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. Then, the TVMS of the cracked gear is derived based on the potential energy method. On this basis, the dynamic model of a PGT with both the carrier eccentricity error and compound gear cracks as internal excitations are established. Furthermore, the meshing characteristics and dynamic responses of the PGT are simulated to investigate the compound fault features. A series of experiments are conducted to further analyze the influence of the compound fault on the vibration response. The relevant conclusions can provide a reference for the compound fault diagnosis of a PGT in practice.

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. Firstly, a feature extractor is constructed to extract fault feature vectors from raw signals, which is based on local temporal self-similarity coupled with bag-of-words models (LTSS-BoW). Then, a multi-label classifier based on a capsule network (CapsNet) is designed, in which the dynamic routing algorithm and average threshold are adopted. The effectiveness of the proposed LTSS-BoW-CapsNet method is validated by processing three compound fault diagnosis tasks. The experimental results demonstrate that our method can via decoupling effectively identify the multi-fault components of different compound fault patterns. The testing accuracy is more than 97%, which is better than the other four traditional classification models.

Dealloying-Derived Nanoporous Cu6Sn5 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) Cu6Sn5 alloy and Cu6Sn5/Sn composite were applied as a lithium-ion battery anode. The as-dealloyed np-Cu6Sn5 has an ultrafine ligament size of 40 nm and a high BET-specific area of 15.9 m2 g-1. The anode shows an initial discharge capacity as high as 1200 mA h g-1, and it remains a capacity of higher than 600 mA h g-1 for the initial five cycles at 0.1 A g-1. After 100 cycles, the anode maintains a stable capacity higher than 200 mA h g-1 for at least 350 cycles, with outstanding Coulombic efficiency. The ex situ XRD patterns reveal the reverse phase transformation between Cu6Sn5 and Li2CuSn. The Cu6Sn5/Sn composite presents a similar cycling performance with a slightly inferior rate performance compared to np-Cu6Sn5. The study demonstrates that dealloyed nanoporous Cu6Sn5 alloy could be a promising candidate for lithium-ion batteries.