WO2021185010A1 - 一种辊压机辊面缺陷识别方法和装置 - Google Patents

一种辊压机辊面缺陷识别方法和装置 Download PDF

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WO2021185010A1
WO2021185010A1 PCT/CN2021/076093 CN2021076093W WO2021185010A1 WO 2021185010 A1 WO2021185010 A1 WO 2021185010A1 CN 2021076093 W CN2021076093 W CN 2021076093W WO 2021185010 A1 WO2021185010 A1 WO 2021185010A1
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roller surface
roller
area
cloud data
point cloud
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French (fr)
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王志凌
高霖
张文进
黄贺
许瑞康
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中建材（合肥）粉体科技装备有限公司
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Publication of WO2021185010A1 publication Critical patent/WO2021185010A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G06T7/001—Industrial image inspection using an image reference approach
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/28—Measuring arrangements characterised by the use of optical techniques for measuring areas
    • G01B11/285—Measuring arrangements characterised by the use of optical techniques for measuring areas using photoelectric detection means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/64—Analysis of geometric attributes of convexity or concavity
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/66—Analysis of geometric attributes of image moments or centre of gravity
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/24—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G01N2021/8854—Grading and classifying of flaws
    • G01N2021/8861—Determining coordinates of flaws
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G01N2021/8854—Grading and classifying of flaws
    • G01N2021/8874—Taking dimensions of defect into account
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
    • G01N21/952—Inspecting the exterior surface of cylindrical bodies or wires
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10028—Range image; Depth image; 3D point clouds
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection

Definitions

  • the invention relates to the technical field of roller surface defect detection, in particular to a method and device for identifying roller surface defects of a roller press.
  • Roller press is widely used in building materials, cement, metallurgy, mining, chemical industry and other fields. It has the advantages of high efficiency, energy saving and environmental protection. Its working principle is shown in Figure 1 below. It mainly relies on two horizontally installed and synchronously rotating squeeze rollers for high pressure. The material layer is crushed. The squeezing force of the enclosed material layer in the process of being forced to move downward gradually increases to a large enough level, until it is crushed and squeezed into a dense cake to be discharged from the machine.
  • Figure 2 shows the common defects of the roller surface of various roller presses: a) The roller surface is not uniformly worn due to the edges of the roller press. The effect is that the middle of the roll surface wears quickly, and the wear on both sides is slow. After long-term operation, the phenomenon of depression in the middle of the roll surface appears, making it impossible to extend the life of the roll surface by adjusting the roll gap. b) Roll surface pits. When metal foreign objects such as steel ball hammers enter the roller press, it is easy to cause partial damage to the roller surface. For example, pits appear on the roller surface. The roll surface is overhauled or scrapped.
  • roller surface defects of roller presses there is no relevant technology, equipment and corresponding research on the identification of roller surface defects of roller presses.
  • the roller surface is inspected manually. If defects are found, surfacing repair is performed. The inspection needs to remove the cover, which increases the labor intensity of the workers. It is often necessary to check and compare at multiple time points to determine whether the roller surface is needed. Repair, labor costs and time costs are high, and the accuracy and timeliness of manual observation is also poor.
  • the invention provides a method and device for identifying defects on the roller surface of a roller press, which can solve the technical problems of low efficiency and large errors in manual inspection of the roller surface.
  • a method for identifying defects on the roller surface of a roller press Based on the roller press, a three-dimensional coordinate system is established on the end surface of the roller press;
  • the present invention also discloses a roller surface defect recognition device of a roller press, which includes the following modules:
  • Three-dimensional scanner used to obtain the three-dimensional point cloud data of the roller surface of the roller press
  • the calculation and judgment unit is used to perform the following steps:
  • calculation and judgment unit is also used to perform the following steps:
  • the roller surface defect recognition method of the present invention automatically calculates, recognizes and judges whether there are defects on the roller surface and the type of the defect based on the acquired three-dimensional point cloud data of the roller surface and comparison with the reference data. , Area, volume, location and other information, and then automatically judge the wear of the roller surface based on the defect identification information.
  • the present invention is more intelligent, more accurate and efficient.
  • Figure 1 Schematic diagram of squeezing and crushing by roller press
  • Figure 2a shows the uneven wear of the roller surface
  • Figure 2b shows the defect of the pits on the roller surface
  • Figure 3 is a schematic diagram of the method of the present invention.
  • Figure 4 is a schematic diagram of the coordinates of the roller surface of the present invention.
  • the method for identifying defects on the roller surface of a roller press in this embodiment is based on the roller press and establishes a three-dimensional coordinate system on the end surface of the roller press;
  • roller surface When ⁇ H is greater than a certain value, the roller surface is set to be unevenly worn.
  • X represents the coordinate value of the point along the width of the roller surface
  • Y represents the coordinate value of the point along the circumferential direction
  • the method for identifying defects on the roller surface of the roller press automatically calculates, recognizes and judges whether there are defects on the roller surface and the type of the defect based on the acquired three-dimensional point cloud data of the roller surface and through comparison with the reference data. , Area, volume, location and other information, and then automatically judge the wear of the roller surface based on the defect identification information. Compared with manual detection, it is smarter, more accurate and more efficient.
  • the present invention also discloses a roller surface defect recognition device of a roller press, which includes the following modules:
  • Three-dimensional scanner used to obtain the three-dimensional point cloud data of the roller surface of the roller press
  • the calculation and judgment unit is used to perform the following steps:
  • calculation and judgment unit is also used to perform the following steps:
  • the device provided in the embodiment of the present invention corresponds to the method provided in the embodiment of the present invention, and the explanations, examples, and beneficial effects of related content can refer to the corresponding parts in the foregoing method.

