cost of responding after the earthquake is more effectively reduced.

[0078] In addition, when the angle formed by the direction in which the long object detected by the long object detection unit 19 extends on the image and the direction in which the long object extends on the image when not hooked on the structure in the shaft 2 is greater than a preset angle threshold, the determination unit 17 determines that there is an abnormality in the long object.

[0079] According to this configuration, the presence or absence of hooking is determined based on the difference between the long object and the state in which there is no abnormality. Therefore, the determination criterion for the presence or absence of abnormality can be set more clearly.

[0080] The sheave detection unit 18 calculates similarity by comparing the portion on the image captured by the camera 14 with a preset template image. The sheave detection unit 18 may detect a position having a similarity equal to or greater than a preset similarity threshold as a sheave portion.

[0081] Furthermore, the sheave detection unit 18 may detect the sheave portion from the image captured by the camera 14 by a machine learning method.

[0082] According to such a configuration, the sheave detection unit 18 can identify the position of the sheave based on the image captured by the camera 14 .

[0083] Furthermore, the sheave detection unit 18 selects a region including a sheave portion on the image captured by the camera 14 based on the type or installation information of the elevator 1. The sheave detection unit 18 performs image processing to detect a sheave portion in the selected region.

[0084] According to such a configuration, the sheave detection unit 18 can more accurately identify the position of the sheave based on the image captured by the camera 14 .

[0085] Furthermore, the elongated object detection unit 19 detects the elongated object portion by specifying a linear object extending from the portion of the sheave detected by the sheave detection unit 18 as a starting point using an edge detector.

[0086] According to such a configuration, the long object detection unit 19 can more accurately detect the long object that is the target of the abnormality determination among the plurality of long objects in the hoistway 2 .

[0087] In addition, the long object detection unit 19 sequentially extracts partial images of a preset size along the travel direction of the car 6, starting from the portion of the sheave detected by the sheave detection unit 18. The long object detection unit 19 sequentially tracks the portions with higher similarity between the close partial images in the extracted partial images with higher priority, thereby detecting the portion of the long object.

[0088] According to such a configuration, even when the elongated object detection unit 19 cannot detect the edge locally due to the influence of external light or illumination, it is possible to detect the elongated object by sequentially tracing back from the sheave as the starting point.

[0089] Furthermore, the determination unit 17 calculates the reliability of the detection of the long object by the long object detection unit 19. The determination unit 17 outputs the calculated reliability together with the determination result of the abnormality of the long object.

[0090] According to such a configuration, a maintenance worker or the like who has received the determination result from the determination unit 17 can determine the recovery response order based on the output reliability.

[0091] Furthermore, the determination unit 17 calculates the position of the portion where the abnormality is detected for the long object in the shaft 2. The determination unit 17 outputs the calculated position in the shaft 2 together with the determination result of the abnormality of the long object.

[0092] According to such a configuration, a maintenance worker or the like who has received the determination result from the determination unit 17 can quickly grasp the position of the portion that requires restoration in the hoistway 2 and perform the restoration work.

[0093] Next, use Fig. 9 An example of the hardware configuration of the long object inspection device 13 will be described.

[0094] Fig. 9 This is a hardware configuration diagram of the main parts of the long object inspection device 13 according to the first embodiment.

[0095] Each function of the long object inspection device 13 can be realized by a processing circuit. The processing circuit has at least one processor 100a and at least one memory 100b. The processing circuit may also have at least one dedicated hardware 200 together with or instead of the processor 100a and the memory 100b.

[0096] In the case where the processing circuit has a processor 100a and a memory 100b, each function of the long object inspection device 13 is implemented by software, firmware, or a combination of software and firmware. At least one of the software and firmware is described as a program. The program is stored in the memory 100b. The processor 100a reads out and executes the program stored in the memory 100b, thereby implementing each function of the long object inspection device 13.

[0097] The processor 100a is also called a CPU (Central Processing Unit), a processing device, an arithmetic device, a microprocessor, a microcomputer, or a DSP. The memory 100b is composed of a nonvolatile or volatile semiconductor memory such as a RAM, a ROM, a flash memory, an EPROM, or an EEPROM.

[0098] In the case where the processing circuit has dedicated hardware 200, the processing circuit is implemented by, for example, a single circuit, a complex circuit, a programmed processor, parallel programmed processors, an ASIC, an FPGA, or a combination thereof.

