WO2024119359A1 - 一种电梯曳引绳状态在线检测系统及其检测方法 - Google Patents

一种电梯曳引绳状态在线检测系统及其检测方法 Download PDF

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WO2024119359A1
WO2024119359A1 PCT/CN2022/136818 CN2022136818W WO2024119359A1 WO 2024119359 A1 WO2024119359 A1 WO 2024119359A1 CN 2022136818 W CN2022136818 W CN 2022136818W WO 2024119359 A1 WO2024119359 A1 WO 2024119359A1
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traction rope
module
image
elevator
state
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PCT/CN2022/136818
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English (en)
French (fr)
Inventor
张强
赵广浩
田莹
顾颉颖
马忠君
冯龙
刘峻铭
张赫哲
孙常亮
刘海滨
谢飞
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Qingdao Special Equipment Inspection And Research Institute
Shandong University of Science and Technology
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Qingdao Special Equipment Inspection And Research Institute
Shandong University of Science and Technology
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Priority to PCT/CN2022/136818 priority Critical patent/WO2024119359A1/zh
Priority to CN202280053510.8A priority patent/CN118176157A/zh
Publication of WO2024119359A1 publication Critical patent/WO2024119359A1/zh
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B9/00—Kinds or types of lifts in, or associated with, buildings or other structures
    • B66B9/04—Kinds or types of lifts in, or associated with, buildings or other structures actuated pneumatically or hydraulically

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  • the present invention relates to the technical field of elevator fault diagnosis, and in particular to an elevator traction rope state online detection system and detection method.
  • the steel wire rope used in the elevator traction machine (i.e., the elevator traction rope) is a key device for the elevator to bear the load and has a driving effect on the rise and fall of the car.
  • the steel wire rope mainly produces three types of stress, including pulling, contact, and bending.
  • problems such as breakage and oil corrosion often occur, which eventually lead to the occurrence of elevator production safety accidents. If it cannot be detected and discovered in advance, it will cause very serious production safety accidents and endanger people's lives.
  • the wire rope is usually observed by manual visual inspection. If there are obvious defects, it is judged directly. If there are minor defects, it is necessary to use the inspection speed and a magnifying glass to assist in the judgment.
  • manual inspection cannot be carried out for a long time and efficiently. After a long time, fatigue will inevitably occur, which will affect the accuracy of the judgment.
  • due to the influence of human vision it is difficult to detect some minor problems, which results in many elevator wire ropes not being discovered in time until serious accidents such as rope breakage occur.
  • the existing technology that can be used to detect elevator traction wire ropes usually chooses weak magnetic induction technology, which is a detection technology that combines permanent magnetic instruments and leakage magnetic instruments.
  • weak magnetic induction technology is a detection technology that combines permanent magnetic instruments and leakage magnetic instruments.
  • the above methods are restricted by objective conditions, are more susceptible to interference, and have a high error rate. Since manual participation is required in the detection work, on the one hand, the detection cost is increased, and on the other hand, the accuracy of the detection is reduced.
  • the technical problem to be solved by the present invention is to provide an elevator traction rope state online detection system and detection method in view of the above-mentioned deficiencies in the prior art, and use machine vision methods to optimize the traditional weak magnetic induction detection method, thereby realizing the online detection of the elevator traction rope state.
  • the technical solution adopted by the present invention is:
  • the present invention provides an elevator traction rope state online detection system, including a weak magnetic induction module, an image acquisition module, a controller, a computer and an alarm braking module; wherein the controller includes: a magnetic field change judgment module and an image information transmission module; the computer includes an image processing module and a traction rope state judgment module; the weak magnetic induction module and the magnetic field change judgment module communicate with each other, the controller controls the image acquisition module to collect the traction rope image, and transmits the collected traction rope image to the image processing module of the computer through the image information transmission module for preprocessing, traction rope edge detection and contour fitting; the traction rope state judgment module judges the traction rope state according to the contour fitting result, and transmits the judgment result to the controller, and finally the controller controls the alarm braking module according to the judgment result, and the alarm braking module alarms and performs braking control on the elevator.
  • the controller includes: a magnetic field change judgment module and an image information transmission module; the computer includes an image processing module and a traction rope state judgment module; the weak magnetic in
  • the traction rope image acquisition module acquires the traction rope image by a high-speed CCD camera, and then transmits the image to the image processing module via the image information transmission module.
  • the weak magnetic induction module includes a sampling circuit and an A/D converter, wherein the sampling circuit collects and amplifies the magnetic field conversion signal of the traction wire rope, and the A/D converter converts the collected analog signal into a digital signal.
  • the weak magnetic induction module includes a plurality of weak magnetic sensors arranged at different angles.
