Preventive condition-based maintenance model of EMU components based on stochastic differential equation

To improve the operation and maintenance management level of large repairable components,such as electrical equipment,large nuclear power facilities,and high-speed electric multiple unit(EMU),and increase economic benefits,preventive maintenance has been widely used in industrial enterprises in recent years.Focusing on the problems of high maintenance costs and considerable failure hazards of EMU components in operation,we establish a state preventive maintenance model based on a stochastic differential equation.Firstly,a state degradation model of the repairable components is established in consideration of the degradation of the components and external random interference.Secondly,based on topology and martingale theory,the state degradation model is analyzed,and its simplex,stopping time,and martingale properties are proven.Finally,the monitoring data of the EMU components are taken as an example,analyzed and simulated to verify the effectiveness of the model.

Deep Learning Method and Infrared Imaging as a Tool for Transformer Faults Detection

In behaviour recognition, the development of the DL （Deep Learning） method introduced massive improvements in the field of artificial intelligence, where DL represents an upgrade of the present ANN （artificial neural network） architecture. Deep Learning as a comprehensive new field of artificial intelligence completely covers the neural networks architecture that is devised to carry out certain forms of identification, such as behaviour, forms of things, trends, similarities in complex forms, etc. Regarding thermography in energy, the cases used to illustrate this are photographs of active energy components in the plant. Failures that are seen with thermography cannot be recognized by other methods. However, an expert needs to do segmentation of focusing and classification of failures. The need for daily sampling and expert work is growing. With the DL method, it can be done in real time any time. One of the popular network architectures for using DL in image analysis is the recognition algorithm--CNN （convolution neural network）. Traditional artificial intelligence methods require determining factors and computations, leading to training algorithm. Machine learning has important features as welt as the right weight to make decisions about new input data. This work presents DL as a flexible and adaptive method for the analysis of thermal images of energy facilities, as well as a tool used for the construction and implementation of an efficient fault analysis on the 10/0.4 kV service transformer.