Aiming at improving the accuracy of planetary gearbox fault diagnosis, an integrated scheme based on dimensionality reduction method and deep belief networks (DBNs) is presented in this paper. Firstly, the acquired vibration signals are decomposed into mono-component called intrinsic mode functions (IMFs) through ensemble empirical mode decomposition (EEMD), and then Teager–Kaiser energy operator (TKEO) is used to track the instantaneous amplitude (IA) and instantaneous frequency (IF) of a mono-component amplitude modulation (AM) and frequency modulation (FM) signal. Secondly, a high dimensional feature set is constructed through extracting statistical features from six different signal groups. Then, an integrated dimensionality reduction method combining feature selection and feature extraction techniques is proposed to yield a more sensitive and lower dimensional feature set, which not only reduces the computation burden for fault diagnosis but also improves the separability of the samples by integrating the label information. Further, the low dimensional feature set is fed into DBNs classifier to identify the fault types using the optimal parameters selected by particle swarm optimization algorithm (PSO). Finally, two independent cases study of planetary gearbox fault diagnosis are carried out on test rig, and the results show that the proposed method provides higher accuracy in comparison with the existing methods.
Sweet potato production is constrained by Fusarium wilt, which is caused by Fusarium oxysporum f. sp. batatas (Fob). The identification of genes related to disease resistance and the underlying mechanisms will contribute to improving disease resistance via sweet potato breeding programs. In the present study, we performed de novo transcriptome assembly and digital gene expression (DGE) profiling of sweet potato challenged with Fob using Illumina HiSeq technology. In total, 89,944,188 clean reads were generated from 12 samples and assembled into 101,988 unigenes with an average length of 666 bp; of these unigenes, 62,605 (61.38%) were functionally annotated in the NCBI non-redundant protein database by BLASTX with a cutoff E-value of 10−5. Clusters of Orthologous Groups (COG), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations were examined to explore the unigenes’ functions. We constructed four DGE libraries for the sweet potato cultivars JinShan57 (JS57, highly resistant) and XinZhongHua (XZH, highly susceptible), which were challenged with pathogenic Fob. Genes that were differentially expressed in the four libraries were identified by comparing the transcriptomes. Various genes that were differentially expressed during defense, including chitin elicitor receptor kinase 1 (CERK), mitogen-activated protein kinase (MAPK), WRKY, NAC, MYB, and ethylene-responsive transcription factor (ERF), as well as resistance genes, pathogenesis-related genes, and genes involved in salicylic acid (SA) and jasmonic acid (JA) signaling pathways, were identified. These data represent a sequence resource for genetic and genomic studies of sweet potato that will enhance the understanding of the mechanism of disease resistance.
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