  • the image information acquisition module converts the analog signal acquired by the CCD camera into a digital signal, and uses a programmable gate array to transmit the digital signal to the CCD sensor.
  • the CCD sensor converts the digital signal into an analog signal
  • the image information transmission module then converts the analog signal converted by the CCD sensor into a YUV signal and transmits it to the image processing module.
  • the present invention also provides an elevator traction rope state online detection method, which optimizes the traditional weak magnetic field detection method by combining a machine vision method, and comprises the following steps:
  • Step 1 Arrange the weak magnetic induction module and the image acquisition module in sequence along the moving direction of the traction rope;
  • Step 2 The weak magnetic induction module detects the electromagnetic field generated by the traction rope in real time, converts the induced magnetic field and magnetic flux changes into digital quantities through digital-to-analog conversion and transmits them to the magnetic field change judgment module in the controller;
  • Step 3 After the magnetic field change judgment module receives the digital signal transmitted by the weak magnetic induction module, it compares it with the set change threshold to determine whether it is necessary to detect it through the machine vision method; if the digital signal transmitted by the weak magnetic induction module is greater than or equal to the set change threshold, execute step 4 and detect it through the machine vision method; otherwise, re-execute step 2;
  • Step 4 The controller starts the image acquisition module to collect image information and position information of the abnormal magnetic field part of the traction rope, and transmits it to the computer;
  • Step 5 The image processing module in the computer receives the image information collected by the image acquisition module, and processes the collected image information to obtain a fitted contour curve equation;
  • the collected images are subjected to bilateral filtering, gradient calculation and double thresholding to obtain preliminary edge detection results of the traction wire rope.
  • the invariant moment matching method is selected, the standard contour shape of the traction rope is set as the matching template, and the connected domain marking method is selected to obtain the entire contour and boundary point coordinate values of the image and the matching template;
  • the least square method is selected to fit the discrete boundary point coordinates of the collected image to obtain the fitted contour curve equation, that is, to obtain six curve equations in the same coordinate system. Two curves in the same image are a group. Substituting a Y value can obtain two X coordinates. Three groups are obtained for three images, with a total of 6 X coordinates.
  • Step 6 The traction rope state judgment module judges the state of the traction rope according to the contour curve equation fitted in step 5, and transmits the judgment result to the controller;
  • a group of Y-axis coordinate values are randomly selected for detection, and the boundary point coordinates of the image to be detected under the same Y-axis coordinate value are compared through the fitted contour curve equation to measure the wear of the traction rope, and then the state of the traction rope is judged according to the wear of the traction rope;
  • the wear measurement standard A is defined as follows:
  • a value the more serious the wear of the traction rope is.
  • A is less than 80%, the traction rope is in a state of severe wear and needs alarm and brake maintenance. Otherwise, the traction rope is in normal working condition.
  • Step 7 The controller controls the alarm brake module to sound an alarm and perform emergency braking on the elevator according to the judgment result of the traction rope state judgment module.
  • the present invention provides an online detection system and method for the state of an elevator traction rope, (1) using machine vision to optimize the weak magnetic detection method, effectively solving the problems of being easily interfered, having a high error rate, and having low reliability in traditional detection technology. (2) using a controller to control the weak magnetic induction module of the traction rope and the image acquisition module of the traction rope, and continuously performing weak magnetic detection on the traction rope to avoid missing detection of continuous wear. (3)
  • the elevator traction rope detection system of the present invention is installed in the elevator shaft to detect the wire rope, because the traction rope in the shaft is under force and taut, and the swing amplitude is small during operation, unless the wire rope is twisted.
  • the traction rope is observed in real time by a camera, and based on image processing technology, the reliability of the detection result can be greatly improved, but the data processing process of this method is complicated.
  • the detection system uses machine vision to optimize the traditional weak magnetic induction method, and can analyze the defects of the traction rope, the defective parts, and specific image display, and automatically obtain the image data of the traction rope through non-contact and real-time detection.
  • FIG1 is a structural block diagram of an elevator traction rope state online detection system provided by an embodiment of the present invention.
  • FIG2 is a schematic diagram of an elevator traction rope state online detection system provided by an embodiment of the present invention.
  • FIG3 is a flow chart of an online detection of the state of an elevator traction rope provided by an embodiment of the present invention.
  • an elevator traction rope state online detection system is installed in the elevator shaft to detect the wire rope, as shown in Figure 1, including a weak magnetic induction module, an image acquisition module, a controller, a computer and an alarm brake module; wherein the controller includes: a magnetic field change judgment module and an image information transmission module; the computer includes an image processing module and a traction rope state judgment module; the weak magnetic induction module and the magnetic field change judgment module communicate with each other, the controller controls the image acquisition module to collect the traction rope image, and transmits the collected traction rope image to the image processing module of the computer through the image information transmission module for preprocessing, traction rope edge detection and contour fitting; the traction rope state judgment module judges the traction rope state according to the contour fitting result, and transmits the judgment result to the controller, and finally the controller controls the alarm brake module according to the judgment result, and the alarm brake module gives an alarm and brakes the elevator.
