Elevator long object testing device

Abstract

A long object detection device is provided that is capable of more accurately detecting an abnormality of a long object in an elevator. In an elevator (1), a camera (14) captures a position above or below a cabin (6) such that the imaged area includes a long object and a grooved wheel. A long object detection device (13) comprises a grooved wheel detection unit (18), a long object detection unit (19), and an evaluation unit (17). The grooved wheel detection unit (18) performs an image processing procedure to identify a portion representing the grooved wheel from an image captured by the camera (14).The long object detection unit (19) performs an image processing process to identify a part representing the long object from the image captured by the camera (14) by referencing the position of the part representing the grooved wheel detected by the grooved wheel detection device (18). The evaluation unit (17) assesses, based on the condition of the detected long object, whether or not the long object is abnormal.

Description

[Technical field]

[0001] The present disclosure relates to an elevator long object inspection device. [State of the art]

[0002] PTL 1 discloses an example of an elevator long object inspection device. The long object inspection device includes a camera, an image pickup device, and a judgment device. The camera is provided for a car traveling in a shaft. The camera captures a position above the car, so that an image includes a main rope, which is a long object. The image pickup device performs an image processing process to extract a part constituting the main rope from the image captured by the camera. Using the processed image, that is, the image on which the image processing process has been performed by the image pickup device, the judgment device judges whether or not the main rope is caught (or snagged) on a catching object in the shaft. [Citation list][Patent literature]

[0003] [PTL 1] JP 2015-20863 A [Summary of the Invention][Problems to be Solved by the Invention]

[0004] Apart from the long object, such as the main rope, which is to be subjected to the judgment of whether or not an abnormality (e.g., the long object being entangled) exists, the elevator shaft is also provided with other long objects and the like that are not subjected to the abnormality judgment process. In addition, in some situations, an inner wall of the shaft may have a linear texture or the like. For this reason, if the long object inspection device in PTL 1 mistakenly recognizes one of the long objects not subjected to the judgment process or having a linear texture as a long object to be subjected to the judgment process, there is a possibility that an abnormality (e.g., a long object being entangled) may be mistakenly detected.

[0005] The present disclosure is intended to solve the above-described problem. The present disclosure provides a long object inspection device capable of more accurately detecting an abnormality of a long object of an elevator. [Means of solving the problem]

[0006] A long object inspection device according to the present disclosure that detects an abnormality of a long object guided and suspended around a grooved wheel in an elevator shaft in which a car of an elevator travels, the long object inspection device comprising: an image pickup unit that images a position above and / or a position below the cabin such that an imaged area includes the long object and the grooved wheel; an image processing unit that performs an image processing process to recognize a part representing the long object from an image captured by the image processing unit; and a judgment unit that judges whether the abnormality of the long object exists or not based on a state of the long object detected by the image processing unit, wherein the image processing unit comprises: a grooved wheel recognition unit that performs an image processing process to recognize a part representing the grooved wheel from the image captured by the image processing unit; and a long object recognition unit that performs an image processing process to recognize the part constituting the long object from the image captured by the image processing unit by referring to a position of the part constituting the grooved wheel detected by the grooved wheel recognition unit.

[0007] A long object inspection device according to the present disclosure that detects an abnormality of a long object guided and suspended around a grooved wheel in an elevator shaft in which a car of an elevator travels, the long object inspection device comprising: an image processing unit that performs an image processing process to recognize a part representing the long object from an image captured by an image pickup unit that images one or both positions above and below the cab so that an imaged area includes the long object and the grooved wheel; and a judging unit that judges whether the abnormality of the long object exists or not based on a state of the long object detected by the image processing unit, wherein the image processing unit comprises: a grooved wheel recognition unit that performs an image processing process to recognize a part representing the grooved wheel from the image captured by the image processing unit; and a long object recognition unit that performs an image processing process to recognize the part constituting the long object from the image captured by the image processing unit by referring to a position of the part constituting the grooved wheel detected by the grooved wheel recognition unit. [Advantageous effects of the invention]

[0008] The long object inspection device according to the present disclosure makes it possible to detect the abnormality of the long object of the elevator more accurately. [Brief description of the drawings] Fig. 1 is a side view showing a configuration of an elevator according to the first embodiment. Fig. 2 is a block diagram showing functions of a long object inspection apparatus according to the first embodiment. Fig. 3 is a drawing showing an example of an image taken by a camera according to the first embodiment. Fig. 4 is a side view showing a state in which an abnormality has occurred in a governor rope of the elevator according to the first embodiment. Fig. 5 is a drawing showing an example of an image taken by the camera when an abnormality occurred on the governor rope of the elevator according to the first embodiment. Fig. 6 is a drawing showing the location where the abnormality occurred on the governor rope of the elevator according to the first embodiment. Fig. 7 is a drawing showing the location where the abnormality occurred on the governor rope of the elevator according to the first embodiment. Fig. 8 is a flowchart showing the example of the movement of the long object inspection apparatus according to the first embodiment. Fig. 9 is a hardware configuration diagram of a main part of the long object inspection apparatus according to the first embodiment. Fig. 10 is a block diagram showing the functions of the long object inspection apparatus according to the second embodiment. Fig. 11 is a drawing showing an example of a panoramic image of a shaft according to the second embodiment. [Description of the embodiments]

[0009] Embodiments for carrying out what is disclosed herein will be explained with reference to the accompanying drawings. In the drawings, some of the elements that are the same or corresponding to each other are denoted using the same reference numerals, and duplicate explanations are simplified or omitted where appropriate. In addition, what is disclosed herein is not limited to the following embodiments, and it is possible to modify any components of the embodiments or omit any components of the embodiments without departing from the gist of the present disclosure. First embodiment

[0010] Fig. 1 is a side view showing a configuration of an elevator 1 according to the first embodiment.