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  • Geometry (AREA)
  • Quality & Reliability (AREA)
  • Chemical & Material Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
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  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Investigating Strength Of Materials By Application Of Mechanical Stress (AREA)
  • Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
  • Length Measuring Devices By Optical Means (AREA)
  • Length Measuring Devices With Unspecified Measuring Means (AREA)

Abstract

本发明的一种辊压机辊面缺陷识别方法和装置，可解决人工检查辊面情况，效率低，误差大的技术问题。包括以下步骤：将首次获取的辊面三维点云数据A0设为基准数据；获取辊面磨损后的三维点云数据A1，对于同一X、Y点，其高度值Z的变化设为ΔZ；当ΔZ大于设定值时，设为异常点；连接相邻异常点，区域连接面积大于设定值时，将该区域设为凹坑缺陷；自动计算凹坑缺陷的面积S、平均高度差ΔZ_A、体积V、中心位置。本发明根据获取的辊面三维点云数据，通过和基准数据的对比，自动计算、识别和判断辊面缺陷的类型、面积、体积及位置等信息，然后根据缺陷识别信息对辊面的磨损情况进行判断。

Description

一种辊压机辊面缺陷识别方法和装置 技术领域
本发明涉及辊面缺陷检测技术领域，具体涉及一种辊压机辊面缺陷识别方法和装置。
背景技术
辊压机广泛运用于建材水泥、冶金矿山、化工等领域，具备高效、节能、环保等优点，其工作原理如下图1所示，主要依靠两个水平安装且同步相向旋转的挤压辊进行高压料层粉碎。被封闭的物料层在被迫向下移动的过程中所受挤压力逐渐增至足够大，直至被粉碎且被挤压成密实料饼从机下排出。
由于辊压机在高压力下运行，在高效粉碎的同时，设备也会同步受到磨损，图2展示了种辊压机辊面常见缺陷：a)辊面不均匀磨损，由于辊压机存在边缘效应，即辊面中间磨损快，两边磨损慢，长期运行后，出现辊面中间凹陷的现象，使得无法通过调节辊缝延长辊面使用寿命的目的。b)辊面凹坑，当有钢球锤头等金属异物进入辊压机时，容易造成辊面局部破坏，例如辊面出现凹坑，如不及时发现处理，则会伤及整个辊面，导致辊面大修或报废。
目前，尚没有针对辊压机辊面缺陷识别的相关技术、装备及相应研究。在水泥工厂都是采用人工方式检查辊面情况，发现有缺陷就进行堆焊修复，而检查需要拆盖，增加了工人劳动强度，常常需要经过多个时间点的检查比较才能确定是否需要辊面修复，工人劳动成本和时间成本较高,人工观察的准确率和及时性也很差。
发明内容
本发明提出的一种辊压机辊面缺陷识别方法和装置，可解决采用人工方式检查辊面情况，效率低，误差大的技术问题。
为实现上述目的，本发明采用了以下技术方案：
