Nuclear power plants have a large number of safety or non-safety related mechanical equipment. Knowing when and where these equipment failure or near failure is one of the key problems in nuclear safety and nuclear health management and maintenance, that is to predict the remaining useful life for specific equipment. A prediction model labeled as Weibull time to event recurrent neural network (WTTE-RNN) is proposed to effectively obtain the prediction and estimation of time to event (such as customer churn, patient survival, machine failure). The model is effective even under some missing-spots in the training data (i.e. deleted data). The purpose of this research is to study the main challenges faced by the application of WTTE-RNN to the nuclear equipment, and identify feasible optimization directions. The results may lay a foundation for the WTTE-RNN application to the remaining useful life.
The existing methods for pavement crack classification and identification solely offer information about the crack type, neglecting size and direction details, which are essential for guiding repair efforts and forming the engineer digital information data. In response to the challenges posed by insufficient crack information, prolonged training time and intricate parameter adjustment inherent in employing deep learning algorithms for pavement crack classification and recognition, we propose an integrated approach combining tensor voting with the random sample consensus for pavement crack classification and recognition. The method involves pre-processing road images using gray value transformation and the K-Means clustering algorithm. Subsequently, the tensor voting algorithm is applied to enhance the linear features, resulting in the generation of linear saliency maps of cracks along with crack junction information. Furthermore, a non-maximum suppression method and the RANSAC algorithm are employed to refine and fit the crack skeleton curves respectively, accomplishing the crack classification and recognition. The outcomes demonstrate that the proposed integrated approach in the crack skeleton segmentation algorithm yields an average F1-score of 0.7879, outperforming traditional non-maximum suppression methods. The accuracy of crack classification and recognition reaches 96%, outperforming other crack classification and recognition algorithms grounded in digital image processing methods. Compared with the neural networks employed for classification and recognition, the proposed algorithm is able to capture direction and size details of cracks, which can provide guidance for intelligent crack repair. This additional information can offer valuable guidance for intelligent crack repair processes.
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