[0011] Elevator 1 is applied to a building with a large number of floors. Fig. 1 shows an example of the elevator 1 viewed from the side. A shaft 2 for the elevator 1 is provided in the building. The shaft 2 is a long space extending upward and downward between the plurality of floors. A pit 3 is provided in a lower end portion of the shaft 2. The elevator 1 includes a traction machine 4, a main rope 5, a car 6, a counterweight 7, and a control panel 8.

[0012] The tractor 4 includes a disk and a motor. The disk of the tractor 4 is connected to a rotation shaft of the motor of the tractor 4. The motor of the tractor 4 is a piece of equipment that generates a driving force to rotate the disk of the tractor 4.

[0013] The main rope 5 is wound and suspended around the sheave of the tractor 4. The main rope 5 supports a load of the car 6 by hanging the car 6 in the shaft 2. The main rope 5 supports a load of the counterweight 7 by hanging the counterweight 7 in the shaft 2. In this example, the main rope 5 is wound and suspended around a pulley 9. The main rope 5 supports the load of the car 6 on one side of the pulley 9. The main rope 5 supports the load of the car 6 on the other side of the pulley 9.

[0014] When the main rope 5 moves due to the rotation of the pulley of the traction machine 4, the car 6 and the counterweight 7 travel in opposite directions in the shaft 2. The car 6 is a piece of equipment that transports passengers and the like between a plurality of floors by traveling up and down within the shaft 2. The counterweight 7 is a piece of equipment that balances the car 6 with the load applied to the main rope 5 on both sides of a pulley, such as the deflection pulley 9, on which the main rope 5 is wound and suspended.

[0015] The control panel 8 is equipment that controls the movement of the elevator 1. For example, the control panel 8 controls the movement of the car 6. The control panel 8 has a function installed to maintain the position of the car 6 within the shaft 2.

[0016] The elevator 1 includes a governor 10, a governor rope 11, and a tension pulley 12. The governor 10 is equipment that prevents the car 6 from traveling at excessive speeds. The governor 10 includes a pulley. The governor rope 11 is wound and suspended around the pulley of the governor 10. Both ends of the governor rope 11 are attached to the car 6. The governor rope 11 is wound and suspended around the tension pulley 12. The tension pulley 12 is a pulley that applies tension to the governor rope 11. For example, the tension pulley 12 can be provided in the pit 3. The pulley of the governor 10 rotates in coordination with the movement of the car 6 via the governor rope 11, which is connected to the car 6. If the rotation speed of the roller is too high, the limiter 10 inhibits the excessive running speed of the car 6.

[0017] A long object inspection device 13 (or long object inspection device) is applied to the elevator 1. The long object inspection device 13 is a device that detects an abnormality of a long object (or long object) in the elevator shaft 2. The long object to be subjected to abnormality detection by the long object inspection device 13 is equipment that is long in one direction. When there is no abnormality, the longitudinal direction of the long object is parallel to the traveling direction of the car 6. In this example, the long object moves in the shaft 2 in conjunction with the operations of the elevator 1. The long object can be, for example, the main rope 5, the governor rope 11, or the like.The long object may be a counterweight rope (not shown) that compensates for an imbalance between a dead load of the main rope 5 on the cab 6 side and a dead load of the main rope 5 on the counterweight 7 side, which arises due to the movement of the main rope 5. The long object may be a control cable used to transmit an electrical signal or to supply power. In other examples, the long object may be a stranded rope, a belt, or a chain. The long object is wound and suspended around a grooved wheel (or sprocket). For example, the grooved wheel may be a pulley, such as the sheave of the tractor 4, the pulley of the governor 10, the idler pulley 9, the tensioner pulley 12, a return pulley, or a landing gear suspension. For example, the grooved wheel includes a part through which the long object passes or reverses.The long object inspection device 13 includes a camera 14 and a data processing device 15.

[0018] The camera 14 is equipped with a function to image the interior of the shaft 2. The camera 14 is an example of the image recording unit. The camera 14 records images inside the shaft 2 so that an image area includes the long object and the grooved wheel around which the long object is guided and suspended. In this example, the camera 14 is mounted on the underside of the floor of the cabin 6. For example, the camera 14 images a position below the cabin 6. Alternatively, the camera 14 can be mounted on the top of the ceiling of the cabin 6. In this situation, the camera 14 images, for example, a position above the cabin 6. In another example, the camera 14 can be installed in the shaft 2. In this situation, the camera 14 images, for example, a lower part of the shaft 2.

