Compound Fault Diagnosis of Planetary Gearbox Based on Improved LTSS-BoW Model and Capsule Network.

The identification of compound fault components of a planetary gearbox is especially important for keeping the mechanical equipment working safely. However, the recognition performance of existing deep learning-based methods is limited by insufficient compound fault samples and single label classification principles. To solve the issue, a capsule neural network with an improved feature extractor, named LTSS-BoW-CapsNet, is proposed for the intelligent recognition of compound fault components.

Compound Fault Characteristic Analysis for Fault Diagnosis of a Planetary Gear Train.

The carrier eccentricity error and gear compound faults are most likely to occur simultaneously in an actual planetary gear train (PGT). Various faults and errors are coupled with each other to generate a complex dynamic response, which makes the diagnosis of PGT faults difficult in practice. In order to analyze the joint effect of the error and the compound faults in a PGT, a carrier eccentricity error model is proposed and incorporated into the TVMS model by considering the time-varying center distance, line of action (LOA), meshing angle, and contact ratio.

Dealloying-Derived Nanoporous CuSn Alloy as Stable Anode Materials for Lithium-Ion Batteries.

The volume expansion during Li ion insertion/extraction remains an obstacle for the application of Sn-based anode in lithium ion-batteries. Herein, the nanoporous (np) CuSn alloy and CuSn/Sn composite were applied as a lithium-ion battery anode. The as-dealloyed np-CuSn has an ultrafine ligament size of 40 nm and a high BET-specific area of 15.