A convolutional neural network (CNN) is one of the most significant networks in the deep learning field. Since CNN made impressive achievements in many areas, including but not limited to computer vision and natural language processing, it attracted much attention from both industry and academia in the past few years. The existing reviews mainly focus on CNN's applications in different scenarios without considering CNN from a general perspective, and some novel ideas proposed recently are not covered. In this review, we aim to provide some novel ideas and prospects in this fast-growing field. Besides, not only 2-D convolution but also 1-D and multidimensional ones are involved. First, this review introduces the history of CNN. Second, we provide an overview of various convolutions. Third, some classic and advanced CNN models are introduced; especially those key points making them reach state-of-the-art results. Fourth, through experimental analysis, we draw some conclusions and provide several rules of thumb for functions and hyperparameter selection. Fifth, the applications of 1-D, 2-D, and multidimensional convolution are covered. Finally, some open issues and promising directions for CNN are discussed as guidelines for future work. 

/pdf/a-survey-of-convolutional-neural-networks-analysis-1hzyfrmo.pdf

A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects

1D convolutional neural networks and applications: A survey

Convolutional Neural Network (CNN) is one of the most significant networks in the deep learning field. Since CNN made impressive achievements in many areas, including but not limited to computer vision and natural language processing, it attracted much attention both of industry and academia in the past few years. The existing reviews mainly focus on the applications of CNN in different scenarios without considering CNN from a general perspective, and some novel ideas proposed recently are not covered. In this review, we aim to provide novel ideas and prospects in this fast-growing field as much as possible. Besides, not only two-dimensional convolution but also one-dimensional and multi-dimensional ones are involved. First, this review starts with a brief introduction to the history of CNN. Second, we provide an overview of CNN. Third, classic and advanced CNN models are introduced, especially those key points making them reach state-of-the-art results. Fourth, through experimental analysis, we draw some conclusions and provide several rules of thumb for function selection. Fifth, the applications of one-dimensional, two-dimensional, and multi-dimensional convolution are covered. Finally, some open issues and promising directions for CNN are discussed to serve as guidelines for future work.

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.

pdf/1d-convolutional-neural-networks-and-applications-a-survey-2nsgvr9zjw.pdf

1D Convolutional Neural Networks and Applications: A Survey

A review of vibration-based damage detection in civil structures : from traditional methods to Machine Learning and Deep Learning applications

The development of a powerful new magnetorheological fluid (MRF), together with recent progress in the understanding of the behavior of such fluids, has convinced researchers and engineers that MRF dampers are among the most promising devices for semi-active automotive suspension vibration control, because of their large force capacity and their inherent ability to provide a simple, fast and robust interface between electronic controls and mechanical components. In this paper, theoretical and experimental studies are performed for the design, development and testing of a completely new MRF damper model that can be used for the semi-active control of automotive suspensions. The MR damper technology presented in this paper is based on a completely new approach where, in contrast to in the conventional solutions where the coil axis is usually superposed on the damper axis and where the inner cylindrical housing is part of the magnetic circuit, the coils are wound in a direction perpendicular to the damper axis. The paper investigates approaches to optimizing the dynamic response and provides experimental verification. Both experimental and theoretical results have shown that, if this particular model is filled with an 'MRF 336AG' MR fluid, it can provide large controllable damping forces that require only a small amount of energy. For a magnetizing system with four coils, the damping coefficient could be increased by up to three times for an excitation current of only 2 A. Such current could be reduced to less than 1 A if the magnetizing system used eight small cores. In this case, the magnetic field will be more powerful and more regularly distributed. In the presence of harmonic excitation, such a design will allow the optimum compromise between comfort and stability to be reached over different intervals of the excitation frequencies.

https://iopscience.iop.org/article/10.1088/0964-1726/14/4/041/pdf

An innovative magnetorheological damper for automotive suspension: from design to experimental characterization

This paper presents a fast, accurate, and simple systematic approach for online condition monitoring and severity identification of ball bearings. This approach utilizes compact one-dimensional (1-D) convolutional neural networks (CNNs) to identify, quantify, and localize bearing damage. The proposed approach is verified experimentally under several single and multiple damage scenarios. The experimental results demonstrated that the proposed approach can achieve a high level of accuracy for damage detection, localization, and quantification. Besides its real-time processing ability and superior robustness against the high-level noise presence, the compact and minimally trained 1-D CNNs in the core of the proposed approach can handle new damage scenarios with utmost accuracy.

/pdf/fault-detection-and-severity-identification-of-ball-bearings-1n0p6xdbum.pdf

Fault Detection and Severity Identification of Ball Bearings by Online Condition Monitoring

Thin-walled structures are used in safety applications such as automotive vehicles and locomotives because of their efficient energy absorption, light weight, and reduced manufacturing cost. The performance of these structures is inhibited under oblique loading conditions. This paper numerically studies the crushing behavior and performance of corrugated tapered tubes (CTTs) as potential efficient thin-walled structures under oblique loading conditions. The proposed CTT design is impacted under 7 different loading angles with a striker mass of 275 kg and 15 m/s velocity. The material assigned to the proposed design structure is AA6060 aluminum alloy. The effect of loading angles on various performance indicators, such as the initial peak force (PF), mean crushing force (MF), energy absorption (EA) and specific energy absorption (SEA) was studied. It was found that increasing the impact angles lead to a reduction in performance, and a reduction of around 54% in EA and SEA was observed when the impact angle increased from 0° to 40°. Moreover, the effect of the geometric parameters on the performance indicators was also investigated. In addition, global bending was found to develop at higher impact angles for CTTs of 80° tapered angles. Finally, it was found that some CTTs can achieve higher SEA relative to their tapered conventional counterparts.

Collapse behavior of thin-walled corrugated tapered tubes under oblique impact

Tracking surface degradation of ball bearings by means of new time domain scalar indicators

The present work deals with the influence of initial geometric imperfections on the dynamic behavior of simply supported rectangular plates subjected to the action of periodic in-plane forces The nonlinear large-deflection plate theory used in this analysis corresponds to the dynamic analog of von Karman's theory The temporal response is analyzed by the first-order generalized asymptotic method The solution for the temporal equations of motion takes into account the possibility of existence of simultaneous forced and parametric vibrations The results indicate that the presence of initial imperfections may significantly raise the resonance frequencies, cause the plate to exhibit a soft spring behavior and improve slightly the stability of the plate by reducing the area of its instability zones Furthermore, the presence of initial imperfections induces forced vibrations which interact with parametric vibrations in order to generate a competitive hesitation phenomenon in the transition zone

Effects of initial geometric imperfections on dynamic behavior of rectangular plates

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