Gearbox Compound Fault Diagnosis in Edge-IoT Based on Legendre Multiwavelet Transform and Convolutional Neural Network.

The application of edge computing combined with the Internet of Things (edge-IoT) has been rapidly developed. It is of great significance to develop a lightweight network for gearbox compound fault diagnosis in the edge-IoT context. The goal of this paper is to devise a novel and high-accuracy lightweight neural network based on Legendre multiwavelet transform and multi-channel convolutional neural network (LMWT-MCNN) to fast recognize various compound fault categories of gearbox. The contributions of this paper mainly lie in three aspects: The feature images are designed based on the LMWT frequency domain and they are easily implemented in the MCNN model to effectively avoid noise interference. The proposed lightweight model only consists of three convolutional layers and three pooling layers to further extract the most valuable fault features without any artificial feature extraction. In a fully connected layer, the specific fault type of rotating machinery is identified by the multi-label method. This paper provides a promising technique for rotating machinery fault diagnosis in real applications based on edge-IoT, which can largely reduce labor costs. Finally, the PHM 2009 gearbox and Paderborn University bearing compound fault datasets are used to verify the effectiveness and robustness of the proposed method. The experimental results demonstrate that the proposed lightweight network is able to reliably identify the compound fault categories with the highest accuracy under the strong noise environment compared with the existing methods.

Optimizing the Structure and Optical Properties of Lanthanum Aluminate Perovskite through Nb5+ Doping.

This work involves the introduction of niobium oxide into lanthanum aluminate (LaAlO3) via a conventional solid-state reaction technique to yield LaAlO3:Nb (LaNbxAl1-xO3+δ) samples with Nb5+ doping levels ranging from 0.00 to 0.25 mol%. This study presents a comprehensive investigation of the effects of niobium doping on the phase evolution, defect control, and reflectance of LaNbxAl1-xO3+δ powder. Powder X-ray diffraction (XRD) analysis confirms the perovskite structure in all powders, and XRD and transmission electron microscopy (TEM) reveal successful doping of Nb5+ into LaNbxAl1-xO3+δ. The surface morphology was analyzed by scanning electron microscopy (SEM), and the results show that increasing the doping concentration of niobium leads to fewer microstructural defects. Oxygen vacancy defects in different compositions are analyzed at 300 K, and as the doping level increases, a clear trend of defect reduction is observed. Notably, LaNbxAl1-xO3+δ with 0.15 mol% Nb5+ exhibits excellent reflectance properties, with a maximum infrared reflectance of 99.7%. This study shows that LaNbxAl1-xO3+δ powder materials have wide application potential in the field of high reflectivity coating materials due to their extremely low microstructural defects and oxygen vacancy defects.