  • the controller includes: a magnetic field change judgment module and an image information transmission module;
  • the computer includes an image processing module and a traction rope
  • the traction rope image acquisition module collects the traction rope image through a high-speed CCD camera, and then transmits it to the image processing module through the image information transmission module.
  • the weak magnetic induction module includes a sampling circuit and an A/D converter, wherein the sampling circuit collects and amplifies the magnetic field conversion signal of the traction wire rope, and the A/D converter converts the collected analog signal into a digital signal.
  • the weak magnetic induction module includes multiple weak magnetic sensors arranged at different angles.
  • the image information acquisition module converts the analog signal obtained by the CCD camera into a digital signal, and uses a programmable gate array to transmit the digital signal to the CCD sensor.
  • the CCD sensor converts the digital signal into an analog signal.
  • the image information transmission module then converts the analog signal converted by the CCD sensor into a YUV signal and transmits it to the image processing module.
  • each module in the online detection system of the elevator traction rope state is shown in Figure 2.
  • the traction rope weak magnetic induction module and the traction rope high-speed image acquisition module are arranged in sequence.
  • the traction rope weak magnetic induction module and the traction rope high-speed image acquisition module run simultaneously, and weak magnetic induction is performed uninterruptedly during image acquisition to avoid missed detection.
  • the weak magnetic induction module is arranged with three weak magnetic sensors 6 separated by 120° to sense the magnetic field where the traction rope is located and the change of magnetic flux to avoid the influence of the angle.
  • the image acquisition module is arranged with three CCD cameras 3 separated by 120° to collect images to ensure that there are no blind spots, and multiple light sources 2 are arranged at the same time to avoid the influence of light and dark on imaging.
  • the weak magnetic sensor 6 and the CCD camera 3 are both connected to the controller 4, and the controller 4 is wirelessly connected to the computer 5.
  • an online detection method for the elevator traction rope state is used to optimize the traditional weak magnetic field detection method by combining a machine vision method, as shown in FIG3 , and includes the following steps:
  • Step 1 Arrange the weak magnetic induction module and the image acquisition module in sequence along the moving direction of the traction rope;
  • Step 2 The weak magnetic induction module detects the electromagnetic field generated by the traction rope in real time, converts the induced magnetic field and magnetic flux changes into digital quantities through digital-to-analog conversion and transmits them to the magnetic field change judgment module in the controller;
  • Step 3 After the magnetic field change judgment module receives the digital signal transmitted by the weak magnetic induction module, it compares it with the set change threshold to determine whether it is necessary to detect it through the machine vision method; if the digital signal transmitted by the weak magnetic induction module is greater than or equal to the set change threshold, execute step 4 and detect it through the machine vision method; otherwise, re-execute step 2;
  • Step 4 The controller starts the image acquisition module to collect image information and position information of the abnormal magnetic field part of the traction rope, and transmits it to the computer;
  • Step 5 The image processing module in the computer receives the image information collected by the image acquisition module, and processes the collected image information to obtain a fitted contour curve equation;
  • the collected images are subjected to bilateral filtering, gradient calculation and double thresholding to obtain preliminary edge detection results of the traction wire rope.
  • Bilateral filtering Select bilateral filtering for noise reduction to improve the edge detail display of the image.
  • the edge pixels of the collected image are retained, and the domain pixel values are weighted to obtain the output pixel value expression using the bilateral filtering algorithm as follows:
  • f(k,l) and I(i,j) represent the input pixel value of the traction rope image and the output pixel value respectively
  • w(i,j,k,l) represents the weighting coefficient
  • Gradient calculation is achieved by calculating the gradient amplitude and determining the direction.
  • the image after filtering is calculated using the first-order partial derivative difference calculation with a neighborhood of 2x2 to obtain the gradient amplitude expression of the acquired image as follows:
  • the gradient direction expression of the towing rope image is as follows:
  • Qx (i,j) and Qy (i,j) represent the partial derivatives of the pixel point (i,j) of the towing rope image in directions x and y respectively, and four angles of 0, 45°, 90°, and 135° are selected as gradient directions.
  • T1 and T2 represent the thresholds for image segmentation. Points with amplitudes greater than T1 and less than T2 are marked as edge points and non-edge points, respectively. When the amplitude result is between the two, it is necessary to further search in the neighborhood. If there are connected points, they are set as edge points, otherwise they are set as non-edge points. The edge detection result of the traction rope image is obtained through the above process.
