From d7473603a8f077b3ba33aad9666121945b1ecd89 Mon Sep 17 00:00:00 2001 From: CaoWangrenbo Date: Thu, 28 Mar 2024 11:45:36 +0800 Subject: [PATCH] =?UTF-8?q?=E4=BF=AE=E6=94=B9=E7=AE=80=E4=BB=8B=E5=92=8C?= =?UTF-8?q?=E5=A2=9E=E5=8A=A0=20PCB=20=E8=AE=BE=E8=AE=A1=E9=83=A8=E5=88=86?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- main.adoc | 77 +++++++++++++------------------------------------------ 1 file changed, 18 insertions(+), 59 deletions(-) diff --git a/main.adoc b/main.adoc index 187eb17..ea03504 100644 --- a/main.adoc +++ b/main.adoc @@ -1,4 +1,4 @@ -= 基于 CNN 和 MEMS 的边缘计算振动检测设备 += 基于 CNN 和 MEMS 的边缘计算振动检测节点 曹王仁博 2024.03.11 :toc: auto @@ -6,69 +6,13 @@ == 摘要 -随着物联网 (IoT) 和工业 4.0 的快速发展，对实时监测和分析振动数据的需求不断增长。传统的振动监测系统通常依赖于中心服务器进行数据处理，这会带来延迟和数据传输成本高昂的问题。为了解决这些问题，本文提出了一种基于卷积神经网络 (CNN) 和微机电系统 (MEMS) 的边缘计算振动检测设备。该设备能够在本地进行实时振动分析，并通过无线网络将结果传输到远程服务器。 - -=== 研究现状 - -传统的振动监测系统通常由传感器、数据采集器和中心服务器组成。传感器负责采集振动数据，数据采集器负责将数据传输到中心服务器，中心服务器负责进行数据分析和处理。这种模式存在以下问题： - -- 调试过程复杂：传统方案主要依靠专家针对测得的信号进行时域和频域特征的分析，通过尝试合适的算法提取振动特征，对于不同的设备需要专门化处理，对于复杂场景的快速部署是不利的。 -- 数据延迟：由于数据需要传输到中心服务器进行处理，因此会存在一定的延迟，这对于需要实时监测的应用来说是不可接受的。 -数据传输成本高昂：对于大型监测系统，需要传输大量数据，这会带来高昂的数据传输成本。 -- 安全性和可靠性问题：中心服务器容易受到攻击和故障的影响，这会影响系统的安全性和可靠性。 - -=== 边缘计算 - -边缘计算是一种将计算资源从中心服务器迁移到边缘设备的新兴技术。边缘设备可以是任何靠近数据源的设备，例如智能手机、智能传感器和网关。边缘计算可以有效解决传统模式存在的延迟、成本和安全问题。 - -=== CNN 和 MEMS - -CNN 是一种深度学习算法，在图像识别和自然语言处理等领域取得了巨大成功。CNN 也可用于振动信号分析。MEMS 是一种微型机电系统，可以用于制造微型传感器。MEMS 传感器可以用于采集振动数据。 - -=== 本文提出的设备 - -本文提出的设备由以下部分组成： - -- MEMS 传感器：用于采集振动数据。 -- CNN 处理器：用于进行实时振动分析。 -- 无线通信模块：用于将分析结果传输到远程服务器。 - - -本文提出了一种基于 CNN 和 MEMS 的边缘计算振动检测设备。该设备能够在本地进行实时振动分析，并通过无线网络将结果传输到远程服务器。该设备可以有效解决传统振动监测系统存在的延迟、成本和安全问题。 +随着物联网 (IoT) 和工业 4.0 的快速发展，对实时监测和分析振动数据的需求不断增长。传统的振动监测系统通常依赖于中心服务器进行数据处理，这会带来延迟和数据传输成本高昂的问题。为了解决这些问题，本文提出了一种基于卷积神经网络 (CNN) 和微机电系统 (MEMS) 的边缘计算振动检测设备。该设备通过采用 MEMS 技术的加速度传感器和温湿度传感器，采集被测对象的相关振动数据和温度信息，并通过快速傅里叶变换获取振动信号的频域特征。将采集到的三轴加速度计的时域或频域数据通过卷积神经网络获得能检测被测对象工况的分类模型，并通过 cube-ai 工具将模型转化压缩并部署到基于微控制器的边缘计算振动检测节点上。该节点具有自动工况识别，基于工业现场总线和蓝牙数据回传的功能。通过该设备，能够实现简化的工况辨识，无需针对特定设备人工设定辨识特征，并可作为兼容的 Modbus 传感器或者 BLE 节点被动查询或主动上报相关数据。实现低成本、易部署的振动检测。 关键词：振动监测、边缘计算、CNN、MEMS == Abstract -With the rapid development of the Internet of Things (IoT) and Industry 4.0, there is a growing need for real-time monitoring and analysis of vibration data. Traditional vibration monitoring systems typically rely on a central server for data processing, which introduces problems with latency and high data transmission costs. To address these issues, this paper proposes an edge computing vibration detection device based on convolutional neural networks (CNN) and micro-electromechanical systems (MEMS). The device is capable of performing real-time vibration analysis locally and transmitting the results to a remote server via wireless network. - -=== Current status of research - -Traditional vibration monitoring systems usually consist of sensors, data collectors and central servers. The sensor is responsible for collecting vibration data, the data collector is responsible for transmitting the data to the central server, and the central server is responsible for data analysis and processing. This model has the following problems: - -- The debugging process is complex: Traditional solutions mainly rely on experts to analyze the time domain and frequency domain characteristics of the measured signals, and extract vibration characteristics by trying appropriate algorithms. Specialized processing is required for different equipment, and rapid deployment in complex scenarios is Adverse. -- Data delay: Since the data needs to be transmitted to the central server for processing, there will be a certain delay, which is unacceptable for applications that require real-time monitoring. -Data transmission costs are high: For large monitoring systems, a large amount of data needs to be transmitted, which will bring high data