Elevator hoisting steel belt defect detection device and method

Abstract

This invention relates to the field of defect detection, and more particularly to a device and method for detecting defects in elevator traction steel belts, comprising the following steps: startup preparation, data acquisition, point cloud data judgment, point cloud data processing, feature extraction, over-limit judgment, elevator stop judgment, defect judgment, and result storage; rapid detection of surface defects in the elevator traction steel belt is achieved through a laser profile sensor. High frame rate data acquisition enables the rapid acquisition of a large amount of data during a round trip of the elevator from its highest point to its lowest point and back. Real-time data processing and analysis are performed using a computer, and defects are judged through a specific algorithm, thereby completing the detection of surface defects in the elevator traction steel belt in a short time. The automated detection process reduces labor costs and time consumption; by timely detection and handling of defects such as wear and cracks on the surface of the elevator traction steel belt, the risk of elevator malfunctions during operation can be reduced, and potential safety hazards can be minimized.

Description

Technical Field

[0001] This invention relates to the field of elevator door fault monitoring, and in particular to a device and method for detecting defects in elevator traction steel belts. Background Technology

[0002] In recent years, with the continuous development of urban construction, elevators, as vertical transportation tools, have become increasingly important in buildings. In elevator systems, the traction steel belt is a key component for elevator operation, and its performance directly affects the elevator's safety, operating efficiency, and service life. The main function of the traction steel belt is to transmit the elevator's driving force and maintain the smooth movement of the elevator car. Traditional elevator traction steel belts typically consist of multiple internal steel wires and an external protective layer, with the protective layer generally made of materials such as polyurethane. This design offers advantages such as improved elevator operating efficiency, reduced operating noise, relatively long service life, and good comfort, and is therefore widely used in traction elevators. However, due to frequent use, the surface of the elevator traction steel belt will experience a certain degree of wear, and may even develop defects such as surface cracks and exposed steel wires. These defects may lead to unstable elevator operation and even potential safety hazards. Therefore, rapid, efficient, and quantitative inspection of the surface of in-service elevator traction steel belts has become an urgent technical challenge.

[0003] Several technologies exist for detecting elevator traction steel belts, but some shortcomings remain. For example, the invention patent "A High-Precision Steel Belt Flatness Measuring Device" (application number 202210238942.8) presents a laser sensor-based device for detecting the flatness of industrial steel belts. However, this device has limitations: 1. It can only measure the flatness of the steel belt and cannot detect other surface defects such as scratches, oxidation, or corrosion, limiting its comprehensiveness in various applications. 2. It has limited applicability: it is only suitable for steel belts whose surface is entirely composed of steel; it cannot accurately measure steel belts of other materials or coatings, limiting its versatility in diverse applications. The utility model patent "A Villa Elevator with a Steel Belt Position Detection Device" (application number 202223267440.2) designs a steel belt installation position detection device for elevators using traction steel belts. However, this device suffers from insufficient detection of elevator traction steel belts: it primarily focuses on the installation position but cannot monitor surface defects or quality problems in the traction steel belt during use, limiting a comprehensive assessment of the overall quality status. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] To address the shortcomings of existing technologies, the present invention aims to provide an elevator door fault monitoring system and method, which solves the aforementioned technical problems:

[0006] Comprehensive defect detection: By introducing a laser profile sensor, this patent can acquire point cloud data of the cross-sectional profile of the elevator traction steel belt and the exposed middle roller surface nearby. Using the point cloud data, the patent employs multiple steps to perform defect detection, including extracting a subset of point clouds that meets the conditions, calculating indicators such as peak-to-peak value and standard deviation, and determining whether these indicators exceed preset thresholds, thereby determining whether a defect exists.

[0007] Universal applicability of the technology: The point cloud data is acquired by using a laser contour sensor. This technology can be applied not only to steel strips whose surface is made entirely of steel, but also to steel strips of other materials or coatings. The point cloud data processing method is not limited to specific types of materials, but is based on the characteristics of point cloud data and has a certain degree of universality.