[0019] The image pickup unit may be a camera that captures both a position above and a position below the booth 6. Furthermore, the image pickup unit may include a plurality of cameras. In this situation, the image pickup unit may include a camera that captures a position above the booth 6 and another camera that captures a position below the booth 6. Furthermore, the long object inspection device 13 may use a camera of an external device as the image pickup unit. In other words, the data processing device 15 of the long object inspection device 13 may detect the abnormality of the long object using an image captured by the camera of the external device.

[0020] The data processing device 15 is a part responsible for data processing related to detecting the abnormality of the long object. The data processing device 15 is connected to the camera 14 to receive the image captured by the camera 14. The data processing device 15 can be provided, for example, on top of the cabin 6.

[0021] Fig. 2 is a block diagram showing the functions of the long object inspection apparatus 13 according to the first embodiment.

[0022] The data processing device 15 includes an image processing unit 16 and a judgment unit 17. The image processing unit 16 is a part in which a function is installed to perform an image processing process for recognizing the long object from the image captured by the camera 14. The image processing unit 16 has a function installed to obtain the image captured by the camera 14. The judgment unit 17 is a part in which a function is installed to judge whether or not an abnormality of the long object exists based on a condition of the long object detected by the image processing unit 16. The judgment unit 17 has a function installed therein to output a judgment result on whether or not the abnormality exists, and the like, to the control panel 8.The image processing device comprises a grooved wheel detection unit 18 and a long object detection unit 19.

[0023] In the grooved wheel detection unit 18, a function is installed to perform an image processing process for detecting a part constituting the grooved wheel on which the long object is wound and suspended from the image captured by the camera 14.

[0024] The grooved wheel recognition unit 18 recognizes the part constituting the grooved wheel, for example, by applying a template comparison method. The grooved wheel recognition unit 18 calculates a similarity degree with respect to each of the various parts of the image captured by the camera 14 by performing a comparison with a template image that has been set in advance and represents a correct response image of the grooved wheel. In this situation, the grooved wheel recognition unit 18 recognizes, for example, a part whose calculated similarity level is equal to or higher than a preset similarity threshold as the part constituting the grooved wheel.

[0025] The grooved wheel recognition unit 18 can specify the part representing the grooved wheel by learning feature values ​​in images by implementing a machine learning method and employing a discriminator using the feature values. For example, the grooved wheel recognition unit 18 can use a histogram of oriented gradients (HOG) or a scale invariance feature transformation (SIFT) scheme as the feature values. For example, the grooved wheel recognition unit 18 can use a support vector machine (SVM) as the discriminator. The grooved wheel recognition unit 18 can specify the part representing the grooved wheel by implementing a deep learning method.

[0026] Alternatively, the grooved wheel recognition unit 18 may detect a marker attached to the grooved wheel to recognize the grooved wheel based on a result of the mark recognition. Examples of the marker attached to the grooved wheel include a sticker affixed to display a predetermined color or a code expressed in a pattern. The grooved wheel recognition unit 18 may narrow down an area including the part constituting the grooved wheel from the image captured by the camera 14 based on the type or installation information of the elevator 1. In this situation, the grooved wheel recognition unit 18 performs an image processing process to recognize the part constituting the grooved wheel from the narrowed down area. The information about the type of the elevator 1 may include, for example, information about the model or a model number of the elevator 1.The installation information of the elevator 1 includes information about the installation position of the grooved wheel or the like. The type or installation information of the elevator 1 can be set in advance in the grooved wheel detection unit 18 or obtained by the grooved wheel detection unit 18 from the control panel 8 of the elevator 1 or the like.

[0027] The long object detection unit 19 performs an image processing process to detect a part constituting the long object from the image captured by the camera 14 by referring to the position of the part constituting the grooved wheel detected by the grooved wheel detection unit 18.

[0028] For example, the long object detection unit 19 detects the part constituting the long object by implementing an edge detection method. For example, among the detected edges, the long object detection unit 19 detects an edge passing through the part constituting the grooved wheel as the part constituting the long object. The long object detection unit 19 indicates the part constituting the long object by indicating a linear object extending from the part constituting the grooved wheel detected by the grooved wheel detection unit 18 while using an edge detector. For example, the long object extends linearly along the traveling direction of the car 6, starting from the grooved wheel.

[0029] The long object recognition unit 19 can recognize the part constituting the long object by implementing a local similarity assessment method. For example, the long object recognition unit 19 can sequentially extract local images, each with a predetermined size, from the image captured by the camera 14 along the traveling direction of the car 6, starting with the part constituting the grooved wheel detected by the grooved wheel recognition unit 18. The long object recognition unit 19 calculates a degree of similarity between parts in groups of local images that are adjacent to each other among the extracted local images.In this situation, the sets of adjacent local images may, for example, each be a set of local images that are adjacent in the traveling direction of the car 6, or each be a set of local images between which the distance in the traveling direction of the car 6 within the image captured by the camera 14 is shorter than a predetermined distance. The long object recognition unit 19 recognizes the part constituting the long object by performing a tracking process while giving priority to certain parts that have higher degrees of similarity between adjacent local images among the extracted local images. As a method for sequentially recognizing the part constituting the long object starting from the position of the grooved wheel, the long object recognition unit 19 may use a tracking filter used in image recognition processes and the like.For example, the long object detection unit 19 may detect the part constituting the long object by tracking the part constituting the long object from the position of the grooved wheel while using a particle filter, a Kalman filter, or the like.

