Synthical logo
Your space
From arXiv

Transformer-Based Denoising of Mechanical Vibration Signals

Mechanical vibration signal denoising is a pivotal task in various industrial applications, including system health monitoring and failure prediction. This paper introduces a novel deep learning transformer-based architecture specifically tailored for denoising mechanical vibration signals. The model leverages a Multi-Head Attention layer with 8 heads, processing input sequences of length 128, embedded into a 64-dimensional space. The architecture also incorporates Feed-Forward Neural Networks, Layer Normalization, and Residual Connections, resulting in enhanced recognition and extraction of essential features. Through a training process guided by the Mean Squared Error loss function and optimized using the Adam optimizer, the model demonstrates remarkable effectiveness in filtering out noise while preserving critical information related to mechanical vibrations. The specific design and choice of parameters offer a robust method adaptable to the complex nature of mechanical systems, with promising applications in industrial monitoring and maintenance. This work lays the groundwork for future exploration and optimization in the field of mechanical signal analysis and presents a significant step towards advanced and intelligent mechanical system diagnostics.
Published on August 4, 2023
Copy BibTeX
There is no AI-powered summary yet, because we do not have a budget to generate summaries for all articles.
1. Buy subscription
We will thank you for helping thousands of people to save their time at the top of the generated summary.
If you buy our subscription, you will be able to summarize multiple articles.
Pay $undefined
≈10 summaries
Pay $undefined
≈60 summaries
2. Share on socials
If this article gets to top-5 in trends, we'll summarize it for free.
Copy link