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Deposition is found in many engineering processes, such as the asphaltene deposition in oil pipelines/wellbores, and biological and chemical foulings in pipes or heat exchangers. These deposition processes usually occur in a two-phase flow environment. This study develops a model for two-phase flow with deposition in vertical pipes. The model consists of three modules: Fluid Transport, Particle Transport,...
Radiomics, an emerging field of quantitative imaging, encompasses a broad class of analytical techniques. Recent literature have interrogated associations between quantitatively derived GLCM-based texture features and clinical/pathology information using machine learning algorithms in many cancer settings, but often fail to elucidate the predictive power of these features. Moreover, for many cancers...
A research approach of crack detection of rotating shafts based on acoustic emission (AE) signals and machine learning is proposed in this paper. The relationship between crack intensity and domain features are investigated, and the features which could well indicate the crack condition are selected for modelling and crack prediction. Multiple Linear Regression (MLR), Artificial Neural Networks (ANN)...
The growing interest in predictive maintenance makes industrials and researchers turning themselves to artificial intelligence methods for fulfilling the tasks of condition monitoring and prognostics. Within this frame, the general purpose of this paper is to investigate the capabilities of an Evolving extended Takagi Sugeno (exTS) based neuro-fuzzy algorithm to predict the tool condition in high-speed...
In this paper, two temporal models, Hidden Markov Model and Auto Regressive Moving Average model with exogenous inputs (ARMAX), are used for health condition monitoring of the cutter in a milling machine. Dataset is acquired through real time force signal sensing. A heuristic statistical approach is used to select dominant features, leading to the selection of 3 dominant features from the 16-dimensional...
The effective approach to monitor the health state of equipment has long been a concern of industrial applications. This paper focuses on the categorical data analysis to build equipment degradation model for predicting equipment failure and monitoring the health state of equipment. Since large volumes of categorical data are generated and collected in most manufacturing execution systems, how to...
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