This paper presents a comprehensive review of 1D Convolutional Neural Networks (1D CNNs) and their major engineering applications. 1D CNNs are a type of deep learning model that are particularly well-suited for processing 1D signals, such as electrocardiogram (ECG) signals, vibration signals, and other time-series data. Unlike 2D CNNs, which are designed for processing 2D images, 1D CNNs are designed for processing 1D data and are therefore more suitable for applications involving 1D signals. 1D CNNs have been shown to be effective in a variety of engineering applications, including structural health monitoring, fault detection, and condition monitoring. The paper also discusses the advantages of 1D CNNs, including their lower computational complexity and their ability to process 1D signals more efficiently than 2D CNNs. The paper concludes with a discussion of the future directions for research on 1D CNNs.This paper presents a comprehensive review of 1D Convolutional Neural Networks (1D CNNs) and their major engineering applications. 1D CNNs are a type of deep learning model that are particularly well-suited for processing 1D signals, such as electrocardiogram (ECG) signals, vibration signals, and other time-series data. Unlike 2D CNNs, which are designed for processing 2D images, 1D CNNs are designed for processing 1D data and are therefore more suitable for applications involving 1D signals. 1D CNNs have been shown to be effective in a variety of engineering applications, including structural health monitoring, fault detection, and condition monitoring. The paper also discusses the advantages of 1D CNNs, including their lower computational complexity and their ability to process 1D signals more efficiently than 2D CNNs. The paper concludes with a discussion of the future directions for research on 1D CNNs.