[0030] Based on the state of the long object detected by the long object detection unit 19, the judgment unit 17 judges whether the long object is abnormal or not. For example, the judgment unit 17 judges whether the long object is abnormal or not based on the position, orientation, or shape of the detected long object. The judgment unit 17 outputs the judgment result regarding whether the long object is abnormal or not to, for example, the control panel 8.

[0031] The judgment unit 17 can calculate a reliability level of the long object detection by the long object detection unit 19. For example, if the long object detection unit 19 detects the long object using the edge detector, the similarity levels between the local images, or the tracking filter, the judgment unit 17 can calculate the reliability level of the detection based on a strength or a probability of the edge. The judgment unit 17 outputs information about the calculated reliability level along with the judgment result to, for example, the control panel 8.

[0032] Furthermore, the judgment unit 17 may calculate the position within the shaft 2 where the abnormality of the long object was detected. The judgment unit 17 may calculate the position of the location within the shaft 2 based on the position within the image of the location where the abnormality was detected. In this situation, for example, the judgment unit 17 may calculate the position of the location within the shaft 2 by using the part constituting the grooved wheel detected by the grooved wheel detection unit 18 as a reference. In this situation, the judgment unit 17 may also use information about the position of the car 6 at the time of image acquisition or the like. For example, the judgment unit 17 outputs the information about the calculated position of the abnormality detection location to the control panel 8 along with the judgment result.

[0033] The control panel 8 can control the movement of the elevator 1 in accordance with the judgment result input from the judgment unit 17. For example, after receiving a judgment result indicating that there is no abnormality, the control panel 8 continues the operation of the elevator 1 without stopping the operation. In contrast, after receiving a judgment result indicating that there is an abnormality, the control panel 8 stops the operation of the elevator 1. The control panel 8 can perform a process in accordance with a reliability level of the judgment result.For example, upon receiving a judgment result indicating that an abnormality exists and having a reliability level higher than a predetermined threshold, the control panel 8 resumes the operation of the elevator 1 without requiring confirmation from a person such as a maintenance person. In this situation, if the elevator 1 has been stopped, the control panel 8 can resume the operation of the elevator 1 without requiring confirmation from a person such as a maintenance technician. In contrast, upon receiving a judgment result indicating that no abnormality exists and having a reliability level lower than the predetermined threshold, the control panel 8 can notify a maintenance technician or the like of the judgment result.In this situation, the control panel 8 can stop the operation of the elevator 1 until a person, such as a maintenance technician, confirms the condition of the long object by visiting the site. Along with the assessment result, the control panel 8 can issue a notification about the reliability level calculated by the assessment unit 17 or the location of the abnormality detection site.

[0034] Fig. 3 is a drawing showing an example of the image captured by the camera 14 according to the first embodiment.

[0035] In the present example, the long object inspection device 13 detects abnormalities of a governor rope 11, which serves as a long object. The long object inspection device 13 first detects the tension pulley 12, which serves as an example of the grooved wheel, and then detects the abnormalities, if any, of the governor rope 11. Fig. 3, structures other than the governor rope 11 and the tension pulley 12 are omitted from the drawing.

[0036] Furthermore, the long object inspection device 13 can detect an abnormality of a long object with respect to another set consisting of a long object and a grooved wheel. The set consisting of a long object and a grooved wheel to be subjected to detection by the long object inspection device 13 may be, for example, a set consisting of the governor rope 11 and a pulley of the tractor 4; and a unit consisting of a counterweight rope and a pulley on which the rope is wound and suspended.

[0037] Next, an example of the detection of the abnormality of the long object by the long object inspection device 13 will be explained with reference to FIG. Fig. 4 to 7. In this example, the long object inspection device 13 detects, as an abnormality of the long object, whether the governor rope 11 wound around the tension pulley 12 and dangling is caught on a structure in the shaft 2 or on the car 6. Examples of the structure in the shaft 2 include a casing of equipment in the shaft 2, a support, a frame, a beam, a column, or a bracket.

[0038] Fig. 4 is a side view showing a state in which an abnormality has occurred on the governor rope 11 of the elevator 1 according to the first embodiment. Fig. 1 shows the elevator 1 in the state where the governor rope 11 has no abnormality. In contrast, Fig. 4 the elevator 1 in a state in which the governor rope 11 is clamped to a structure in the shaft 2.

[0039] Fig. 5 is a drawing showing an example of an image taken by the camera 14 when an abnormality has occurred on the governor rope 11 of the elevator 1 according to the first embodiment. Fig. Figure 3 shows the example of the image taken when the governor rope 11 has no abnormality. In contrast, Fig. 5 shows an example of the image taken when the governor rope 11 is clamped to the structure in shaft 2. In this example, the structure in shaft 2 is omitted from the drawing.

