Xi'an Jiaotong University

About: Xi'an Jiaotong University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Heat transfer & Dielectric. The organization has 85440 authors who have published 99682 publications receiving 1579683 citations. The organization is also known as: '''Xi'an Jiaotong University''' & Xi'an Jiao Tong University.

Dislocated Time Series Convolutional Neural Architecture: An Intelligent Fault Diagnosis Approach for Electric Machine

Abstract: In most current intelligent diagnosis methods, fault classifiers of electric machine are built based on complex handcrafted features extractor from raw signals, which depend on prior knowledge and is difficult to implement intelligentization authentically. In addition, the increasingly complicated industrial structures and data make handcrafted features extractors less suited. Convolutional neural network (CNN) provides an efficient method to act on raw signals directly by weight sharing and local connections without feature extractors. However, effective as CNN works on image recognition, it does not work well in industrial applications due to the differences between image and industrial signals. Inspired by the idea of CNN, we develop a novel diagnosis framework based on the characteristics of industrial vibration signals, which is called dislocated time series CNN (DTS-CNN). The DTS-CNN architecture is composed of dislocate layer, convolutional layer, sub-sampling layer and fully connected layer. By adding a dislocate layer, this model can extract the relationship between signals with different intervals in periodic mechanical signals, thereby overcome the weaknesses of traditional CNNs and is more applicable for modern electric machines, especially under nonstationary conditions. Experiments under constant and nonstationary conditions are performed on a machine fault simulator to validate the proposed framework. The results and comparison with respect to the state of the art in the field is illustrated in detail, which highlights the superiority of the proposed method in industrial applications.

Novel barium titanate based capacitors with high energy density and fast discharge performance

Abstract: Recently, dielectric capacitors have attracted much attention due to their high power density based on fast charge–discharge capability. However, their energy storage applications are limited by their low discharge energy densities. In this work, we designed novel lead-free relaxor-ferroelectric 0.88BaTiO3–0.12Bi(Li0.5Nb0.5)O3 (0.88BT–0.12BLN) ceramics with high breakdown strength and high discharge energy density. The 0.88BT–0.12BLN ceramics were prepared by a conventional solid state reaction method. Optimal energy storage properties were obtained in 0.88BT–0.12BLN ceramics sintered at 1220 °C with an impressive discharge energy density of 2.032 J cm−3 and a charge–discharge efficiency of beyond 88% at 270 kV cm−1. The energy storage properties of the 0.88BT–0.12BLN also displayed good thermal stability from 20 to 120 °C at an electric field of 150 kV cm−1. Moreover, the discharge speed behavior was investigated by using pulsed current. The pulsed discharge current waveforms showed that all the samples have fast discharge times (less than 0.5 μs) under different electric fields. This work significantly increases the intrinsic breakdown strength and discharge energy density of BaTiO3-based materials with high charge–discharge efficiency for high power energy storage devices.