Fault diagnosis plays an important role in maintaining the safe and stable operation of hydropower units. This paper presents an intelligent fault diagnosis scheme for hydropower units based on the pattern recognition of axis orbits. Firstly, the vibration signals in X and Y directions which constitute the axis orbit of the unit shaft are processed by the denoising method based on VMD and PE entropy. Secondly, the relative position and distribution of the axis orbits for different samples in the image window are unified. Thirdly, the trained CNN is chosen as the classifier to recognize the axis orbit image for the fault type recognition. Through the analysis of the measured data of hydropower station, the influence of the sample number of training set and the size of axis orbit image on the performance of the proposed method and the necessity of denoising operation are studied. Compared with the existing methods, the proposed method has higher fault recognition accuracy and better generalization performance for different training sample sets. The results indicate that the proposed method is an effective alternative for the fault diagnosis of hydropower units.
The circadian clock is an autonomous oscillator that produces endogenous biological rhythms with a period of approximately 24 h. A number of circadian clock-associated factors have been intensely studied in the model plant Arabidopsis thaliana (At), including pseudo-response regulators (PRRs), which are key regulators of the circadian clock. In Populus trichocarpa (Pt), seven orthologs of the AtPRR genes have been identified. Here, the PtPRR family of genes, PtPRR1, PtPRR37, PtPRR5a, PtPRR5b, PtPRR73, PtPRR9la, and PtPRR9lb, were analyzed for circadian expression at the transcriptional level. These genes were expressed diurnally in the following order: PtPRR9la/PtPRR9lb→PtPRR37/PtPRR73 →PtPRR5a/PtPRR5b and PtPRR1, with the PtPRR mRNAs starting to accumulate sequentially in 2-3-h intervals. These sequential transcriptional events, termed 'circadian waves of PtPRR, ' were not significantly affected by the photoperiod conditions. All PtPRR genes were shown to be primarily expressed in mature leaves. These results suggest that members of the PtPRR family play important roles in mechanisms underlying the poplar circadian clock.
Axis-orbit recognition is an essential means for the fault diagnosis of hydropower units. An axis-orbit recognition method based on feature combination and feature selection is proposed, aiming to solve the problems of the low recognition accuracy, poor robustness, and low efficiency of existing axis-orbit recognition methods. First, various contour, moment, and geometric features of axis orbit samples are extracted from the original data and combined into a multidimensional feature set; then, Random Forest (RF)-Fisher feature selection is applied to realize feature dimensionality reduction; and finally, the selected features are set as the input of the support vector machine (SVM), which is optimized by the gravitational search algorithm (GSA) for axis-orbit recognition. The analytical results show that the proposed method has high recognition efficiency and good robustness while maintaining high accuracy for axis-orbit recognition.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.