[0040] Fig. 6 is a drawing showing the location where the abnormality occurred on the governor rope 11 of the elevator 1 according to the first embodiment. Fig. Figure 6 shows an enlarged view of the point where the rope catches on the structure. The dashed line in Fig. 6 shows the state of the governor rope 11 that would be observed if the rope were not caught on the structure in the shaft 2. The state, such as the orientation and position of the governor rope 11, that would be observed if the rope were not caught is obtained in advance as long as no abnormalities occur. The state, such as the orientation and position of the governor rope 11, that would be observed if the rope were not caught can be determined in advance, for example, at the time of installation of the elevator 1 or at the time of a regular inspection of the elevator 1. The long object inspection device 13 stores the determined state in a memory.

[0041] Since the rope is caught on the structure, an angle D is formed between the orientation of the governor rope 11 extending from the tension pulley 12 and the orientation of the governor rope 11 in the state that would be observed if the rope were not caught. For example, the judgment unit 17 calculates the angle D based on the detection result of the long object detection unit 19. For example, if the calculated angle D is equal to or greater than a preset angle threshold, the judgment unit 17 judges that the governor rope 11 is caught on a structure and has an abnormality.

[0042] Fig. 7 is a drawing showing the location where the abnormality occurred on the governor rope 11 of the elevator 1 according to the first embodiment. Fig. Figure 7 shows a plurality of sampling points on the detected long object. For example, the sampling points are sampled at equal intervals in the direction of travel of the car 6.

[0043] The detection unit 17 can detect an abnormality of the long object based on the linearity of the detected long object. For example, the judgment unit 17 can calculate the orientation of each of the line segments connecting any two adjacent sampling points in the traveling direction of the car 6 to judge whether the angle formed by the orientations of any two adjacent line segments is equal to or greater than a preset threshold value, indicating that the governor rope 11 is caught on a structure and has an abnormality.

[0044] In this situation, the long object inspection device 13 can detect other abnormalities besides the catch of the long object. The long object inspection device 13 can detect an abnormality such as the long object being broken, damage such as a strand being broken, or a stress defect. For example, the long object inspection device 13 can detect the breakage of the long object based on the continuity of the detected long object or the like. For example, the long object inspection device 13 can detect local damage or the like based on a change in the similarity levels of the image along the longitudinal direction of the detected long object. For example, the long object inspection device 13 can detect the stress defect or the like based on the linearity of the detected long object.

[0045] Next, an example of the movement of the long object inspection device 13 will be explained with reference to Fig. 8. Fig. 8 is a flowchart showing the example of the movement of the long object inspection device 13 according to the first embodiment.

[0046] The processes in Fig. 8 can be executed, for example, when the occurrence of an earthquake is detected in elevator 1. The processes in Fig. 8 can be carried out continuously during the operation of the elevator 1 or can be carried out at a predetermined regular or irregular time sequence. Furthermore, the processes can be carried out in Fig. 8 be carried out on the basis of an operation performed by a maintenance person or a manager or be carried out on the basis of the occurrence of an event detected in elevator 1.

[0047] In step S1, the camera 14 captures an image of the shaft 2. After that, the process of the long object inspection device 13 proceeds to step S2.

[0048] In step S2, the grooved wheel recognition unit 18 performs the process of recognizing the grooved wheel from the image captured by the camera 14. The grooved wheel recognition unit 18 outputs the information about the part constituting the grooved wheel recognized from the image. After that, the process of the long object inspection device 13 proceeds to step S3.

[0049] In step S3, the long object recognition unit 19 performs the process of recognizing the part constituting the long object by referring to the position of the part constituting the grooved wheel output from the grooved wheel recognition unit 18. The long object recognition unit 19 outputs the information about the part constituting the long object recognized from the image. After that, the process of the long object inspection device 13 proceeds to step S4.

[0050] In step S4, the judgment unit 17 judges whether an abnormality exists, such as the long object being detected, based on the state of the part constituting the long object output by the long object recognition unit 19. If an abnormality exists, the process of the long object inspection device 13 proceeds to step S5. If no abnormality exists, the process of the long object inspection device 13 proceeds to step S6.

[0051] In step S5, the judgment unit 17 outputs an abnormality signal to the control panel 8. Thereafter, the process of the long object inspection device 13 is completed.

[0052] In step S6, the judgment unit 17 outputs a normal signal to the control panel 8. After that, the process of the long object inspection device 13 is completed.

[0053] As explained above, the long object inspection device 13 according to the first embodiment detects the abnormality of the long object wrapped around the grooved wheel and suspended in the elevator shaft 2. Regarding the elevator 1, the camera 14 is provided for the car 6 traveling in the shaft 2. The camera 14 captures one or both of the positions above and below the car 6, so that the imaged area includes the long object and the grooved wheel. The long object inspection device 13 includes the image processing unit 16 and the judgment unit 17. The image processing unit 16 performs the image processing process to detect the part constituting the long object from the image captured by the camera 14. Based on the state of the long object detected by the image processing unit 16, the judgment unit 17 judges whether or not there is an abnormality of the long object.The image processing unit 16 includes the grooved wheel detection unit 18 and the long object detection unit 19. The grooved wheel detection unit 18 performs the image processing process to detect the part constituting the grooved wheel from the image captured by the camera 14. The long object detection unit 19 performs the image processing process to detect the part constituting the long object from the image captured by the camera 14 by referring to the position of the part constituting the grooved wheel detected by the grooved wheel detection unit 18.