[0008] Comprehensive elevator traction steel belt condition assessment: The detection method in the patent not only focuses on the installation position of the elevator traction steel belt, but also achieves a comprehensive assessment of the overall quality condition of the elevator traction steel belt during use by detecting surface defects. By setting thresholds and the number of frames exceeding the limit, the patent can determine whether there are defects such as breakage or wear in the elevator traction steel belt, thereby providing more comprehensive quality information.

[0009] (II) Technical Solution

[0010] To achieve the above objectives, the present invention provides the following technical solution: an elevator door fault monitoring system and method, comprising the following steps:

[0011] Start-up preparation: Pass the elevator traction steel belt through the upper roller, middle roller, and lower roller, and fix the vertical connecting plate. Start the elevator and activate the elevator traction steel belt defect detection device of the present invention.

[0012] Data Acquisition: The computer receives the elevator start signal, sets the over-limit frame number F to 0, and simultaneously reads one frame of point cloud data from the laser contour sensor {(x i ,z i )}, i=1,2,…,N;

[0013] Point cloud data judgment: The computer judges a frame of point cloud data acquired by the laser contour sensor and determines whether the number of point cloud data N is greater than or equal to the set value k*M. If it is, continue; otherwise, discard the frame of data and return to the step data acquisition.

[0014] Point cloud data processing: The average of the maximum and minimum z-coordinates is calculated in a computer, and filtering conditions Q are set. The point cloud data is then processed to extract points that meet specific conditions, forming a subset {(x...}}. 1i ,z 1i )}, and count the number W of points that meet the conditions;

[0015] Feature extraction: In a computer, for a subset of a point cloud {(x 1i ,z 1i The z-coordinate in the given coordinates is calculated, including the average value. Peak-to-peak value Z d and standard deviation S z ;

[0016] Exceeding limits: Determining the average value in a computer. Peak-to-peak value Z d Standard deviation S z If at least one indicator exceeds the limit, increment the number of frames exceeding the limit F and proceed to the next step; otherwise, return to the data acquisition step.

[0017] Elevator stop detection: Determine if the computer has received an elevator stop signal. If yes, proceed to the next step; otherwise, return to step data acquisition.

[0018] Defect detection: In the computer, determine whether the number of frames exceeding the limit F exceeds a given threshold F. H When the number of frames exceeding the limit F exceeds a given threshold F H If the surface of the elevator traction steel belt is found to be damaged or worn, it is considered to be qualified.

[0019] Results storage: The computer digitally stores the three-dimensional point cloud and results of all elevator traction steel belt surface defect detection.

[0020] Preferably, the data acquisition process includes the following specific steps: Receiving the elevator start signal: The computer receives the elevator start signal through an interface or sensor connected to the elevator system; Setting the number of over-limit frames F to 0: Before starting the acquisition of point cloud data, the computer sets the number of frames F used to determine over-limits to 0; Reading one frame of point cloud data from the laser contour sensor: The computer connects to the laser contour sensor and reads one frame of point cloud data through a communication protocol or interface.

[0021] {(x i ,z i )},i=1,2,…,N, are used to represent the cross-sectional profile of the elevator traction steel belt and the exposed middle roller surface near it in the current frame.

[0022] Preferably, the point cloud data processing steps include the following specific steps:

[0023] Calculate the average of the maximum and minimum z-coordinates: Set For all point cloud data in this frame {(x i ,z i Calculate the average of the z-coordinates of the 100 largest points in the array; set For all point cloud data in this frame {(x i ,zi Calculate the average of the z-coordinates of the 100 smallest points in the array.

[0024] Filter criteria settings: Set the filter criterion to Q, and then set it to...

[0025] Point cloud data judgment: For each point (x) i ,z i Determine its height coordinate z. i Does the point (x) meet the filtering condition Q? If it does, then select that point (x). i ,z i Add ) to the point cloud subset to form a new set {(x 1i ,z 1i If the condition is not met, the data is removed, where i represents the index of a point in the set, i = 1, 2, ..., W, and W is the number of points in the point cloud data of that frame that satisfy the above conditions.