  • the edge detection result of the traction wire rope can be preliminarily obtained.
  • the invariant moment matching method is selected, the standard contour shape of the traction rope is set as the matching template, and the connected domain marking method is selected to obtain the entire contour of the image and the matching template and the coordinate values of the boundary points;
  • the least square method is selected to fit the discrete boundary point coordinates of the collected image to obtain the fitted contour curve equation, that is, six curve equations in the same coordinate system are obtained. Two curves in the same image are grouped together, and two X coordinates can be obtained by substituting a Y value;
  • the least squares method is selected to fit the discrete boundary point coordinates of the collected image.
  • the specific process of obtaining the fitted contour curve equation is as follows: Given the boundary coordinate values (x i , y i ), we now need Make
  • a group of Y-axis coordinate values are randomly selected for detection, and the boundary point coordinates of the image to be detected under the same Y-axis coordinate value are compared through the fitted contour curve equation to measure the wear of the traction rope, and then the state of the traction rope is judged according to the wear of the traction rope;
  • a group of Y-axis coordinate values are randomly selected for detection, and the coordinates of the boundary points of the image to be detected under the same group of Y-axis coordinate values are compared to measure the wear of the traction rope;
  • the wear measurement standard A is defined as follows:
  • Step 7 The controller controls the alarm brake module to sound an alarm and perform emergency braking on the elevator according to the judgment result of the traction rope state judgment module.

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  • Automation & Control Theory (AREA)
  • Structural Engineering (AREA)
  • Maintenance And Inspection Apparatuses For Elevators (AREA)