[0099] Each function of the long object inspection device 13 can be implemented by a processing circuit respectively. Alternatively, each function of the long object inspection device 13 can also be implemented uniformly by a processing circuit. Regarding each function of the long object inspection device 13, a part can also be implemented by dedicated hardware 200, and another part can be implemented by software or firmware. In this way, the processing circuit implements each function of the long object inspection device 13 through dedicated hardware 200, software, firmware or a combination thereof.

[0100] Implementation Method 2

[0101] In the second embodiment, the differences from the example disclosed in the first embodiment are described in detail. Any features of the example disclosed in the first embodiment may be adopted as features not described in the second embodiment.

[0102] Fig.10 This is a block diagram showing the functions of the long object inspection device 13 according to the second embodiment.

[0103] The image processing unit 16 includes a panoramic image generating unit 20. The panoramic image generating unit 20 is a unit equipped with a function of generating a panoramic image using images sequentially captured by the camera 14 when the car 6 rises or descends. The panoramic image generating unit 20 cuts out a part of the sequentially captured images and joins them, thereby generating a panoramic image. The panoramic image generating unit 20 may also generate a panoramic image by adding an image of the pit 3 captured by the camera 14 to the joined image. In this example, the panoramic image is an image representing the entirety of the hoistway 2. In addition, when there is no sheave with a long object as a determination object wound in the pit 3, etc., the panoramic image generating unit 20 may also generate a panoramic image without adding an image of the pit 3.

[0104] The sheave detector 18 detects the sheave portion using the panoramic image generated by the panoramic image generator 20. The long object detector 19 detects the long object portion using the panoramic image generated by the panoramic image generator 20 with reference to the detected sheave portion.

[0105] Fig.11 This is a diagram showing an example of a panoramic image of the shaft 2 according to the second embodiment.

[0106] exist Fig.11 In FIG. 1 , the speed governor rope 11 in the image of the pit 3 is indicated by reference numeral 11a. In addition, the speed governor rope 11 in the portion of the image joining the hoistway 2 is indicated by reference numeral 11b.

[0107] In the panoramic image thus generated, the speed governor rope 11 is connected and represented as a long object throughout the entire shaft 2. Therefore, the long object detection unit 19 can detect the portion of the speed governor rope 11 throughout the entire shaft 2, starting from the tensioning pulley 12 detected by the sheave detection unit 18. In addition, the image processing unit 16 of the long object detection device can also exclude the portion of the image of the shaft 2 and the boundary of the image of the pit 3 from the long object abnormality determination object.

[0108] As described above, the image processing unit 16 of the long object detection device of Embodiment 2 has a panoramic image generation unit 20. The panoramic image generation unit 20 combines at least a portion of the images sequentially captured by the camera 14 along the travel direction of the car 6, thereby generating a panoramic image of the shaft 2 throughout the travel direction of the car 6. The sheave detection unit 18 performs image processing on the portion of the sheave detection for the panoramic image generated by the panoramic image generation unit 20. The long object detection unit 19 refers to the position of the portion of the sheave detected by the sheave detection unit 18, and performs image processing on the portion of the long object detection for the panoramic image generated by the panoramic image generation unit 20.

[0109] Furthermore, the panoramic image generation unit 20 generates a panoramic image by adding an image of the pit 3 at the lower end of the hoistway 2 .

[0110] According to this structure, the image processing unit 16 can set the processing object to a panoramic image of a static image. As a result, the calculation load in the image processing unit 16 and the usage of the memory for holding the image can be suppressed. In addition, the long object without abnormality is represented as a straight line along the travel direction of the car 6 on the panoramic image. Therefore, the judgment criterion of the presence or absence of abnormalities such as hooking can be set more clearly.

[0111] Industrial Applicability

[0112] The long object inspection device disclosed in the present invention can be applied to elevators.

[0113] Description of symbols

[0114] 1: elevator; 2: hoistway; 3: pit; 4: traction machine; 5: main rope; 6: car; 7: counterweight; 8: control panel; 9: return rope pulley; 10: speed limiter; 11: speed limiter rope; 12: tensioning pulley; 13: long object inspection device; 14: camera; 15: information processing device; 16: image processing unit; 17: determination unit; 18: sheave detection unit; 19: long object detection unit; 20: panoramic image generation unit; 100a: processor; 100b: memory; 200: dedicated hardware.