[0026] Preferably, in the feature extraction step, the average value of all z-coordinates is... The expression is:

[0027] Preferably, in the feature extraction step, the peak-to-peak value Z is obtained by subtracting the minimum value from the maximum value of all z-coordinates. d The expression is: Z d =z max -z min .

[0028] Preferably, in the feature extraction step, the standard deviation S of all z-coordinates is... z The expression is:

[0029] An elevator traction steel belt defect detection device includes: a vertical connecting plate, a horizontal connecting plate fixed to the upper left side of the vertical connecting plate, a transition plate fixed to the upper front side of the horizontal connecting plate, a laser profile sensor fixed to the lower right side of the transition plate, and an upper roller, a middle roller, and a lower roller sequentially fixed to the upper, middle, and lower right sides of the vertical connecting plate. The laser profile sensor is electrically connected to a computer. The linear laser emitted by the laser profile sensor is parallel to the axis of the middle roller.

[0030] (III) Beneficial Effects

[0031] The purpose of this invention is to provide an elevator door fault monitoring system and method. This system utilizes a laser profile sensor to rapidly detect surface defects in the elevator traction steel belt. Employing high-frame-rate data acquisition, it can quickly acquire a large amount of data during the elevator's round trip from its highest point to its lowest point and back. Real-time data processing and analysis are performed using a computer, and a specific algorithm is applied to determine defects. This automated detection process reduces labor costs and time consumption. Furthermore, by promptly detecting and addressing defects such as wear and cracks on the elevator traction steel belt surface, the risk of elevator malfunctions during operation can be reduced, minimizing potential safety hazards. The device causes minimal interference to the elevator system and does not affect normal elevator operation. Through a rationally designed device structure and the use of laser technology, it is ensured that the detection process will not negatively impact the elevator system. Attached Figure Description

[0032] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0033] Figure 1 This is a schematic diagram of the arrangement of the left and right markers on the elevator landing door or car door in the embodiments of this application.

[0034] Figure 2 This is a block diagram of the elevator system in the embodiments of this application.

[0035] Figure 3 This is a flowchart of an elevator door fault monitoring system and method according to an embodiment of this application.

[0036] Figure 4 This is a frame of point cloud data collected in this embodiment of the application, corresponding to a cross-sectional profile of the surface of the elevator traction steel belt and the middle roller.

[0037] Reference numerals: 1-Vertical connecting plate; 2-Horizontal connecting plate; 3-Adapter plate; 4-Laser contour sensor; 5-Upper roller; 6-Middle roller; 7-Lower roller; 8-Computer; 9-Traction steel belt Detailed Implementation

[0038] The following will describe the technical solution of the present invention clearly and completely. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] This invention provides a technical solution:

[0040] A defect detection device for elevator traction steel belts, such as Figure 1 As shown, it includes: a vertical connecting plate 1, a horizontal connecting plate 2 fixed to the upper left side of the vertical connecting plate 1, an adapter plate 3 fixed to the upper front side of the horizontal connecting plate 2, a laser profile sensor 4 fixed to the lower right side of the adapter plate 3, an upper roller 5, a middle roller 6 and a lower roller 7 fixed sequentially to the upper, middle and lower right sides of the vertical connecting plate 1, and the laser profile sensor 4 is electrically connected to the computer 8; the linear laser emitted by the laser profile sensor 4 is parallel to the axis of the middle roller 6.