Abstract

本发明提供一种电梯曳引绳状态在线检测系统及检测方法，涉及电梯故障诊断技术领域。该系统包括弱磁感应模块、图像采集模块、控制器、计算机以及报警制动模块；其中，控制器中包括：磁场变化判断模块和图像信息传输模块；计算机中包括图像处理模块和曳引绳状态判断模块；弱磁感应模块和磁场变化判断模块互相通讯，控制器控制图像采集模块采集曳引绳图像，并通过图像信息传输模块将采集的曳引绳图像传输至计算机的图像处理模块进行预处理、曳引绳边缘检测及轮廓拟合；曳引绳状态判断模块根据轮廓拟合结果判断曳引绳状态，并将判断结果传输至控制器，最终控制器根据判断结果控制报警制动模块，报警制动模块进行报警并对电梯进行制动控制。

Description

[根据细则26改正 03.12.0024]一种电梯曳引绳状态在线检测系统及其检测方法 技术领域
本发明涉及电梯故障诊断技术领域，尤其涉及一种电梯曳引绳状态在线检测系统及检测方法。
背景技术
电梯曳引机使用的钢丝绳(即电梯曳引绳)是电梯承重的关键装置，对于轿厢的上升和下降具有驱动作用。在电梯进行作业期间，钢丝绳主要产生的是包括拉、接触、弯曲在内的三种应力。在长期使用不保养的情况下经常会出现断裂、被油污腐蚀等问题，最终导致电梯安全生产事故的发生。在不能够提前检测并发现的情况下，将造成非常严重的安全生产事故，危及人的生命。
在电梯投入运行以后，一般使用人工目测的方法对钢丝绳进行观察，发现有明显的地方直接进行判断，如是细微的瑕疵，则必须要使用检修速度并使用放大镜来进行辅助判定。而人工无法长时间高效率的进行检测，时间一长就会不可避免的产生疲劳，进而影响判断的准确性。另外，受人眼视觉的影响，对一些细小的问题很难检出，这就造成很多电梯钢丝绳直到发生断绳的严重事故都无法及时发现。
现有能够用于检测电梯曳引钢丝绳的技术，通常选择弱磁感应技术，它是将永磁类仪器和漏磁类仪器结合起来构成的一种检测技术。但上述几种方法都受到客观条件的制约，比较容易受到干扰，差错率较高，由于需要人工参与到检测工作中，一方面增加了检测成本，另一方面降低了检测的准确性。
发明内容
本发明要解决的技术问题是针对上述现有技术的不足，提供一种电梯曳引绳状态在线检测系统及检测方法，使用机器视觉方法来优化传统的弱磁感应的检测方法，进而实现电梯曳引绳状态的在线检测。
为解决上述技术问题，本发明所采取的技术方案是：
一方面，本发明提供一种电梯曳引绳状态在线检测系统，包括弱磁感应模块、图像采集模块、控制器、计算机以及报警制动模块；其中，控制器中包括：磁场变化判断模块和图像信息传输模块；计算机中包括图像处理模块和曳引绳状态判断模块；所述弱磁感应模块和磁场变化判断模块互相通讯，控制器控制图像采集模块采集曳引绳图像，并通过图像信息传输模块将采集的曳引绳图像传输至计算机的图像处理模块进行预处理、曳引绳边缘检测及轮廓拟合；曳引绳状态判断模块根据轮廓拟合结果判断曳引绳状态，并将判断结果传输至控制器， 最终控制器根据判断结果控制报警制动模块，报警制动模块进行报警并对电梯进行制动控制。
优选地，所述曳引绳图像采集模块通过高速CCD摄像机采集曳引绳图像，然后经图像信息传输模块传输至图像处理模块。
优选地，所述弱磁感应模块包括采样电路和A/D转换器，其中，采样电路采集曳引钢丝绳的磁场变换信号，并进行放大，A/D转换器将采集的模拟信号转化为数字信号。
优选地，所述弱磁感应模块包括多台不同角度布置的弱磁传感器。
优选地，所述图像信息采集模块将CCD摄像机获取的模拟信号转换为数字信号，利用可编程门阵列，将数字信号传输给CCD传感器，CCD传感器将数字信号转换为模拟信号，图像信息传输模块再将CCD传感器转换的模拟信号转换为YUV信号传输至图像处理模块。
另一方面，本发明还提供一种电梯曳引绳状态在线检测方法，结合机器视觉的方法对传统的弱磁检测方法进行优化，包括以下步骤：
步骤1：沿曳引绳运动方向，依次布置弱磁感应模块和图像采集模块；
步骤2：弱磁感应模块实时检测曳引绳运行中产生的电磁场，将感应到的磁场以及磁通量变化经过数模转换，转换为数字量传输至控制器中的磁场变化判断模块；
步骤3：磁场变化判断模块接收到弱磁感应模块传输的数字信号后，与设定的变化阈值进行对比，判断是否需要通过机器视觉方法进行检测；如果弱磁感应模块传输的数字信号大于等于设定的变化阈值，则执行步骤4，通过机器视觉方法进行检测；否则重新执行步骤2；
步骤4：控制器启动图像采集模块，采集曳引绳磁场异常部分的图像信息以及位置信息，并传输至计算机；
步骤5：计算机中的图像处理模块接收图像采集模块采集的图像信息，并对采集的图像信息进行处理后得到拟合的轮廓曲线方程；
对采集的图像进行双边滤波，梯度计算以及双阈值化处理初步得到曳引钢丝绳的边缘检测结果；
进行上述图像预处理后，选取不变矩匹配方法，设置曳引绳标准轮廓形状为匹配模板，选取连通域标记方法获取图像以及匹配模板的全部轮廓以及边界点坐标值；