一种辊压机辊面缺陷识别方法，基于辊压机，在辊压机的端面建立三维坐标系；
包括以下步骤：
将首次获取的辊面三维点云数据A0设为基准数据；
获取辊面磨损后的三维点云数据A1，对于同一X、Y点，其高度值Z的变化设为ΔZ；
当ΔZ大于设定值时，设为异常点；
连接相邻异常点，区域连接面积大于设定值时，将该区域设为凹坑缺陷；
自动计算凹坑缺陷的面积S、平均高度差ΔZ_A、体积V、中心位置。
进一步，还包括对于三维点云数据A 1，当X＝xm不变，Y从y1至yn变化，计算x m圆周的平均高度H m；
计算最大平均高度H max与最小平均高度H min的差值ΔH；
当ΔH大于设定值时，辊面设为不均匀磨损。
另一方面本发明还公开一种辊压机辊面缺陷识别装置，包括以下模块：
三维扫描仪，用于获取辊压机的辊面三维点云数据；
计算判断单元，用于执行以下步骤：
将三维扫描仪首次获取的辊面三维点云数据A0设为基准数据；
使用三维扫描仪获取辊面磨损后的三维点云数据A1，对于同一X、Y点，其高度值Z的变化设为ΔZ；
当ΔZ大于设定值时，设为异常点；
连接相邻异常点，区域连接面积大于设定值时，将该区域设为凹坑缺陷；
自动计算凹坑缺陷的面积S、平均高度差ΔZ_A、体积V、中心位置。
进一步的，计算判断单元，还用于执行以下步骤：
对于三维点云数据A 1，当X＝xm不变，Y从y1至yn变化，计算x m圆周的平均高度H m；
计算最大平均高度H max与最小平均高度H min的差值ΔH；
当ΔH大于设定值时，辊面设为不均匀磨损。
由上述技术方案可知，本发明的辊压机辊面缺陷识别方法根据获取的辊面三维点云数据，通过和基准数据的对比，自动计算、识别和判断辊面是否存在缺陷，以及缺陷的类型、面积、体积及位置等信息，然后根据缺陷识别信息对辊面的磨损情况进行自动判断。相比人工检测，本发明更智能，更精准且效率高。
附图说明
图1辊压机挤压粉碎示意图；
图2a为辊面的不均匀磨损；
图2b为辊面的凹坑的缺陷；
图3是本发明的方法原理图；
图4是本发明辊面的坐标示意图；
具体实施方式
为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。
如图3和图4所示，本实施例所述的辊压机辊面缺陷识别方法，基于辊压机，在辊压机的端面建立三维坐标系；
包括以下步骤：
将首次获取的辊面三维点云数据A0设为基准数据；
获取辊面磨损后的三维点云数据A1，对于同一X、Y点，其高度值Z的变化设为ΔZ；
当ΔZ大于设定值时，设为异常点；
连接相邻异常点，区域连接面积大于设定值时，将该区域设为凹坑缺陷；
自动计算凹坑缺陷的面积S、平均高度差ΔZ_A、体积V、中心位置。
其次，
对于三维点云数据A 1，当X＝xm不变，Y从y1至yn变化，计算x m圆周的平均高度H m；
计算最大平均高度H max与最小平均高度H min的差值ΔH；
当ΔH大于设定值时，辊面设为不均匀磨损。
以下对于上述步骤具体说明：
1、将首次获取的辊面三维点云数据A 0设为基准数据；
获取辊面磨损后的三维点云数据A 1，对于同一X、Y点，其高度值Z的变化设为ΔZ；
当ΔZ大于某值时，设为异常点；
连接相邻异常点，区域连接面积大于某值时，将该区域设为凹坑缺陷；
自动计算凹坑缺陷的面积S、平均高度差ΔZ A、体积V、中心位置。
2、对于三维点云数据A 1，当X＝xm不变，Y从y1至yn变化，计算x m圆周的平均高度H m；
计算最大平均高度H max与最小平均高度H min的差值ΔH；
