Coupling VOF interfacial mass transfer model with RSM approach in LLE systems: Developing the new correlations for mass transfer, aspect ratio and terminal velocity

Roshdi, Sepideh; Kasiri, Norollah

doi:10.1016/j.icheatmasstransfer.2021.105216

Cited by 5 publications

(1 citation statement)

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“…Manifold learning is a unique machine learning method. Popular feature learning methods are divided into linear learning and nonlinear learning [22]. Its essence is that the sample points in the high-dimensional space are expanded into a manifold by a few main independent variables acting on the measurement space at the same time, and manifold learning is to pick up the low-dimensional manifolds embedded in the high-dimensional observation space.…”

Section: Reference Epidemic Cluster Analysismentioning

confidence: 99%

Vibration Signal Analysis of Roadheader Based on Referential Manifold Learning

Jiang,

Ji,

Yang

et al. 2023

Shock and Vibration

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Roadheader is important large equipment in coal mining. The roadheader has a higher failure rate due to its harsh working environment and high working intensity. In this paper, we proposed a fault diagnosis method based on reference manifold (RM) learning by using the vibration signals of roadheader in the actual production process. First, health and fault vibration signals were extracted from a large number of field data. The abovementioned signals were analyzed by time domain and wavelet packet energy analysis and got the characteristic parameters of the signal which can form the characteristic parameter sets. RM method can reduce the dimension of the characteristic parameters, and the projection of different characteristic parameters was obtained. Finally, the health parameters and fault parameters of different characteristic parameters were segmented by linear discriminant analysis (LDA). It could get the different segment area range of characteristic parameters for health signals and fault signals. This method provides a set of fault analysis ideas and methods for equipment working under complex working conditions and improves the theoretical basis for fault type analysis.

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Section: Reference Epidemic Cluster Analysismentioning

confidence: 99%