[0054] With the above configuration, since the part constituting the long object is detected by referring to the detected part constituting the grooved wheel, it is possible to more accurately detect the long object to be subjected to the judgment of whether or not there is an abnormality, even if the elevator shaft 2 has other long objects that are not subjected to the detection process or the linear texture. Consequently, it is possible to more accurately detect the abnormality from the elevator 1, for example, when the long object is caught on a structure. If a disaster such as an earthquake occurs at the location where the elevator 1 is deployed, the elevator 1 can make an emergency stop.In this situation, it is important to check whether any of the long objects in the shaft 2, such as the governor rope 11, the main rope 5, a counterweight rope, or a traveling cable, has become entangled with a structure in the shaft 2 or any part of the car 6 in order to restore the operation of the elevator 1. If maintenance personnel or the like were to individually check a large number of elevators 1 in the earthquake-stricken area and make the corresponding confirmations, this would require a huge amount of time and expense. In contrast, it is possible to reduce time, expense, and the like in post-earthquake response by detecting the long object entangled in the camera 14 that images the interior of the shaft 2. In addition, it is possible to more effectively reduce time and expense in post-earthquake response because the abnormality of the long object is more accurately detected.

[0055] Further, the judgment unit 17 judges that the long object has an abnormality when the angle formed by the extending direction of the long object detected by the long object detection unit 19 within the image and the extending direction of the long object within the image is equal to or larger than the predetermined angle threshold in the state where the long object does not get stuck on any structure in the elevator shaft 2.

[0056] In the above configuration, since whether or not there is a sticking is judged based on a difference from the long object in the state without abnormality, it is possible to more clearly set a judgment standard for determining whether or not there is an abnormality.

[0057] Further, the grooved wheel recognition unit 18 calculates the degree of similarity by comparing the parts of the image captured by the camera 14 with the preset template image. The grooved wheel recognition unit 18 can recognize a specific position whose degree of similarity is equal to or higher than the preset similarity threshold as the part representing the grooved wheel.

[0058] Furthermore, the grooved wheel recognition unit 18 can recognize the part constituting the grooved wheel from the image captured by the camera 14 by applying a machine learning method.

[0059] With the above configuration, the grooved wheel detection unit 18 is able to determine the position of the grooved wheel from the image captured by the camera 14.

[0060] Furthermore, the grooved wheel detection unit 18 limits the area including the part constituting the grooved wheel within the image captured by the camera 14 based on the type or installation information of the elevator 1. The grooved wheel detection unit 18 performs the image processing process to detect the part constituting the grooved wheel from the limited area.

[0061] With the above configuration, the grooved wheel detection unit 18 is able to more accurately determine the position of the grooved wheel from the image captured by the camera 14.

[0062] Further, the long object detection unit 19 detects the part constituting the long object by specifying, using the edge detector, the linear object extending from the part constituting the grooved wheel detected by the grooved wheel detection unit 18.

[0063] With the above configuration, the long object detection unit 19 is capable of more accurately detecting the long object to be subjected to the judgment of whether there is an abnormality or not from among the plurality of long objects in the shaft 2.

[0064] Furthermore, the long object detection unit 19 sequentially extracts the local images, each having a predetermined size, along the traveling direction of the car 6, starting with the part representing the grooved wheel detected by the grooved wheel detection unit 18. The long object detection unit 19 detects the part representing the long object by sequentially performing the tracking process while giving priority to certain parts that have higher similarity levels between adjacent local images among the extracted local images.

[0065] With the above configuration, the long object detection unit 19 is able to detect the long object by sequentially following the local images while starting from the grooved wheel, even if it is not possible to detect the edge locally due to the effect of natural light or illumination.

[0066] Further, the judgment unit 17 calculates the reliability level of detection of a long object by the long object detection unit 19. The judgment unit 17 outputs the calculated reliability level together with the judgment result regarding the abnormality of the long object.

[0067] With the above configuration, after receiving the result of judgment from the judging unit 17, a maintenance person or the like is able to determine an order in which a recovery process should be performed based on the output of reliability.

[0068] Furthermore, the judgment unit 17 calculates the position within the elevator shaft 2 where the abnormality of the long object was detected. The judgment unit 17 outputs the calculated position within the elevator shaft 2 along with the judgment result regarding the abnormality of the long object.

[0069] With the above configuration, a maintenance person or the like can work after receiving the judgment result from the judgment unit 17 after immediately understanding the position in the shaft 2 where restoration is required.

[0070] Next, an example of a hardware configuration of the long object inspection apparatus 13 will be explained with reference to Fig. 9. Fig. 9 is a hardware configuration diagram of a main part of the long object inspection apparatus 13 according to the first embodiment.

[0071] It is possible to implement functions of the long object inspection device 13 using a processing circuit. The processing circuit includes at least one processor 100a and at least one memory 100b. In addition to or instead of the processor 100a and the memory 100b, the processing circuit may include at least one piece of dedicated hardware 200.

