高效振动信号压缩与异常检测的感知器IO模型

IF 5.9 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Guillaume Lebonvallet;Luis A. Salazar-Zendeja;Faicel Hnaien;Hichem Snoussi;Brice Nélain
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引用次数: 0

摘要

本研究提出了一个深度学习模型,用于振动信号压缩和异常检测,使用percepver io -一种基于注意力的架构-在铁路行业的案例研究中。该模型重量轻,针对能源和内存受限的传感器进行了优化,是嵌入式应用的理想选择。与传统方法不同,它同时执行信号压缩和异常检测,提供了适用于各行业的高效统一的解决方案。为了避免需要单独的分类模型或繁重的体系结构,使用对比学习来改进特征提取。这使得学习表征可以通过简单的线性函数轻松分离,从而在保持信号重建质量的同时实现有效的异常检测。该模型使用加权损失函数结合均方误差(mse)、余弦相似度和对比损失在大型振动信号数据集上进行训练。对比学习在正常和异常信号聚类中起着关键作用,达到近乎完美的分类精度。压缩性能是用峰值信噪比(PSNR)、余弦相似度和压缩比等指标来评估的。该模型在保持更小的架构的同时实现了21.87的PSNR。该方法显示了实时工业部署的强大潜力,确保了资源受限环境下的高精度和高效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Perceiver IO Model for Efficient Vibration Signal Compression and Anomaly Detection
This research presents a deep learning model for vibration signal compression and anomaly detection, using Perceiver IO—an attention-based architecture—within a railway industry case study. The model is lightweight and optimized for deployment on energy- and memory-constrained sensors, making it ideal for embedded applications. Unlike traditional methods, it performs signal compression and anomaly detection simultaneously, offering an efficient and unified solution applicable across industries. To avoid the need for separate classification models or heavy architectures, contrastive learning is used to improve feature extraction. This allows the learned representations to be easily separable via a simple linear function, enabling effective anomaly detection while preserving signal reconstruction quality. The model is trained on a large vibration signal dataset using a weighted loss function combining mean squared error (mse), cosine similarity, and contrastive loss. Contrastive learning plays a key role in clustering normal and abnormal signals, achieving near-perfect classification accuracy. Compression performance is evaluated with metrics such as peak signal-to-noise ratio (PSNR), cosine similarity, and compression ratio. The model achieves a PSNR of 21.87 while maintaining a much smaller architecture. The approach shows strong potential for real-time industrial deployment, ensuring high accuracy and efficiency in resource-constrained environments.
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来源期刊
IEEE Transactions on Instrumentation and Measurement
IEEE Transactions on Instrumentation and Measurement 工程技术-工程:电子与电气
CiteScore
9.00
自引率
23.20%
发文量
1294
审稿时长
3.9 months
期刊介绍: Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.
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