Claims

1. A long object inspection device, which detects abnormalities of a long object wound around a sheave in a hoistway for an elevator car to travel, wherein: The long object inspection device comprises: A photographing unit that photographs one or both of the upper side and the lower side of the car in such a manner that the long object and the sheave are included in a photographing range; An image processing unit, which performs image processing on the image captured by the imaging unit to detect a portion of the long object; as well as a determination unit for determining whether the long object has an abnormality based on the state of the long object detected by the image processing unit, The image processing unit comprises: a sheave detection unit for performing image processing for detecting a portion of the sheave with respect to the image captured by the imaging unit; as well as The long object detection unit refers to the position of the portion of the sheave detected by the sheave detection unit and performs image processing for detecting the portion of the long object on the image captured by the imaging unit.

2. A long object inspection device, which detects abnormalities of a long object wound around a sheave in a hoistway for an elevator car to travel, wherein: The long object inspection device comprises: an image processing unit that performs image processing for detecting a portion of the long object on an image captured by a capturing unit, wherein the capturing unit captures one or both of an upper portion and a lower portion of the car in a manner that includes the long object and the sheave within a capturing range; and a determination unit for determining whether the long object has an abnormality based on the state of the long object detected by the image processing unit, The image processing unit comprises: a sheave detecting unit that performs image processing for detecting a portion of the sheave with respect to the image captured by the capturing unit; and The long object detection unit refers to the position of the portion of the sheave detected by the sheave detection unit and performs image processing for detecting the portion of the long object on the image captured by the imaging unit.

3. The long object inspection device according to claim 1 or 2, wherein: When the angle formed by the direction in which the long object detected by the long object detection unit extends on the image captured by the shooting unit and the direction in which the long object extends on the image when not hooked on the structure in the shaft is greater than a predetermined angle threshold, the determination unit determines that there is an abnormality in the long object.

4. The long object inspection device according to any one of claims 1 to 3, wherein: The sheave detection unit calculates a similarity between a portion on the image captured by the imaging unit and a preset template image, and detects a position having a similarity equal to or greater than a preset similarity threshold as the sheave portion.

5. The long object inspection device according to any one of claims 1 to 3, wherein: The sheave detection unit detects a sheave portion from the image captured by the imaging unit by a machine learning method.

6. The long object inspection device according to any one of claims 1 to 5, wherein: The sheave detection unit filters out a region including the sheave portion on the image captured by the imaging unit based on the type or installation information of the elevator, and performs image processing to detect the sheave portion in the filtered region.

7. The long object inspection device according to any one of claims 1 to 6, wherein: The elongated object detection unit detects the portion of the elongated object by specifying a linear object extending from the portion of the sheave detected by the sheave detection unit as a starting point using an edge detector.

8. The long object inspection device according to any one of claims 1 to 6, wherein: The long object detection unit takes the portion of the sheave detected by the sheave detection unit as a starting point, and sequentially extracts local images of a preset size along the traveling direction of the car, and sequentially tracks the portions with higher similarity among the close local images in the extracted local images with greater priority, thereby detecting portions of the long object.

9. The long object inspection device according to any one of claims 1 to 8, wherein: The determination unit calculates reliability of detection of the elongated object by the elongated object detection unit, and outputs the calculated reliability together with a determination result of abnormality of the elongated object.

10. The long object inspection device according to any one of claims 1 to 9, wherein: The determination unit calculates a position in the shaft of a portion where an abnormality is detected for the long object, and outputs the calculated position in the shaft together with a determination result of the abnormality of the long object.

11. The long object inspection device according to any one of claims 1 to 10, wherein: The camera unit is disposed in the car. The image processing unit includes a panoramic image generating unit that combines at least a portion of the images sequentially captured by the imaging unit along the traveling direction of the car to generate a panoramic image of the shaft throughout the traveling direction of the car. The sheave detection unit performs image processing to detect a portion of the sheave with respect to the panoramic image generated by the panoramic image generation unit, The long object detection unit refers to the position of the portion of the sheave detected by the sheave detection unit, and performs image processing to detect the portion of the long object on the panoramic image generated by the panoramic image generation unit.

12. The long object inspection device according to claim 11, wherein: The panoramic image generation unit generates a panoramic image by adding a pit image of a lower end portion of the hoistway.

Images