transmission costs. -- Security and reliability issues: Central servers are vulnerable to attacks and failures, which can affect the security and reliability of the system. - -=== Edge Computing - -Edge computing is an emerging technology that migrates computing resources from central servers to edge devices. An edge device can be any device that is close to the data source, such as smartphones, smart sensors, and gateways. Edge computing can effectively solve the latency, cost and security issues of traditional models. - -=== CNN and MEMS - -CNN is a deep learning algorithm that has achieved great success in areas such as image recognition and natural language processing. CNN can also be used for vibration signal analysis. MEMS are microelectromechanical systems that can be used to create tiny sensors. MEMS sensors can be used to collect vibration data. - -=== Equipment proposed in this article - -The device proposed in this article consists of the following parts: - -- MEMS sensors: used to collect vibration data. -- CNN processor: for real-time vibration analysis. -- Wireless communication module: used to transmit analysis results to a remote server. - - -This paper proposes an edge computing vibration detection device based on CNN and MEMS. The device is capable of performing real-time vibration analysis locally and transmitting the results to a remote server via wireless network. This device can effectively solve the delay, cost and safety problems of traditional vibration monitoring systems. +With the rapid development of the Internet of Things (IoT) and Industry 4.0, there is a growing need for real-time monitoring and analysis of vibration data. Traditional vibration monitoring systems typically rely on a central server for data processing, which introduces problems with latency and high data transmission costs. To address these issues, this paper proposes an edge computing vibration detection device based on convolutional neural networks (CNN) and microelectromechanical systems (MEMS). The device collects relevant vibration data and temperature information of the measured object through acceleration sensors and temperature and humidity sensors using MEMS technology, and obtains the frequency domain characteristics of the vibration signal through fast Fourier transform. Use the collected time domain or frequency domain data of the three-axis accelerometer through a convolutional neural network to obtain a classification model that can detect the working conditions of the measured object, and use the cube-ai tool to convert, compress and deploy the model to a microcontroller-based Edge computing vibration detection node. The node has automatic working condition recognition and is based on industrial fieldbus and Bluetooth data return functions. Through this device, simplified working condition identification can be achieved without the need to manually set identification characteristics for specific equipment, and it can be used as a compatible Modbus sensor or BLE node to passively query or actively report relevant data. Enable low-cost, easy-to-deploy vibration detection. Keywords: vibration monitoring, edge computing, CNN, MEMS @@ -296,6 +240,21 @@ image::doc_attachments/2024-03-26T08-58-51-017Z.png[XC6206 典型应用电路] === 基于 MEMS 和 CNN 的边缘计算振动监测节点 PCB 设计 +为了保证设计对于振动、温度变化等环境的耐受性以及生产时的一致性，整体电路采用印刷电路板（PCB）制作。PCB 的器件布局，功率、信号线的布置对于产品性能有着不可忽视的影响，因此需要对 PCB 进行合理设计。 + +==== 电源电路布局布线 + +电源方面，本设计中设备功耗较低，实测运行时功率在 0.5W 以下，负载电流较小。 + +对于 SGM6132，功率方面较大电流网络 SW 和 VIN 使用较大面积铺铜。但由于 SW 对其他网络存在高频干扰，所以布局时要尽量将电感和续流二极管靠近 SW 引脚附近，并在许可情况下减少该网络铺铜面积和长度。反馈电阻也应靠近 FB 引脚放置，以减少线上电阻带来的损耗。 + +对于用电器件，滤波和退耦电容应尽量贴近器件供电引脚放置，并且要求路径先通过电容，以保证效果。 + +==== 通信接口布局布线 + +本设计中使用的通信接口，除 RS-485 以外都不是差分信号，通信速率都较低且都为串行通信方式，一般情况下不需要考虑线路阻抗和等长需求。对于 RS-485 信号，可以将其作为差分对布线。 + + == 基于 MEMS 和 CNN 的边缘计算振动监测节点的程序开发 === 基于 MEMS 和 CNN 的边缘计算振动监测节点主控程序开发