[0041] The upper roller 5, middle roller 6, and lower roller 7 are all cylinders of the same size. Each roller has a bearing and a shaft installed inside to ensure that the outer ring of the roller can rotate. The shafts of each roller are fixed to the vertical connecting plate with bolts. The lengths of the upper roller 5, middle roller 6, and lower roller 7 are all greater than the width of the elevator traction steel belt 9 being tested. Let A, B, and C be the intersection points of the axes of the upper roller, middle roller, and lower roller with the surface of the vertical connecting plate (e.g., ...). Figure 2 To ensure the steel strip remains pressed firmly against the middle roller during testing, the value of ∠ABC ranges from 150 to 180 degrees. The diameters of the upper roller 5, middle roller 6, and lower roller 7 range from 5 mm to 200 mm.

[0042] The laser profile sensor 4 emits a linear laser beam that illuminates the surface being measured. A single measurement yields the profile point cloud data for one cross-section. The laser profile sensor 4 is bolted to the adapter plate 3, which is in turn bolted to the vertical connecting plate 1 via a horizontal connecting plate 2, thus ensuring the relative position of the laser profile sensor 4 and the intermediate roller 6. The linear laser beam emitted by the laser profile sensor 4 is parallel to the axis of the intermediate roller 5, and this beam is positioned on the surface of the intermediate roller 5 at the closest point to the generatrix of the laser profile sensor 4. The laser profile sensor 4 is connected to the computer 8 via a network cable, enabling the transmission of detection data to the computer 8.

[0043] Computer 8 contains an I / O board. An elevator start / stop signal line is led out from the elevator control cabinet, and the elevator start / stop signal is sent to computer 8 via the I / O board. The function of computer 8 is to receive point cloud data from laser contour sensor 4, execute a pre-set defect detection algorithm, and determine whether there are defects such as breakage or wear on the surface of the elevator traction steel belt. It then outputs the results. Based on the algorithm's analysis, computer 8 can output information determining the quality status of the elevator traction steel belt, such as the presence of defects.

[0044] Working principle: When this detection device is working, it uses three rollers to hold the traction steel belt on the elevator, allowing the traction steel belt to pass through... Figure 1The laser profile sensor scans the same side surfaces of the upper and lower rollers, as well as the other side surface of the middle roller, with the traction steel belt on the middle roller surface positioned closer to the laser profile sensor. Simultaneously, the linear laser emitted by the laser profile sensor is approximately perpendicular to the traction steel belt. As the traction steel belt pulls the elevator car up and down, it rapidly passes through the upper, middle, and lower rollers, pressing against the surface of the middle roller. While the traction steel belt continues to move, the laser profile sensor scans the cross-sectional data of the traction steel belt surface at a high frequency, obtaining point cloud data of one side of the traction steel belt surface.

[0045] A method for detecting defects in elevator traction steel belts, the specific process is as follows: Figure 3 As shown, it includes the following steps:

[0046] Startup preparation, detailed description below:

[0047] a. Elevator traction steel belt installation: Guide the elevator traction steel belt through the upper roller, middle roller and lower roller of the device of the present invention, ensure that the steel belt is in normal contact with the roller surface, and ensure that the bearings and shafts of the upper roller, middle roller and lower roller are installed normally so that these rollers can rotate smoothly;

[0048] b. Vertical connecting plate fixing: The vertical connecting plate is a vertical plate-like structure used to connect the mounting parts of the laser profile sensor and the roller. The vertical connecting plate is fixed in a suitable position using brackets to ensure that the relative position of the laser profile sensor and the roller does not change.

[0049] c. Start the elevator: Start the elevator system to ensure the elevator traction steel belt begins to move. At this time, the traction steel belt will pass through the upper roller, middle roller, and lower roller, and its up-and-down movement will cause the rollers to rotate; the elevator traction steel belt defect detection device is activated.

[0050] d. Start the elevator traction steel belt defect detection device: Start the elevator traction steel belt defect detection device of the present invention, ensure that the device starts working, start the laser profile sensor, and start collecting point cloud data. Throughout the process, it is necessary to ensure that the elevator traction steel belt can pass normally through the device of the present invention, and that the laser profile sensor can work normally, so that the subsequent defect detection process can proceed smoothly. The purpose of this step is to provide a normal operating environment and data source for subsequent detection.

