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Fault-influencing factors analysis is an important part of the quality supervision process. There are double functions for high-voltage switchgears that switch off and protect electric circuits in power transmission lines. Such devices have serious impact on power grid–operating efficiency, factory operation, and resident life, which will cause economic losses. As it was difficult for traditional methods to analyze fault-influencing factors accurately and comprehensively, a novel method based on industrial big data was proposed to analyze high-voltage switchgears fault-influencing factors in the process of quality supervision in this article, which integrated the qualitative and quantitative analyses method. In this model, the Classification Based on Multiple Class-Association Rules based on Gaussian Mixture Model as the qualitative analysis method was adapted to analyze the whole life cycle of fault-influencing factors of high-voltage switchgears comprehensively, and supplied fault-influencing factors with discrete interval value ranges. The logistic regression method based on qualitative analysis was constructed to calculate fault occurrence probability quantitatively, including the single-fault occurrence probability and the multiple-faults joint occurrence probability. In addition, the single-fault occurrence probability was used to modify the discrete interval value ranges calculated by the qualitative analysis method, which could make the ranges more accurately. Consequently, the proposed method could provide important reference for high-voltage switchgears operation maintenance, and it would be possible to design accurate maintenance plans before equipment failure. The final instance demonstrates the effectiveness of the proposed methodology.

Due to the complex shield construction characteristics and the complex effects of geological environment, it is difficult to control the direction of shield tunneling and to determine the reasonable tunneling parameters such as tunneling speed and so on. During the tunneling, shield tunneling machine may rise, shift and snake advance, which are not conducive to control tunnel axis. Aiming at the problem that it is difficult to accurately predict and correct the axis attitude deviation in the shield tunneling process, a prediction of axis attitude deviation and deviation correction method based on data driven during shield tunneling was put forward in this paper. Under certain geological conditions, the relationship between the attitude deviation of the construction axis and tunneling parameters during shield tunneling process with different tunneling mileages is established. Based on the tunneling historical data, the XGBoost prediction model is constructed, and the axis deviation variation is predicted and analyzed with the shield construction parameters. The multi-ring deviation correction parameter calculation model based on the fusion of geometric model and association rules is designed to obtain the internal correlation of the deviation amount, the number of deviation correction rings and the deviation correction parameters of each ring under the maximum deviation of different deviation correction sections, so as to realize the accurate prediction and deviation correction of shield axis deviation in the complex construction process. Under the verification of 155 ring data in a certain subway construction section, the method proposed in this paper has higher prediction accuracy, which is important for improving the safety and quality of shield tunneling. Results from the measured shield construction ring data verify the reasonability of the proposed axis attitude deviation prediction and multi-ring deviation correction method.

Liquid crystal (LC) smart windows with adjustable reflectivity have been gradually applied in green and intelligent building materials for energy saving needs, but their applications are limited by their fundamental defects. In this study, we developed local photo-induced in situ polymerization to rapidly fabricate the infrared reflection microsheets of a cholesteric LC polymer as functional units. With the exception of the LC formula, the photo mask, liquid crystal cell, polymerization inhibitor, and the preparation conditions were specifically managed to control the extent of in situ polymerization, namely the microsheet morphology. The circular, triangular and oval-shaped microsheets were precisely obtained and were slightly bigger than the light hole. This easy, controllable, continuous and recyclable technology is expected to promote the industrialization of a high quality LC smart window with an adjustable reflection band and state.