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.
