Wind turbines are widely installed as the new source of cleaner energy production. Dynamic and random stress imposed on the generator bearing of a wind turbine may lead to overheating and failure. In this paper, a data-driven approach for condition monitoring of generator bearings using temporal temperature data is presented. Four algorithms, the support vector regression machine, neural network, extreme learning machine, and the deep belief network are applied to model the bearing behavior. Comparative analysis of the models has demonstrated that the deep belief network is most accurate. It has been observed that the bearing failure is preceded by a change in the prediction error of bearing temperature. An exponentially-weighted moving average (EWMA) control chart is deployed to trend the error. Then a binary vector containing the abnormal errors and the normal residuals are generated for classifying failures. LS-SVM based classification models are developed to classify the fault bearings and the normal ones. The proposed approach has been validated with the data collected from 11 wind turbines.
Pre-mRNA splicing factors play a fundamental role in regulating transcript diversity both temporally and spatially. Genetic defects in several spliceosome components have been linked to a set of non-overlapping spliceosomopathy phenotypes in humans, among which skeletal developmental defects and non-syndromic retinitis pigmentosa (RP) are frequent findings. Here we report that defects in spliceosome-associated protein CWC27 are associated with a spectrum of disease phenotypes ranging from isolated RP to severe syndromic forms. By whole-exome sequencing, recessive protein-truncating mutations in CWC27 were found in seven unrelated families that show a range of clinical phenotypes, including retinal degeneration, brachydactyly, craniofacial abnormalities, short stature, and neurological defects. Remarkably, variable expressivity of the human phenotype can be recapitulated in Cwc27 mutant mouse models, with significant embryonic lethality and severe phenotypes in the complete knockout mice while mice with a partial loss-of-function allele mimic the isolated retinal degeneration phenotype. Our study describes a retinal dystrophy-related phenotype spectrum as well as its genetic etiology and highlights the complexity of the spliceosomal gene network.
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