当ΔH大于某值时，辊面设为不均匀磨损。
以下具体说明：
首次检测时，通过扫描仪获取三维点云数据
Figure PCTCN2021076093-appb-000001
X代表点所处的沿辊面宽度的坐标值，Y代表点所处沿圆周方向的坐标值，Z代表该点距离圆柱轴线的高度值，如X＝x 1，Y＝y 1，则
Figure PCTCN2021076093-appb-000002
X＝x 1，Y＝y 2，则
Figure PCTCN2021076093-appb-000003
将首次扫描结果设为该辊面的基准数据。
当辊面出现缺陷后，获取三维点云数据
Figure PCTCN2021076093-appb-000004
对于同一个点，对比磨损前后的三维数据，其二维平面坐标X、Y值不会改变，改变的仅仅是Z值，此时将此次扫描结果与基准数据相减，可得到该点的高度差ΔZ＝Z 0-Z 1，当高度差ΔZ≥5mm时，将该点设置为异常点。
若部分异常点彼此相连，当相连的异常点的汇总面积S≥100mm 2，将该面积内的所有点设置为凹坑缺陷，计算凹坑的面积S、平均高度差
Figure PCTCN2021076093-appb-000005
体积V＝S×ΔZ A及中心位置。
对于扫描数据
Figure PCTCN2021076093-appb-000006
当X＝x m不变，Y从y 1至y n变化，代表着x m所处辊面宽度坐标位置一周的点，将这些点的高度值算术平均即得到x m圆周的平均高度
Figure PCTCN2021076093-appb-000007
算出最大平均高度H max和最小平均高度H min，得到差值ΔH＝H max-H min，当ΔH≥10mm，将该辊面设定为不均匀磨损。
由上可知，本发明实施例的辊压机辊面缺陷识别方法根据获取的辊面三维点云数据，通过和基准数据的对比，自动计算、识别和判断辊面是否存在缺陷，以及缺陷的类型、面积、体积及位置等信息，然后根据缺陷识别信息对辊面的磨损情况进行自动判断。相比人工检测，更智能，更精准且效率高。
另一方面本发明还公开一种辊压机辊面缺陷识别装置，包括以下模块：
三维扫描仪，用于获取辊压机的辊面三维点云数据；
计算判断单元，用于执行以下步骤：
将三维扫描仪首次获取的辊面三维点云数据A0设为基准数据；
使用三维扫描仪获取辊面磨损后的三维点云数据A1，对于同一X、Y点，其高度值Z的变化设为ΔZ；
当ΔZ大于设定值时，设为异常点；
连接相邻异常点，区域连接面积大于设定值时，将该区域设为凹坑缺陷；
自动计算凹坑缺陷的面积S、平均高度差ΔZ_A、体积V、中心位置。
进一步的，计算判断单元，还用于执行以下步骤：
对于三维点云数据A 1，当X＝xm不变，Y从y1至yn变化，计算x m圆周的平均高度H m；
计算最大平均高度H max与最小平均高度H min的差值ΔH；
当ΔH大于设定值时，辊面设为不均匀磨损。
可理解的是，本发明实施例提供的装置与本发明实施例提供的方法相对应，相关内容的解释、举例和有益效果可以参考上述方法中的相应部分。
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：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分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各实施例技术方案的精神和范围。

