基于树莓派和视觉图像的钻井振动筛倾角调节系统

doi:10.3785/j.issn.1006-754X.2024.03.147

工程设计学报

2024, Vol. 31

Issue (2): 254-262 DOI: 10.3785/j.issn.1006-754X.2024.03.147

整机和系统设计

基于树莓派和视觉图像的钻井振动筛倾角调节系统

侯勇俊^1,²(

),贾文俊¹,刘博文³,吴先进⁴

^1.西南石油大学机电工程学院，四川成都 610500
^2.石油天然气装备技术四川省科技资源共享服务平台，四川成都 610500
^3.中国石油天然气股份有限公司西南油气田分公司成都天然气化工总厂，四川成都 610213
^4.宝石机械成都装备制造分公司，四川成都 610052

Inclination angle adjustment system for shale shaker based on Raspberry Pi and visual image

Yongjun HOU^1,²(

),Wenjun JIA¹,Bowen LIU³,Xianjin WU⁴

^1.School of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, China
^2.Oil and Gas Equipment Technology Sharing and Service Platform of Sichuan Province, Chengdu 610500, China
^3.Chengdu Natural Gas Chemical Plant, Southwest Oil & Gasfeild Company of PetroChina Company Limited, Chengdu 610213, China
^4.BOMCO Chengdu Equipment Manufacturing Company, Chengdu 610052, China

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摘要：

目前，我国的钻井振动筛均采用人工监测筛面固液分离状态和手动调节筛面倾角的方式进行操作，无法实现自适应工作，常常出现钻井液“跑浆”现象。针对这一问题，提出了一种基于树莓派和视觉图像的钻井振动筛倾角调节系统。该系统以树莓派及专用摄像头、电机驱动板和2台步进电机为硬件平台，搭载基于OpenCV和改进AlexNet模型开发的图像识别软件，用于实现钻井振动筛的筛面固液分离状态视觉检测与筛面倾角自动调节。首先，根据钻井振动筛固液分离过程中液相终止线的位置特征，将采集的筛面图像分为正常、少浆和跑浆状态三种类别，并构建筛面图像数据集。然后，使用TensorFlow平台搭建基于迁移学习的AlexNet模型，以实现钻井振动筛的筛面固液分离状态的自动识别。最后，基于识别结果，由树莓派的GPIO（general purpose input/output，通用型输入/输出）接口控制2台步进电机同步工作，以实现钻井振动筛的倾角调节。结果表明，所设计的倾角调节系统对筛面固液分离状态的识别准确率达到97.33%，响应时间约为1.5 s，可满足钻井振动筛的倾角调节要求。该倾角调节系统设备体积小，成本低，且便于调试与维护，可有效提高钻井振动筛的自动化水平。

关键词： 钻井振动筛; 树莓派; 倾角调节系统; AlexNet模型; 图像识别

Abstract:

At present, the shale shakers in China are operated by manually detecting the solid-liquid separation status and manually adjusting the inclination angle of the screen surface, which can not realize self-adaptive work, and the phenomenon of drilling fluid "running" often occurs. To solve this problem, an inclination angle adjustment system for shale shaker based on Raspberry Pi and visual image was proposed. The system used Raspberry Pi, dedicated camera, motor drive board and two stepper motors as hardware platform, and was equipped with an image recognition software developed based on OpenCV and improved AlexNet model, which could achieve visual detection of screen surface solid-liquid separation status and automatic adjustment of screen surface inclination angle of the shale shaker. Firstly, according to the position characteristics of the liquid phase termination line during the solid-liquid separation process of shale shaker, the collected screen surface images were divided into three categories: normal, low mud and slurry running state, and the screen surface image dataset was constructed. Then, the AlexNet model based on transfer learning was constructed using TensorFlow platform to automatically recognize the screen surface solid-liquid separation status of shale shaker. Finally, based on the recognition results, two stepper motors were controlled synchronously by the GPIO (general purpose input/output) interface of Raspberry Pi to realize the inclination angle adjustment for the shale shaker. The results showed that the accuracy of the designed inclination angle adjustment system for recognizing screen surface solid-liquid separation status reached 97.3%, and the response time was about 1.5 s, which could meet the inclination angle adjustment requirements of the shale shaker. The inclination angle adjustment system equipment has small volume, low cost and is easy to debug and maintain, which can effectively improve the automation level of shale shakers.

Key words: shale shaker Raspberry Pi inclination adjustment system AlexNet model image recognition

收稿日期: 2023-04-03 出版日期: 2024-04-26

CLC:

TE 928

基金资助: 四川省科技计划项目(2021YFG0261)

作者简介: 侯勇俊（1967—），男，四川盐亭人，教授，博士，从事石油矿场机械、振动筛技术等研究，E-mail: hyj2643446@126.com，https://orcid.org/0000-0003-2000-4335

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引用本文:

侯勇俊,贾文俊,刘博文,吴先进. 基于树莓派和视觉图像的钻井振动筛倾角调节系统[J]. 工程设计学报, 2024, 31(2): 254-262.

Yongjun HOU,Wenjun JIA,Bowen LIU,Xianjin WU. Inclination angle adjustment system for shale shaker based on Raspberry Pi and visual image[J]. Chinese Journal of Engineering Design, 2024, 31(2): 254-262.

链接本文:

https://www.zjujournals.com/gcsjxb/CN/10.3785/j.issn.1006-754X.2024.03.147 或 https://www.zjujournals.com/gcsjxb/CN/Y2024/V31/I2/254

图1 钻井振动筛倾角调节系统总体框图

图2 树莓派4B平台的外设布局

图3 索尼IMX219传感器扩展板

图4 DRV8825电机驱动芯片的电路图

图5 钻井振动筛倾角调节系统程序控制流程

图6 筛面图像滤波处理结果

图7 筛面图像预处理结果

图8 筛面图像示例样本

图9 基于改进AlexNet的筛面图像识别模型

图10 基于改进AlexNet的筛面图像识别模型的训练和测试结果

图11 筛面图像测试集分类结果

图12 基于改进AlexNet的筛面图像识别模型的精确率曲线

图13 基于改进AlexNet的筛面图像识别模型的召回率曲线

图14 基于改进AlexNet的筛面图像识别模型的 F1 值曲线

表1 不同模型的精度评价结果对比

图15 钻井振动筛倾角调节系统实验装置

表2 钻井振动筛倾角调节系统测试结果

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