= 基于 CNN 和 MEMS 的边缘计算振动检测设备
曹王仁博 <cao.wangrenbo@yandex.com>
2024.09.07
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== 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。

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.

Keywords: vibration monitoring, edge computing, CNN, MEMS