[0051] Data acquisition mainly involves the preparation work for data collection, which is described in detail below:

[0052] a. Receiving elevator start signal: The computer receives the elevator start signal through the interface or sensor connected to the elevator system. This signal can be the signal from the elevator start button or a signal from other sensors that detect the elevator starting to run.

[0053] b. Set the number of frames exceeding the limit F to 0: Before starting to collect point cloud data, the computer sets the number of frames F used to judge the limit to 0. The number of frames exceeding the limit F is an important parameter used to calculate the surface defects of the traction steel strip in subsequent defect detection.

[0054] c. Read a frame of point cloud data from the laser contour sensor: The computer connects to the laser contour sensor and reads a frame of point cloud data {(x i ,z i The point cloud data represents a cross-sectional profile of the elevator traction steel belt and the exposed intermediate roller surface nearby. This cross-sectional profile is composed of N data points, each with coordinates (x, y, y). i ,z i ), where x i Let z be the transverse coordinate of the cross section. i These are the coordinates in the height direction.

[0055] In general, in this step, the computer prepares for data acquisition by receiving the elevator start signal, sets the over-limit frame number F to 0, and then reads a frame of point cloud data from the laser profile sensor to prepare for subsequent surface defect detection of the traction steel belt. This data will be used to analyze the shape and characteristics of the elevator traction steel belt surface.

[0056] Point cloud data evaluation: In this step, the computer evaluates a frame of point cloud data acquired by the laser contour sensor to determine if there is sufficient data for subsequent analysis. The following is a detailed description:

[0057] a. Determine if the number of point cloud data points N is greater than or equal to the set value k*M: N represents the number of data points in a frame of point cloud data, determined by (x i ,z i The coordinates are composed of k*M, where k is a coefficient and M represents the number of contour points per frame designed by the laser contour sensor. Therefore, k*M represents a threshold used to determine whether the point cloud data is sufficient, ensuring that the amount of data is adequate.

[0058] b. Continue or discard frame data: If N≥k*M, it means that the frame of point cloud data contains enough data points and can continue to be processed. If N<k*M, it means that there are not enough data points and the frame data needs to be discarded. Return to the data acquisition step to obtain new point cloud data.

[0059] Overall, this step ensures sufficient statistical significance for subsequent data analysis by determining whether the amount of point cloud data reaches a set threshold. Insufficient data points may lead to inaccurate analysis results, therefore appropriate processing is necessary when the data volume is insufficient. This step ensures that the amount of data used in the analysis process is reliable.

[0060] Point cloud data processing: In this step, the average of the maximum and minimum z-coordinates is calculated, the point cloud data is processed, points that meet specific conditions are extracted, and the number of points that meet the conditions is counted. The following is a detailed description:

[0061] a. Calculate the average of the maximum and minimum z-coordinates: Set For all point cloud data in this frame {(x i ,z i Calculate the average of the z-coordinates of the 100 largest points in the array; set For all point cloud data in this frame {(x i ,z i Calculate the average of the z-coordinates of the 100 smallest points in the array.

[0062] b. Filter criteria settings: Set the filter criteria to Q, specifically set as follows:

[0063] c. Point cloud data judgment: For each point (x i ,z i Determine its height coordinate z. i Does the point (x) meet the filtering condition Q? If it does, then select that point (x). i ,z i Add ) to the point cloud subset to form a new set {(x 1i ,z 1i If the condition is not met, the data is removed, where i represents the index of a point in the set, i = 1, 2, ..., W, and W is the number of points in the point cloud data of that frame that satisfy the above conditions.