Claims (6)

  1. 一种辊压机辊面缺陷识别方法，基于辊压机，其特征在于：在辊压机的端面建立三维坐标系；
    包括以下步骤：
    将首次获取的辊面三维点云数据A0设为基准数据；
    获取辊面磨损后的三维点云数据A1，对于同一X、Y点，其高度值Z的变化设为ΔZ；
    当ΔZ大于设定值时，设为异常点；
    连接相邻异常点，区域连接面积大于设定值时，将该区域设为凹坑缺陷；
    自动计算凹坑缺陷的面积S、平均高度差ΔZ_A、体积V、中心位置。
  2. 根据权利要求1所述的辊压机辊面缺陷识别方法，其特征在于：还包括：
    对于三维点云数据A 1，当X＝xm不变，Y从y1至yn变化，计算x m圆周的平均高度H m；
    计算最大平均高度H max与最小平均高度H min的差值ΔH；
    当ΔH大于设定值时，辊面设为不均匀磨损。
  3. 根据权利要求2所述的辊压机辊面缺陷识别方法，其特征在于：
    设获取三维点云数据
    Figure PCTCN2021076093-appb-100001
    X代表点所处的沿辊面宽度的坐标值，Y代表点所处沿圆周方向的坐标值，Z代表该点距离圆柱轴线的高度值；
    将首次三维点云数据设为该辊面的基准数据；
    当辊面出现缺陷后对辊面进行扫描，获取三维点云数据
    Figure PCTCN2021076093-appb-100002
    对于同一个点，对比磨损前后的三维数据，其二维平面坐标X、Y值不会改变，改变的仅仅是Z值，此时将此次扫描结果与基准数据相减，可得到该点的高度差ΔZ＝Z 0-Z 1，当高度差ΔZ≥5mm时，将该点设置为异常点；
    若部分异常点彼此相连，当相连的异常点的汇总面积S≥100mm 2，将该面积内的所有点设置为凹坑缺陷，计算凹坑的面积S、平均高度差
    Figure PCTCN2021076093-appb-100003
    体积V＝S×ΔZ A及中心位置。
  4. 根据权利要求3所述的辊压机辊面缺陷识别方法，其特征在于：
    对于扫描数据
    Figure PCTCN2021076093-appb-100004
    当X＝x m不变，Y从y 1至y n变化，代表着x m所处辊面宽度坐标位置一周的点，将这些点的高度值算术平均即得到x m圆周的平均高度
    Figure PCTCN2021076093-appb-100005
    算出最大平均高度H max和最小平均高度H min，得到差值ΔH＝H max-H min，当ΔH≥10mm，将该辊面设定为不均匀磨损。
  5. 一种辊压机辊面缺陷识别装置，其特征在于：
    包括以下模块：
    三维扫描仪，用于获取辊压机的辊面三维点云数据；
    计算判断单元，用于执行以下步骤：
    将三维扫描仪首次获取的辊面三维点云数据A0设为基准数据；
    使用三维扫描仪获取辊面磨损后的三维点云数据A1，对于同一X、Y点，其高度值Z的变化设为ΔZ；
    当ΔZ大于设定值时，设为异常点；
    连接相邻异常点，区域连接面积大于设定值时，将该区域设为凹坑缺陷；
    自动计算凹坑缺陷的面积S、平均高度差ΔZ_A、体积V、中心位置。
  6. 根据权利要求5所述的一种辊压机辊面缺陷识别装置，其特征在于：
    计算判断单元，还用于执行以下步骤：
    对于三维点云数据A 1，当X＝xm不变，Y从y1至yn变化，计算x m圆周的平均高度H m；
    计算最大平均高度H max与最小平均高度H min的差值ΔH；
    当ΔH大于设定值时，辊面设为不均匀磨损。
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