1D Convolutional Neural Networks and Applications: A Survey

TL;DR

This survey reviews 1D CNN architectures and their recent applications in fields like biomedical data analysis, structural health monitoring, and fault detection, emphasizing their efficiency, real-time capabilities, and state-of-the-art performance.

Contribution

It provides a comprehensive overview of 1D CNN architectures, applications, and benchmarks, highlighting recent progress and practical implementation advantages.

Findings

1D CNNs achieve state-of-the-art results in biomedical and fault detection applications.

They enable real-time, low-cost hardware implementations due to simple architecture.

Benchmark datasets and software are publicly available for further research.

Abstract

During the last decade, Convolutional Neural Networks (CNNs) have become the de facto standard for various Computer Vision and Machine Learning operations. CNNs are feed-forward Artificial Neural Networks (ANNs) with alternating convolutional and subsampling layers. Deep 2D CNNs with many hidden layers and millions of parameters have the ability to learn complex objects and patterns providing that they can be trained on a massive size visual database with ground-truth labels. With a proper training, this unique ability makes them the primary tool for various engineering applications for 2D signals such as images and video frames. Yet, this may not be a viable option in numerous applications over 1D signals especially when the training data is scarce or application-specific. To address this issue, 1D CNNs have recently been proposed and immediately achieved the state-of-the-art performance levels in several applications such as personalized biomedical data classification and early diagnosis, structural health monitoring, anomaly detection and identification in power electronics and motor-fault detection. Another major advantage is that a real-time and low-cost hardware implementation is feasible due to the simple and compact configuration of 1D CNNs that perform only 1D convolutions (scalar multiplications and additions). This paper presents a comprehensive review of the general architecture and principals of 1D CNNs along with their major engineering applications, especially focused on the recent progress in this field. Their state-of-the-art performance is highlighted concluding with their unique properties. The benchmark datasets and the principal 1D CNN software used in those applications are also publically shared in a dedicated website.