[0064] Feature extraction: In the feature extraction step, for the point cloud subset {(x 1i ,z 1i The z-coordinate will be calculated, including the average value. Peak-to-peak value Z d and standard deviation S z The following is a detailed description:

[0065] a. Calculate the average of all z-coordinates. Its expression is:

[0066] b. Calculate the peak-to-peak value Z by subtracting the minimum value from the maximum value of all z-coordinates. d Its expression is: Z d =z max -z min ;

[0067] c. Calculate the standard deviation S of all z-coordinates.z Its expression is:

[0068] Exceeding limits: In this step, for a specific point cloud subset {(x 1i ,z 1i The average value calculated on )} Peak-to-peak value Z d and standard deviation S z Three characteristic indicators are used to determine thresholds and whether defects exist. The specific steps are as follows:

[0069] a. Set threshold: Set the threshold These are used to determine whether the average value, peak-to-peak value, and standard deviation exceed the limits.

[0070] b. Determine if the limit is exceeded: Compare the calculated average value. Is it greater than Compare the calculated average value Z d Is it greater than Compare the calculated average value S z Is it greater than

[0071] c. Counting frames exceeding limits: If at least one indicator exceeds the limit, increment the number of frames exceeding the limit F by one and proceed to the next step; otherwise, return to step data acquisition.

[0072] Elevator Stop Detection: In the elevator stop detection step, the system monitors whether the computer has received an elevator stop signal. The following is a detailed description:

[0073] a. Elevator Stop Signal Monitoring: The computer receives elevator operating status signals via a signal line connected to the elevator system. When the elevator system sends a stop signal to the computer, the computer will detect this signal.

[0074] b. Determine if a stop signal has been received: If the computer successfully receives the elevator stop signal, it means that the elevator has stopped running.

[0075] c. Decision on the next step: If an elevator stop signal is received, proceed to the next step, which involves determining whether there is damage or wear on the surface of the elevator traction steel belt. If no elevator stop signal is received, return to the data acquisition step and continue waiting for the elevator to start and for data acquisition to proceed.

[0076] The purpose of this step is to inspect the surface of the elevator traction steel belt for defects after ensuring the elevator has stopped. This avoids inspection during elevator operation, ensuring safety and data accuracy.

[0077] Defect detection: In this step, it is determined whether the number of frames exceeding the limit F exceeds a given threshold F.H The purpose is to determine whether there are defects such as breakage or wear on the surface of the elevator traction steel belt. The following is a detailed description:

[0078] a. Compare the number of frames exceeding the limit with the threshold: Determine whether the number of frames exceeding the limit F exceeds the given threshold F. H

[0079] b. Determine the surface condition of the elevator traction steel belt: If the number of excessive frames F exceeds the given threshold F H If the detected elevator traction steel belt is found to have surface defects such as breakage or wear, then the excess frame count F is considered to be within the given threshold F. H If the surface of the elevator traction steel belt is deemed acceptable, then the elevator traction steel belt is deemed to be of acceptable quality.

[0080] Inspection of the other side surface: To meet the inspection requirements of the other side surface of the steel strip, the inspection device is reinstalled, the installation of the other surface is completed, and the same method is used to complete the defect inspection of the other side surface.

[0081] Result Storage: The computer stores the 3D point cloud data acquired by the laser contour sensor. Each point cloud data point contains geometric information about the surface of the elevator traction steel belt, including the lateral coordinate x. i , height direction coordinate z i It stores the inspection results for each elevator traction steel belt, indicating whether there are defects such as damage or wear on the surface. Digital storage allows for convenient management and analysis of large amounts of inspection data, helping maintenance personnel to understand the condition of the elevator traction steel belt in a timely manner and prevent potential problems.