选择最小二乘法对采集的图像的离散的边界点坐标进行拟合，得到拟合的轮廓曲线方程，即得到同一坐标系下六条曲线方程，同一图像中两条曲线为一组，带入一个Y值能够得到两个X坐标，三张图象即得到三组，共6个X坐标；
步骤6：曳引绳状态判断模块根据步骤5拟合的轮廓曲线方程对曳引绳的状态进行判断，并将判断结果传输至控制器；
在曳引绳状态判断模块中，随机选取一组Y轴坐标值进行检测，通过拟合的轮廓曲线方程对比同一Y轴坐标值下待检测图象的边界点坐标，以衡量曳引绳的磨损量，进而根据曳引绳的磨损量判断曳引绳的状态；
随机选取一组Y轴坐标值进行检测，对比同一组Y轴坐标值下待检测图象的边界点坐标，以衡量曳引绳的磨损量；设所选择的一组Y轴坐标为y i,(i＝1,2,…,n)，n为Y轴坐标个数，将上述Y轴坐标带入拟合的曲线方程，得到待检测图像一的X坐标为x i1，x i2，图像二X坐标为x i3，x i4，图像三X坐标为x i5，x i6；
定义磨损量的衡量标准A，如下公式所示：
Figure PCTCN2022136818-appb-000001
A值越小则曳引绳的磨损越严重，当A小于80％时，曳引绳处于严重磨损状态，并需要进行报警以及制动维修，否则曳引绳为正常工作状态；
步骤7：控制器根据曳引绳状态判断模块的判断结果控制报警制动模块进行报警并对电梯进行紧急制动。
采用上述技术方案所产生的有益效果在于：本发明提供的一种电梯曳引绳状态在线检测系统及方法，(1)使用机器视觉对弱磁检测方法进行优化，有效解决了传统检测技术存在的容易受到干扰，差错率较高，可靠性较低等问题。(2)使用控制器控制曳引绳弱磁感应模块与曳引绳图像采集模块，不间断对曳引绳进行弱磁检测，避免连续磨损处的缺检漏检。(3)本发明的电梯曳引绳检测系统，安装在电梯井道内对钢丝绳进行检测，因为井道内曳引绳是受力绷紧的，运行过程中摆动幅度小，除非出现钢丝绳扭曲的现象。(4)通过摄像机对于曳引绳进行实时观察，基于图像处理技术，可以大大提高检测结果的可靠性，但该方法数据处理过程复杂。该检测系统使用机器视觉的方法优化传统弱磁感应方法，可以分析曳引绳的缺陷，缺陷部位，特定的图像显示，通过非接触式、实时检测的方式，从而自动获取曳引绳的图像数据。
附图说明
图1为本发明实施例提供的一种电梯曳引绳状态在线检测系统的结构框图；
图2为本发明实施例提供的一种电梯曳引绳状态在线检测系统的示意图；
图3为本发明实施例提供的一种电梯曳引绳状态在线检测的流程图。
图中：1、曳引绳；2、光源；3、摄像机；4、控制器；5、计算机；6、弱磁传感器。
具体实施方式
下面结合附图和实施例，对本发明的具体实施方式作进一步详细描述。以下实施例用于说明本发明，但不用来限制本发明的范围。
本实施例中，一种电梯曳引绳状态在线检测系统，安装在电梯井道内对钢丝绳进行检测，如图1所示，包括弱磁感应模块、图像采集模块、控制器、计算机以及报警制动模块；其中，控制器中包括：磁场变化判断模块和图像信息传输模块；计算机中包括图像处理模块和曳引绳状态判断模块；所述弱磁感应模块和磁场变化判断模块互相通讯，控制器控制图像采集模块采集曳引绳图像，并通过图像信息传输模块将采集的曳引绳图像传输至计算机的图像处理模块进行预处理、曳引绳边缘检测及轮廓拟合；曳引绳状态判断模块根据轮廓拟合结果判断曳引绳状态，并将判断结果传输至控制器，最终控制器根据判断结果控制报警制动模块，报警制动模块进行报警并对电梯进行制动控制。曳引绳图像采集模块通过高速CCD摄像机采集曳引绳图像，然后经图像信息传输模块传输至图像处理模块。弱磁感应模块包括采样电路和A/D转换器，其中，采样电路采集曳引钢丝绳的磁场变换信号，并进行放大，A/D转换器将采集的模拟信号转化为数字信号。弱磁感应模块包括多台不同角度布置的弱磁传感器。图像信息采集模块将CCD摄像机获取的模拟信号转换为数字信号，利用可编程门阵列，将数字信号传输给CCD传感器，CCD传感器将数字信号转换为模拟信号，图像信息传输模块再将CCD传感器转换的模拟信号转换为YUV信号传输至图像处理模块。
本实施例中，电梯曳引绳状态在线检测系统中各模块的布置如图2所示，沿曳引绳1运动方向，依次布置曳引绳弱磁感应模块与曳引绳高速图像采集模块。曳引绳弱磁感应模块与曳引绳高速图像采集模块同时运行，在进行图像采集时不间断进行弱磁感应，避免漏检。弱磁感应模块布置3台相隔120°的弱磁传感器6感应曳引绳所在磁场以及磁通量变化，避免角度的影响。图像采集模块布置3台相隔120°的CCD摄像机3采集图像，保证没有盲区，同时布置多个光源2，避免光照明暗对成像的影响。弱磁传感器6和CCD摄像机3均与控制器4连接，控制器4与计算机5无线连接。
本实施例中，一种电梯曳引绳状态在线检测方法，结合机器视觉的方法对传统的弱磁检测方法进行优化，如图3所示，包括以下步骤：
