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.

Optimization of location, power allocation and orientation for lighting lamps in a visible light communication system using the firefly algorithm.

To optimize the uniformity of signal-to-noise ratio (SNR) distribution in a visible light communication (VLC) system, the firefly algorithm is improved for joint optimization of location, power allocation and orientation of a light-emitting diode (LED) lamp array. Taking 16 LED lamps as an example, optimizations with a different number of degrees-of-freedom (DOF) are investigated. The orientation-involved optimizations significantly decrease average SNR and average illuminance. However, if the average illuminance is restricted to a large value, the effects of the orientation DOF would be small. With the restriction of illuminance, the optimization with all the three DOFs gives an improvement of 4.18 times in SNR uniformity, compared to the typical square-circle layout. The optimizations are further studied by varying the number of LED lamps.

The γ-gliadin-like γ-prolamin genes in the tribe Triticeae.

The γ-prolamins are important components of seed storage proteins in wheat and other Triticeae species. Here, the γ-prolamin genes from the diploid Triticeae species were systemically characterized. Most of the γ-prolamins (except 75 K γ-secalins) characterized were defined as γ-gliadin-like γ-prolamins, since they shared same characteristic model structure with γ-gliadins. Over one-third of these putatively functional γ-prolamin peptides contained different number of cysteine residues as compared to the eight residues present in γ-gliadins. Sequence polymorphism and linkage disequilibrium analyses showed the conservation of γ-prolamin genes in Triticeae species under evolutionary selection. Phylogenetic analyses indicated that these γ-prolamin genes can not be clearly separated according to their genomic origins, reflecting the conservation of γ-gliadinlike γ-prolamin genes after the divergence of Triticeae species. A screening of coeliac disease (CD) toxic epitopes shows that the γ-prolamins from some other genomes contain much fewer epitopes than those from the A, S (B) and D genomes of wheat. These findings contribute to better understanding of γ-prolamin family in Triticeae and build a ground for breeding less CD-toxic wheat cultivars.