[0082] Finally, manual inspection is conducted as needed. In this step, any elevator traction steel belts found to be defective undergo further manual inspection to confirm the defect. The following is a detailed description: Locating the Defect: Based on the digitally stored inspection results, determine the specific location and area of ​​the defective elevator traction steel belt. Visual Inspection: Maintenance personnel or professional technicians focus their vision on the area marked as defective, carefully observing the surface of the elevator traction steel belt. Confirming the Defect Type: Confirm the specific type of defect, such as wear, cracks, corrosion, etc. This helps in developing a repair or replacement plan. Measuring Defect Dimensions: Use measuring tools, such as a tape measure or other appropriate measuring equipment, to measure the dimensions of the defect. This helps assess the severity of the defect. Recording Inspection Results: Record the results of the manual inspection, including information such as the location, type, and size of the defect. This information can be used for subsequent maintenance records and analysis. Deciding on Repair or Replacement: Based on the results of the manual inspection, develop a repair or replacement plan. Small defects may need to be repaired, while severe defects may require replacing the entire elevator traction steel belt. Updating Digital Storage: If repair or replacement has been performed, update the corresponding data in the digital storage promptly to reflect the actual maintenance operation. Manual inspection complements digital inspection results, providing more detailed and intuitive information to help maintenance personnel make correct decisions and take appropriate maintenance measures.

[0083] In a specific embodiment of this invention, the laser contour sensor used is the Hikrobot MV-DP3300-01H, and the computer is a Dell Precision 3581 workstation laptop. A frame of point cloud data was collected as follows: Figure 4 As shown, this is a cross-sectional profile of the surface of the elevator traction steel belt and the middle roller. The workstation laptop is running Windows 10 and the software program is developed in C++.

[0084] If the scanning frame rate of the laser profile sensor is set to 1KHz and the movement speed of the elevator traction steel belt being detected is set to 2m / s, then the actual spacing between the cross sections of each frame of the detected steel belt is 2mm.

[0085] This invention discloses an elevator traction steel belt defect detection device and method, which achieves rapid detection of surface defects in the elevator traction steel belt using a laser profile sensor. Employing high frame rate data acquisition, it can rapidly acquire a large amount of data during a round trip of the elevator from its highest point to its lowest point and back. Real-time data processing and analysis are performed using a computer, and defects are identified through a specific algorithm, thus completing the detection of surface defects in the elevator traction steel belt in a short time. The automated detection process reduces labor costs and time consumption. The detection results and 3D point cloud data are digitally stored, enabling digital archiving of the elevator traction steel belt surface condition. This digital management facilitates subsequent data analysis, maintenance, and recording, improving the overall manageability of the elevator system. By promptly detecting and addressing defects such as wear and cracks on the elevator traction steel belt surface, the risk of elevator malfunctions can be reduced, minimizing potential safety hazards. The device has virtually no interference with the elevator system and does not affect normal elevator operation. Through a rationally designed device structure and the use of laser technology, it is ensured that the detection process will not negatively impact the elevator system.

[0086] In summary, this invention, by introducing modern sensor and computer technology, provides an efficient and digital method for detecting surface defects in elevator traction steel belts, offering strong support for the safe operation and maintenance of elevator systems.

[0087] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for detecting defects in elevator traction steel belts, characterized in that, Includes the following steps: Start-up preparation: Pass the elevator traction steel belt through the upper roller, middle roller, and lower roller, and fix the vertical connecting plate. Start the elevator and activate the elevator traction steel belt defect detection device. Data acquisition: the computer receives the elevator start signal, sets the overrun frame number F as 0, and the computer reads a frame of point cloud data {(x i ,z i )},i=1,2,…,N, wherein N is the number of point cloud data; Point cloud data judgment: The computer judges a frame of point cloud data acquired by the laser contour sensor. It judges whether the number of point cloud data N is greater than or equal to a set value. If the judgment is successful, it continues; otherwise, it discards the frame of data and returns to the step data acquisition. Point cloud data processing: calculate the average of the maximum and minimum z coordinates in the computer and set the screening condition Q, process the point cloud data {(x i ,z i )}, extract points that meet the specific conditions, form a point cloud subset {(x 1i ,z 1i )}, and count the number of points W that meet the conditions; Feature extraction: In a computer, for a subset of a point cloud {(x 1i ,z 1i The z-coordinate in the given coordinates is calculated, including the average value. Peak-to-peak value Z d and standard deviation S z ; Exceeding limits: Determining the average value in a computer. Peak-to-peak value Z d Standard deviation S z If at least one indicator exceeds the limit, increment the number of frames exceeding the limit F and proceed to the next step; otherwise, return to the data acquisition step. Elevator stop detection: Determine if the computer has received an elevator stop signal. If so, proceed to the next step. Otherwise, return to step data collection; Defect detection: In the computer, determine whether the number of frames exceeding the limit F exceeds a given threshold F. H When the number of frames exceeding the limit F exceeds a given threshold F H If the surface of the elevator traction steel belt is found to be damaged or worn, it is considered to be qualified. Results storage: The computer digitally stores the three-dimensional point cloud and results of all elevator traction steel belt surface defect detection.