步骤1：沿曳引绳运动方向，依次布置弱磁感应模块和图像采集模块；
步骤2：弱磁感应模块实时检测曳引绳运行中产生的电磁场，将感应到的磁场以及磁通量变化经过数模转换，转换为数字量传输至控制器中的磁场变化判断模块；
步骤3：磁场变化判断模块接收到弱磁感应模块传输的数字信号后，与设定的变化阈值进行对比，判断是否需要通过机器视觉方法进行检测；如果弱磁感应模块传输的数字信号大于等于设定的变化阈值，则执行步骤4，通过机器视觉方法进行检测；否则重新执行步骤2；
步骤4：控制器启动图像采集模块，采集曳引绳磁场异常部分的图像信息以及位置信息，并传输至计算机；
步骤5：计算机中的图像处理模块接收图像采集模块采集的图像信息，并对采集的图像信息进行处理后得到拟合的轮廓曲线方程；
对采集的图像进行双边滤波，梯度计算以及双阈值化处理初步得到曳引钢丝绳的边缘检测结果；
具体为：(1)双边滤波：选取双边滤波进行降噪处理，提升图象的边缘细节显示程度。保留所采集图象的边缘象素，对领域像素值进行加权处理，获取采用双边滤波算法输出象素值表达式如下:
Figure PCTCN2022136818-appb-000002
式中：f(k,l)与I(i,j)分别表示输人的曳引绳图象象素值以及输出象素值，w(i,j,k,l)表示加权系数。
(2)梯度计算：通过计算梯度幅值以及方向确定两部分实现梯度计算。将完成滤波处理后图象利用邻域为2x2的一阶偏导差分计算，获取采集图象的梯度幅值表达式如下:
Figure PCTCN2022136818-appb-000003
曳引绳图象的梯度方向表达式如下:
Figure PCTCN2022136818-appb-000004
其中:Q x(i,j)与Q y(i,j)分别表示方向为x与y的曳引绳图象象素点(i,j)的偏导，选取0、45°、90°、135°4个角度作为梯度方向。
(3)双阈值化处理：T 1与T 2表示实现图像分割的阈值，将幅值大于T 1以及小于T 2的点分别标记为边缘点以及非边缘点，幅值结果处于二者之间时，需进一步在邻域中搜寻，存在可连通的点设置为边缘点，否则设置为非边缘点，通过以上过程获取曳引绳图象边缘检测结果。
进行上述图像预处理后，即可初步得到曳引钢丝绳的边缘检测结果，为获取不同图像中曳引钢丝绳轮廓及其坐标值，方便进一步处理，选取不变矩匹配方法，设置曳引绳标准轮廓形状为匹配模板，选取连通域标记方法获取图像以及匹配模板的全部轮廓以及边界点坐标值；
选择最小二乘法对采集的图像的离散的边界点坐标进行拟合，得到拟合的轮廓曲线方程， 即得到同一坐标系下六条曲线方程，同一图像中两条曲线为一组，带入一个Y值能够得到两个X坐标；
选择最小二乘法对采集的图像的离散的边界点坐标进行拟合，得到拟合的轮廓曲线方程的具体过程为：已知边界坐标值为(x i,y i)，现求
Figure PCTCN2022136818-appb-000005
使得
Figure PCTCN2022136818-appb-000006
而上式为多元函数，其最小值存在的必要条件，是其对应偏导等于零，由此可得：
Figure PCTCN2022136818-appb-000007
即：
Figure PCTCN2022136818-appb-000008
上式即为法方程，通过该方程可求出唯一解a k，k＝0,1,…,n，从而确定拟合函数。本实施例设定拟合后的曲线方程为y t＝f t(x),(t＝1,2,3,4,5,6)。
步骤6：曳引绳状态判断模块根据步骤5拟合的轮廓曲线方程对曳引绳的状态进行判断，并将判断结果传输至控制器；
在曳引绳状态判断模块中，随机选取一组Y轴坐标值进行检测，通过拟合的轮廓曲线方程对比同一Y轴坐标值下待检测图象的边界点坐标，以衡量曳引绳的磨损量，进而根据曳引绳的磨损量判断曳引绳的状态；
本实施例中，随机选取一组Y轴坐标值进行检测，对比同一组Y轴坐标值下待检测图象的边界点坐标，以衡量曳引绳的磨损量；设所选择的一组Y轴坐标为y i,i＝1,2,…,n，n为Y轴坐标个数，将上述Y轴坐标带入拟合的曲线方程，得到待检测图像一的X坐标为x i1，x i2，图像二X坐标为x i3，x i4，图像三X坐标为x i5，x i6；
定义磨损量的衡量标准A，如下公式所示：
Figure PCTCN2022136818-appb-000009
A值越小则曳引绳的磨损越严重，当A小于80％时，曳引绳处于严重磨损状态，否则曳引绳为正常工作状态。该衡量标准A以曳引绳边界宽度变化衡量磨损程度，减小数据量，提高处理速度。
步骤7：控制器根据曳引绳状态判断模块的判断结果控制报警制动模块进行报警并对电 梯进行紧急制动。
最后应说明的是：以上实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述实施例所记载的技术方案进行修改，或者对其中部分或者全部技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明权利要求所限定的范围。

