This paper analyzed the surface conditions and boundary-layer climate of regional haze events and heavy haze in southern Jiangsu Province in China. There are 5 types with the surface conditions which are equalized pressure (EQP), the advancing edge of a cold front (ACF), the base of high pressure (BOH), the backside of high pressure (BAH), the inverted trough of low pressure (INT), and saddle pressure (SAP) with the haze days. At that time, 4 types are divided with the regional haze events and each of which has a different boundary-layer structure. During heavy haze, the surface mainly experiences EQP, ACF, BOH, BAH, and INT which also have different boundary-layer structures.

In order to reduce maintenance costs and avoid safety accidents, it is of great significance to carry out fault prediction to reasonably arrange maintenance plans for rotating mechanical equipment. At present, the relevant research mainly focuses on fault diagnosis and remaining useful life (RUL) predictions, which cannot provide information on the specific health condition and fault types of rotating mechanical equipment in advance. In this paper, a novel three-stage fault prediction method is presented to realize the identification of the degradation period and the type of failure simultaneously. Firstly, based on the vibration signals from multiple sensors, a convolutional neural network (CNN) and long short-term memory (LSTM) network are combined to extract the spatiotemporal features of the degradation period and fault type by means of the cross-entropy loss function. Then, to predict the degradation trend and the type of failure, the attention-bidirectional (Bi)-LSTM network is used as the regression model to predict the future trend of features. Furthermore, the predicted features are given to the support vector classification (SVC) model to identify the specific degradation period and fault type, which can eventually realize a comprehensive fault prediction. Finally, the NSF I/UCR Center for Intelligent Maintenance Systems (IMS) dataset is used to verify the feasibility and efficiency of the proposed fault prediction method.

The influence of structural characteristics on the cable ampacity and electrical property of the cross-linked polyethylene (XLPE) cable was presented in this paper. First, four HVAC XLPE cables were selected to detect the structural characteristics by Fourier transform infrared spectroscopy spectrum, gel content, differential scanning calorimetry, and x-ray diffraction measurements. Second, relevant structural parameters were extracted from the measurements. Subsequently, the cable ampacity and electrical property, serving as the macroscopic properties of the cable, were quantified by the measurement of thermal resistivity ρ and breakdown field strength E0. These two parameters reflect the behavior of thermal conduction and electrical conduction, respectively. Finally, the correlation between the structural characteristics and the macroscopic properties was established by linear regression fitting. The results show that the cable ampacity is deeply subjected to the integrity of the XLPE molecular chains and the proportion of secondary crystals within the XLPE. The electrical property is deeply dominated by the crystallinity and lamellar thickness of the primary crystals within the XLPE.

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