[0072] When the processing circuit includes processor 100a and memory 100b, the functions of long object inspection device 13 are implemented using software, firmware, or a combination of software and firmware. At least one of the two, software and firmware, is written as a program. The program is stored in memory 100b. Processor 100a implements the functions of long object inspection device 13 by reading and executing the program stored in memory 100b.

[0073] Processor 100a may be referred to as a central processing unit (CPU), processing device, arithmetic device, microprocessor, microcomputer, or DSP. For example, memory 100b may be configured using non-volatile or volatile semiconductor memory such as RAM, ROM, flash memory, EPROM, or EEPROM.

[0074] When the processing circuit includes the dedicated hardware 200, the processing circuit is implemented using, for example, a single circuit, composite circuits, a programmed processor, parallel programmed processors, an ASIC, an FPGA, or a combination thereof.

[0075] It is possible to implement each of the functions of the long object inspection device 13 using a processing circuit. Alternatively, it is also possible to implement the functions of the long object inspection device 13 collectively using a processing circuit. It is also acceptable to implement some of the functions of the long object inspection device 13 using the dedicated hardware 200, while implementing the remaining functions using software or firmware. As described herein, the circuit implements the functions of the long object inspection device 13 using the dedicated hardware 200, software, firmware, or a combination thereof. Second embodiment

[0076] In a second embodiment, some of the elements different from those in the examples disclosed in the first embodiment will be explained in detail. For the features not explained in the second embodiment, it is acceptable to adopt one of the features in the examples disclosed in the first embodiment.

[0077] Fig. 10 is a block diagram showing functions of the long object inspection apparatus 13 according to the second embodiment.

[0078] The image processing unit 16 includes a panoramic image generation unit 20. The panoramic image generation unit 20 is a part in which a function for generating a panoramic image using images sequentially captured by the camera 14 while the car 6 is moving upward or downward is installed. The panoramic image generation unit 20 generates the panoramic image by cutting out parts of the sequentially captured images and splicing them together. The panoramic image generation unit 20 can generate the panoramic image by adding an image of the pit 3 captured by the camera 14 to the spliced ​​images. In this example, the panoramic image is a single image representing the entire shaft 2.Further, in the situation where the pit 3 does not have the grooved wheel on which the long object to be subjected to the evaluation process is wound and suspended, the panoramic image generation unit 20 can generate the panoramic image without adding the image to the pit 3.

[0079] The grooved wheel detection unit 18 detects the part constituting the grooved wheel using the panoramic image generated by the panoramic image generation unit 20. Further, the long object detection unit 19 detects the part constituting the long object with reference to the detected part constituting the grooved wheel using the panoramic image generated by the panoramic image generation unit 20.

[0080] Fig. 11 is a drawing showing an example of the panoramic image of the shaft 2 according to the second embodiment.

[0081] In Fig.11, the limiter rope 11 is indicated by reference numeral 11a in the image of the pit 3. Furthermore, in the part obtained by connecting images of the shaft 2, the limiter rope 11 is indicated by reference numeral 11b.

[0082] In the panoramic image thus generated, the governor rope 11, which serves as a long object, is continuously displayed throughout the entire length of the shaft 2. Consequently, the long object detection unit 19 can detect the portion constituting the governor rope 11 throughout the entire length of the shaft 2, starting from the tension pulley 12 detected by the grooved wheel detection unit 18. In one example, the image processing unit 16 of the long object detection device can exclude a boundary between the portion obtained by combining the images of the elevator shaft 2 and the image of the pit 3 from the long object abnormality judgment process.

[0083] As explained above, the image processing unit 16 of the long object detection device according to the second embodiment includes the panoramic image generation unit 20. The panoramic image generation unit 20 generates the panoramic image of the elevator shaft 2 along the traveling direction of the car 6 by combining at least parts of the images sequentially captured by the camera 14 along the traveling direction of the car 6. The grooved wheel detection unit 18 performs the image processing process to recognize the part constituting the grooved wheel from the panoramic image generated by the panoramic image generation unit 20. The long object detection unit 19 performs the image processing process to recognize the part constituting the long object from the panoramic image generated by the panoramic image generation unit 20 by referring to the position of the part constituting the grooved wheel detected by the grooved wheel detection unit 18.Further, the panoramic image generation unit 20 generates the panoramic image so that the image of the pit 3 is added in the lower end part of the shaft 2.