Claims (8)

  1. 一种电梯曳引绳状态在线检测系统，其特征在于：包括弱磁感应模块、图像采集模块、控制器、计算机以及报警制动模块；其中，控制器中包括：磁场变化判断模块和图像信息传输模块；计算机中包括图像处理模块和曳引绳状态判断模块；所述弱磁感应模块和磁场变化判断模块互相通讯，控制器控制图像采集模块采集曳引绳图像，并通过图像信息传输模块将采集的曳引绳图像传输至计算机的图像处理模块进行预处理、曳引绳边缘检测及轮廓拟合；曳引绳状态判断模块根据轮廓拟合结果判断曳引绳状态，并将判断结果传输至控制器，最终控制器根据判断结果控制报警制动模块，报警制动模块进行报警并对电梯进行制动控制。
  2. 根据权利要求1所述的一种电梯曳引绳状态在线检测系统，其特征在于：所述曳引绳图像采集模块通过高速CCD摄像机采集曳引绳图像，然后经图像信息传输模块传输至图像处理模块。
  3. 根据权利要求1所述的一种电梯曳引绳状态在线检测系统，其特征在于：所述弱磁感应模块包括采样电路和A/D转换器，其中，采样电路采集曳引钢丝绳的磁场变换信号，并进行放大，A/D转换器将采集的模拟信号转化为数字信号。
  4. 根据权利要求1所述的一种电梯曳引绳状态在线检测系统，其特征在于：所述弱磁感应模块包括多台不同角度布置的弱磁传感器。
  5. 根据权利要求2所述的一种电梯曳引绳状态在线检测系统，其特征在于：所述图像信息采集模块将CCD摄像机获取的模拟信号转换为数字信号，利用可编程门阵列，将数字信号传输给CCD传感器，CCD传感器将数字信号转换为模拟信号，图像信息传输模块再将CCD传感器转换的模拟信号转换为YUV信号传输至图像处理模块。
  6. 一种电梯曳引绳状态在线检测方法，基于权利要求1所述电梯曳引绳状态在线检测系统实现，其特征在于：包括以下步骤：
    步骤1：沿曳引绳运动方向，依次布置弱磁感应模块和图像采集模块；
    步骤2：弱磁感应模块实时检测曳引绳运行中产生的电磁场，将感应到的磁场以及磁通量变化经过数模转换，转换为数字量传输至控制器中的磁场变化判断模块；
    步骤3：磁场变化判断模块接收到弱磁感应模块传输的数字信号后，与设定的变化阈值进行对比，判断是否需要通过机器视觉方法进行检测；如果弱磁感应模块传输的数字信号大于等于设定的变化阈值，则执行步骤4，通过机器视觉方法进行检测；否则重新执行步骤2；
    步骤4：控制器启动图像采集模块，采集曳引绳磁场异常部分的图像信息以及位置信息，并传输至计算机；
    步骤5：计算机中的图像处理模块接收图像采集模块采集的图像信息，并对采集的图像信息进行处理后得到拟合的轮廓曲线方程；
    步骤6：曳引绳状态判断模块根据步骤5拟合的轮廓曲线方程对曳引绳的状态进行判断，并将判断结果传输至控制器；
    步骤7：控制器根据曳引绳状态判断模块的判断结果控制报警制动模块进行报警并对电梯进行紧急制动。
  7. 根据权利要求6所述的一种电梯曳引绳状态在线检测方法，其特征在于：所述步骤5的具体方法为：
    对采集的图像进行双边滤波，梯度计算以及双阈值化处理初步得到曳引钢丝绳的边缘检测结果；
    进行上述图像预处理后，选取不变矩匹配方法，设置曳引绳标准轮廓形状为匹配模板，选取连通域标记方法获取图像以及匹配模板的全部轮廓以及边界点坐标值；
    选择最小二乘法对采集的图像的离散的边界点坐标进行拟合，得到拟合的轮廓曲线方程，即得到同一坐标系下六条曲线方程，同一图像中两条曲线为一组，带入一个Y值能够得到两个X坐标，三张图象即得到三组，共6个X坐标。
  8. 根据权利要求7所述的一种电梯曳引绳状态在线检测方法，其特征在于：所述步骤6的具体方法为：
    在曳引绳状态判断模块中，随机选取一组Y轴坐标值进行检测，通过拟合的轮廓曲线方程对比同一Y轴坐标值下待检测图象的边界点坐标，以衡量曳引绳的磨损量，进而根据曳引绳的磨损量判断曳引绳的状态；
    随机选取一组Y轴坐标值进行检测，对比同一组Y轴坐标值下待检测图象的边界点坐标，以衡量曳引绳的磨损量；设所选择的一组Y轴坐标为y i,i＝1,2,…,n，n为Y轴坐标个数，将上述Y轴坐标带入拟合的曲线方程，得到待检测图像一的X坐标为x i1，x i2，图像二X坐标为x i3，x i4，图像三X坐标为x i5，x i6；
    定义磨损量的衡量标准A，如下公式所示：
    Figure PCTCN2022136818-appb-100001
    A值越小则曳引绳的磨损越严重，当A小于80％时，曳引绳处于严重磨损状态，并需要进行报警以及制动维修，否则曳引绳为正常工作状态。
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