Amorphous solids such as glass are ubiquitous in our daily life and have found broad applications ranging from window glass and solar cells to telecommunications and transformer cores 1,2 . However, due to the lack of long-range order, the threedimensional (3D) atomic structure of amorphous solids have thus far defied any direct experimental determination 3-12 . Here, using a multi-component metallic glass as a model, we advance atomic electron tomography to determine its 3D atomic positions and chemical species with a precision of 21 picometer. We quantify the short-range order (SRO) and medium-range order (MRO) of the 3D atomic arrangement. We find that although the 3D atomic packing of the SRO is geometrically disordered, some SROs connect with each other to form crystal-like

Bearing is one of the most vital components of industrial machinery. The failure of bearing causes severe problems in the machinery. Therefore, continuous monitoring for the bearings is essential rather than regular manual checking, with the requirement for accuracy of prediction and efficiency. This paper proposes a novel intelligent bearing fault condition monitoring and diagnosis method focusing on computation efficiency, which is an important aspect of a continuous monitoring and embedded-based diagnosis device. In the proposed method, acoustic emission signals containing bearing health information are converted into 2-D spectrograms by Constant Q-Transform (CQT) before using a convolutional neural network to infer the bearing state. To reduce the latency while maintaining high accuracy, we propose an efficient search space for neural network architecture search, i.e., a channel distribution search, that automatically obtain the best performing network. Moreover, we present a separation between two processes of condition monitoring and fault diagnosis to save overall computing resources, with a policy of sharing weights in the training process and sharing features in the testing process. The experimental results show that the proposed method reduces about 50% inference time compared to previous methods while achieving an accuracy of 99.82% for eight types of single and compound fault diagnosis for variable rotational speeds.INDEX TERMS Acoustic emission, bearing fault condition monitoring, bearing fault diagnosis, convolutional neural network, neural network architecture search.

Liquids and solids are two fundamental states of matter. However, due to the lack of direct experimental determination, our understanding of the 3D atomic structure of liquids and amorphous solids remained speculative. Here we advance atomic electron tomography to determine for the first time the 3D atomic positions in monatomic amorphous materials, including a Ta thin film and two Pd nanoparticles. We observe that pentagonal bipyramids are the most abundant atomic motifs in these amorphous materials. Instead of forming icosahedra, the majority of pentagonal bipyramids arrange into a novel medium-range order, named the pentagonal bipyramid network. Molecular dynamic simulations further reveal that pentagonal bipyramid networks are prevalent in monatomic amorphous liquids, which rapidly grow in size and form icosahedra during the quench from the liquid state to glass state. The experimental method and results are expected to advance 2 the study of the amorphous-crystalline phase transition and glass transition at the single-atom level.In 1952, Frank hypothesized that icosahedral order is the prevalent atomic motif in monatomic liquids 1 . Over the past six decades, there have been a great deal of experimental, computational, and theoretical studies to understand the structure of liquids and amorphous materials [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] . A polytetrahedral packing model was proposed to explain the 3D atomic structure of monatomic liquids and amorphous materials 22 , in which icosahedral order is a key feature. The icosahedral order has also been found to play a critical role in the structure of metallic glasses and quasicrystals [23][24][25][26][27][28][29] . Despite all these developments, however, no experimental method could directly determine the 3D atomic packing of liquids and amorphous materials due to the lack of long-range order. Atomic electron tomography (AET), allowing the determination of 3D atomic structure of materials without assuming crystallinity 30 , is uniquely positioned to address this challenge. Since its first demonstration in 2012 31 , AET has been applied to reveal a wide range of crystal defects in materials such as grain boundaries, dislocations, stacking faults, point defects, atomic ripples, bond distortion, strain tensors and chemical order/disorder in three and four dimensions 30,[32][33][34][35][36][37][38][39] . More recently, AET was used to determine the structure of a multi-component glass-forming nanoparticle and quantitatively characterize the short-and medium-range order of its 3D atomic arrangement 40 . Here we advance AET to reveal the 3D atomic structure of an amorphous Ta thin film and two amorphous Pd nanoparticles that are not metallic glasses but liquid-like solids. We observe that pentagonal bipyramids are the main atomic motifs in the monatomic amorphous materials. Instead of assembling icosahedra, most pentagonal bipyramids closely connect

Predicting bearing faults is an essential task in machine health monitoring because bearings are vital components of rotary machines, especially heavy motor machines. Moreover, indicating the degradation level of bearings will help factories plan maintenance schedules. With advancements in the extraction of useful information from vibration signals, diagnosis of motor failures by maintenance engineers can be gradually replaced by an automatic detection process. Especially, state-of-the-art methods using deep learning have contributed significantly to automatic fault diagnosis. This paper proposes a novel method for diagnosing bearing faults and their degradation level under variable shaft speed. In the proposed method, vibration signals are represented by spectrograms to apply deep learning methods through preprocessing using Short-Time Fourier Transform (STFT). Then, feature extraction and health status classification are performed by a convolutional neural network (CNN), VGG16. According to our various experiments, our proposed method can achieve very high accuracy and robustness for bearing fault diagnosis even under noisy environments.

Bearing elements are vital in induction motors; therefore, early fault detection of rolling-element bearings is essential in machine health monitoring. With the advantage of fault feature representation techniques of time–frequency domain for nonstationary signals and the advent of convolutional neural networks (CNNs), bearing fault diagnosis has achieved high accuracy, even at variable rotational speeds. However, the required computation and memory resources of CNN-based fault diagnosis methods render it difficult to be compatible with embedded systems, which are essential in real industrial platforms because of their portability and low costs. This paper proposes a novel approach for establishing a CNN-based process for bearing fault diagnosis on embedded devices using acoustic emission signals, which reduces the computation costs significantly in classifying the bearing faults. A light state-of-the-art CNN model, MobileNet-v2, is established via pruning to optimize the required system resources. The input image size, which significantly affects the consumption of system resources, is decreased by our proposed signal representation method based on the constant-Q nonstationary Gabor transform and signal decomposition adopting ensemble empirical mode decomposition with a CNN-based method for selecting intrinsic mode functions. According to our experimental results, our proposed method can provide the accuracy for bearing faults classification by up to 99.58% with less computation overhead compared to previous deep learning-based fault diagnosis methods.