[0084] With the above configuration, the image processing unit 16 is able to use the panoramic image, which is a single still image, as the processing target. Consequently, it is possible to suppress a computational load on the image processing unit 16 and a usage amount of the memory in which the images are stored. In addition, long objects that do not exhibit any abnormality are reproduced linearly along the traveling direction of the car 6 in the panoramic image. Consequently, it is possible to more clearly establish the judgment standard for determining whether or not an abnormality (e.g., jamming) exists. [Industrial applicability]

[0085] The long object inspection apparatus according to the present disclosure is applicable to elevators. [List of reference symbols]

[0086] 1 Elevator, 2 Elevator shaft, 3 Shaft pit, 4 Traction machine, 5 Main rope, 6 Cabin, 7 Counterweight, 8 Control panel, 9 Deflection pulley, 10 Governor, 11 Governor rope, 12 Tension pulley, 13 Long object inspection device, 14 Camera, 15 Data processing device, 16 Image processing unit, 17 Judgment unit, 18 Grooved wheel detection unit, 19 Long object detection unit, 20 Panoramic image generation unit, 100a Processor, 100b Memory, 200 Dedicated hardware QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited patent literature

[0000] JP 2015-20863 A

[0003]

Claims

[1] A long object inspection device that detects an abnormality of a long object wound and suspended around a grooved wheel in a shaft in which a car of an elevator travels, the long object inspection device comprising: an image pickup unit that images one or both of a position above and a position below the cabin such that a captured area includes the long object and the grooved wheel; an image processing unit that performs an image processing process to recognize a part representing the long object from an image captured by the image capturing unit; and a judging unit that judges whether the abnormality of the long object exists or not based on a state of the long object detected by the image processing unit, wherein the image processing unit includes: a grooved wheel detection unit that performs an image processing process to detect a part constituting the grooved wheel from the image captured by the image capturing unit; and a long object recognition unit that performs an image processing process to recognize the part constituting the long object from the image captured by the image pickup unit by referring to a position of the part constituting the grooved wheel detected by the grooved wheel recognition unit. [2] A long object inspection device that detects an abnormality of a long object wound and suspended around a grooved wheel in a shaft in which a car of an elevator travels, the long object inspection device comprising: an image processing unit that performs an image processing process to recognize a part representing the long object from an image captured by an image pickup unit that captures one or both of a position above and a position below the cab so that an imaged area includes the long object and the grooved wheel; and a judging unit that judges whether the abnormality of the long object exists or not based on a state of the long object detected by the image processing unit, wherein the image processing unit includes: a grooved wheel detection unit that performs an image processing process to detect a part constituting the grooved wheel from the image captured by the image capturing unit; and a long object recognition unit that performs an image processing process to recognize the part constituting the long object from the image captured by the image pickup unit by referring to a position of the part constituting the grooved wheel detected by the grooved wheel recognition unit. [3] The long object inspection apparatus according to claim 1 or 2, wherein the judging unit judges that the long object has the abnormality when an angle formed by an extending direction, within the image captured by the image capturing unit, of the long object detected by the long object detecting unit and an extending direction, within the image, of the long object in a state where the long object is not caught on a structure in the shaft is equal to or larger than a predetermined angle threshold. [4] The long object inspection apparatus according to any one of claims 1 to 3, wherein the grooved wheel recognition unit calculates a similarity degree by comparing a part of the image captured by the image capturing unit with a predetermined original image, and recognizes a position whose similarity degree is equal to or higher than a predetermined similarity threshold as the part constituting the grooved wheel. [5] The long object inspection apparatus according to any one of claims 1 to 3, wherein, from the image picked up by the image pickup unit, the grooved wheel recognition unit recognizes the part constituting the grooved wheel by applying a machine learning method. [6] The long object inspection device according to any one of claims 1 to 5, wherein the grooved wheel recognition unit defines an area including the part constituting the grooved wheel in the image captured by the image capturing unit based on a type or installation information about the elevator, and performs the image processing process to recognize the part constituting the grooved wheel from the defined area. [7] The long object inspection apparatus according to any one of claims 1 to 6, wherein the long object detection unit detects the part constituting the long object by specifying, using an edge detector, a linear object extending from the part constituting the grooved wheel detected by the grooved wheel detection unit. [8] The long object inspection apparatus according to any one of claims 1 to 6, wherein, along a traveling direction of the car, the long object recognition unit sequentially extracts local images each having a predetermined size starting from the part constituting the grooved wheel detected by the grooved wheel recognition unit, and recognizes the part constituting the long object by sequentially performing a tracking process while giving priority to a specific part having a higher degree of similarity between adjacent local images among the extracted local images. [9] The long object inspection apparatus according to any one of claims 1 to 8, wherein the judging unit calculates a reliability level of detection of the long object by the long object detection unit and outputs the calculated reliability level together with a judgment result regarding the abnormality of the long object. [10] The long object inspection apparatus according to any one of claims 1 to 9, wherein the judging unit calculates a position within the shaft at which the abnormality of the long object was detected, and outputs the calculated position within the shaft together with a judgment result regarding the abnormality of the long object. [11] Long object inspection device according to one of claims 1 to 10, wherein the image recording unit is intended for the cabin, the image processing unit comprises a panoramic image generation unit which generates a panoramic image of the shaft along a direction of travel of the car by linking at least parts of images sequentially captured by the image recording unit along the direction of travel of the car, the grooved wheel detection unit performs the image processing process to detect the part representing the grooved wheel from the panoramic image generated by the panoramic image generation unit, and the long object recognition unit performs the image processing process to recognize the part constituting the long object from the panoramic image generated by the panoramic image generation unit by referring to a position of the part constituting the grooved wheel detected by the grooved wheel recognition unit. [12] The long object inspection apparatus according to claim 11, wherein the panoramic image generation unit generates the panoramic image so that an image of a pit of a lower end part of the shaft is added.

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