2. The method for detecting defects in elevator traction steel belts according to claim 1, characterized in that, The data acquisition process includes the following specific steps: Receiving elevator start signal: The computer receives the elevator start signal through an interface or sensor connected to the elevator system; Set the number of frames exceeding the limit F to 0: Before starting to collect point cloud data, the computer sets the number of frames F used to determine the limit to 0; Reading a frame of point cloud data from a laser contour sensor: The computer connects to the laser contour sensor and reads a frame of point cloud data via a communication protocol or interface. i ,z i )},i=1,2,…,N, are used to represent the cross-sectional profile of the elevator traction steel belt and the exposed middle roller surface near it in the current frame.

3. The method for detecting defects in elevator traction steel belts according to claim 1, characterized in that, The point cloud data processing steps include the following specific steps: Calculate the average of the maximum and minimum z-coordinates: Set For all point cloud data in this frame {(x i ,z i Calculate the average of the z-coordinates of the 100 largest points in the array; set For all point cloud data in this frame {(x i ,z i Calculate the average of the z-coordinates of the 100 smallest points in the array. Filter criteria settings: Set the filter criterion to Q, and then set it to... Point cloud data judgment: For each point (x) in the point cloud data i ,z i Determine its height coordinate z. i Does the point (x) meet the filtering condition Q? If it does, then select that point (x). i ,z i Add ) to the point cloud subset to form a new set {(x 1i ,z 1i If the condition is not met, the data is removed, where i represents the index of a point in the set, i = 1, 2, ..., W, and W is the number of points in the point cloud data of that frame that satisfy the above conditions.

4. The method for detecting defects in elevator traction steel belts according to claim 1, characterized in that, In the feature extraction step, the average value of all z-coordinates The expression is:

5. The method for detecting defects in elevator traction steel belts according to claim 1, characterized in that, In the feature extraction step, the peak-to-peak value Z is obtained by subtracting the minimum value from the maximum value of all z coordinates. d The expression is: WITH d =z max -With min 。 6. The method for detecting defects in elevator traction steel belts according to claim 1, characterized in that, In the feature extraction step, the standard deviation S of all z coordinates z The expression is:

7. A defect detection device for implementing the elevator traction steel belt defect detection method according to any one of claims 1-6, characterized in that, include: A vertical connecting plate (1) is provided, with a horizontal connecting plate (2) fixed to the upper left side of the vertical connecting plate (1). A transition plate (3) is fixed to the upper front side of the horizontal connecting plate (2). A laser profile sensor (4) is fixed to the lower right side of the transition plate (3). An upper roller (5), a middle roller (6), and a lower roller (7) are fixed to the upper, middle, and lower right sides of the vertical connecting plate (1) in sequence. The laser profile sensor (4) is electrically connected to a computer (8). The line laser emitted by the laser profile sensor (4) is parallel to the axis of the middle roller (6).

8. The elevator traction steel belt defect detection device according to claim 7, characterized in that, The lengths of the upper roller (5), middle roller (6) and lower roller (7) are all greater than the width of the elevator traction steel belt (9) being tested.

9. The elevator traction steel belt defect detection device according to claim 7, characterized in that, The angle range of the intersection point between the upper roller (5), the middle roller (6), the lower roller (7) and the surface of the vertical connecting plate (1) is 150° to 180°.

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