Development of suction pipe design criterion to secure oil return to compressor

In the present work, phenomena associated with counter current flow limitation (CCFL) were experimentally investigated using small diameter tubes in order to suggest criterion for which the oil return is secured. The test section is made of Pyrex glass tube to allow visual observation. The inner diameter of the test tube is 7mm and the height is 1m. The inclination of test tubes varied from vertical to crank type with various horizontal lengths. Waterair flow and lubricant oil-air flow were examined through a series of experiment at various liquid flow rates. In this experimental study, flow reversal and flooding phenomena were visually observed and two-phase flow rate were measured. Flow reversal point represents the air flow rate when the liquid film begins to flow downwards in the tube below the liquid inlet location. Whole supplied liquid flows upward when the gas flow rate is larger than this value. So the flow reversal point can be interpreted as oil return criterion and the flow reversal points were measured using various shape of test section in a wide range of liquid flow rate. The gas velocities for the flow reversal point appeared to be similar over a certain range of liquid flow rate. Flooding point was defined as the air flow rate when liquid starts to flow above the liquid inlet part. The air flow rate needed to cause flooding is inversely proportional to the liquid flow rate. Both flow reversal and flooding velocity also depend on the inclination angle, horizontal length and liquid property

Multi-Stage Approach Using Convolutional Triplet Network and Ensemble Model for Fault Diagnosis in Oil Plant Rotary Machines

Ensuring the operational safety and reliability of rotary machinery systems, especially in oil plants, has become a focal point in both academic and industry arenas. Specifically, in terms of key rotary machinery components such as shafts, the diagnosis of these systems is paramount for achieving enhanced generalization capabilities in fault diagnosis, encompassing multiple sensor-derived variables with their respective fault patterns. This study introduces a multi-stage approach to generalize capabilities for fault diagnosis that considers multiple sensor-derived variables and their fault patterns. This method combines the Convolutional Triplet Network for feature extraction with an ensemble model for fault classification. Initially, vibration signals are processed to yield the most representative temporal and spatial features. Then, an ensemble approach is used to maximize both diversity and accuracy by balancing the contributions of the individual classifiers. The approach can detect three representative types of shaft faults more accurately than traditional single-stage machine learning models. Comprehensive experiments, detailed within, showcase the method’s efficacy in diagnosing rotary machine faults across diverse operational scenarios

Machine Learning-Based Slope Failure Prediction Model Considering the Uncertainty of Prediction

Slope failure is a severe natural disaster that can cause property damage and human costs. In order to develop a warning system for slope failure, various studies have been conducted, including research based on both physics-based models and machine learning-based models. While machine learning-based approaches have shown promise due to their ability to automatically extract hidden patterns in data, conventional machine learning models have their limitations. Specifically, while they can always provide a prediction value, they fail to provide information about the uncertainty of the prediction results. In this study, we developed a machine learning model that can predict the slope failure by training trends in time-series data. Our proposed model addresses the limitations of the conventional machine learning models by incorporating the Monte Carlo dropout to calculate the uncertainty during the prediction stage. The experimental results demonstrated that the proposed model significantly outperforms the conventional machine learning models in terms of both its prediction accuracy and the ability to estimate uncertainty. Furthermore, the model proposed in this study can support decision-makers by providing more accurate information than the conventional models

A Comparative Analysis of Slope Failure Prediction Using a Statistical and Machine Learning Approach on Displacement Data: Introducing a Tailored Performance Metric

Slope failures pose significant threats to human safety and vital infrastructure. The urgent need for the accurate prediction of these geotechnical events is driven by two main goals: advancing our understanding of the underlying geophysical mechanisms and establishing efficient evacuation protocols. Although traditional physics-based models offer in-depth insights, their reliance on numerous assumptions and parameters limits their practical usability. In our study, we constructed an experimental artificial slope and monitored it until failure, generating an in-depth displacement dataset. Leveraging this dataset, we developed and compared prediction models rooted in both statistical and machine learning paradigms. Furthermore, to bridge the gap between generic evaluation metrics and the specific needs of slope failure prediction, we introduced a bespoke performance. Our results indicate that while the statistical approach did not effectively provide early warnings, the machine learning models, when assessed with our bespoke performance metric, showed significant promise as reliable early warning systems. These findings hold potential to fortify disaster prevention measures and prioritize human safety

Deep Regression Prediction of Rheological Properties of SIS-Modified Asphalt Binders

The engineering properties of asphalt binders depend on the types and amounts of additives. However, measuring engineering properties is time-consuming, requires technical expertise, specialized equipment, and effort. This study develops a deep regression model for predicting the engineering property of asphalt binders based on analysis of atomic force microscopy (AFM) image analysis to test the feasibility of replacing traditional measuring estimate techniques. The base asphalt binder PG 64-22 and styrene–isoprene–styrene (SIS) modifier were blended with four different polymer additive contents (0%, 5%, 10%, and 15%) and then tested with a dynamic shear rheometer (DSR) to evaluate the rheological data, which indicate the rutting properties of the asphalt binders. Different deep regression models are trained for predicting engineering property using AFM images of SIS binders. The mean absolute percentage error is decisive for the selection of the best deep regression architecture. This study’s results indicate the deep regression architecture is found to be effective in predicting the G*/sin δ value after the training and validation process. The deep regression model can be an alternative way to measure the asphalt binder’s engineering property quickly. This study would encourage applying a deep regression model for predicting the engineering properties of the asphalt binder

Conceptual Development Process of Mass-customizable Data Analytics Services for Manufacturing SMEs

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RNAseq-based Transcriptome Analysis of Burkholderia glumae Quorum Sensing

Burkholderia glumae causes rice grain rot and sheath rot by producing toxoflavin, the expression of which is regulated by quorum sensing (QS). The QS systems of B. glumae rely on N-octanoyl homoserine lactone, synthesized by TofI and its cognate receptor TofR, to activate the genes for toxoflavin biosynthesis and an IclR-type transcriptional regulator gene, qsmR. To understand genome-wide transcriptional profiling of QS signaling, we employed RNAseq of the wild-type B. glumae BGR1 with QS-defective mutant, BGS2 (BGR1 tofI::Ω) and QS-dependent transcriptional regulator mutant, BGS9 (BGR1 qsmR::Ω). A comparison of gene expression profiling among the wild-type BGR1 and the two mutants before and after QS onset as well as gene ontology (GO) enrichment analysis from differential expressed genes (DEGs) revealed that genes involved in motility were highly enriched in TofI-dependent DEGs, whereas genes for transport and DNA polymerase were highly enriched in QsmR-dependent DEGs. Further, a combination of pathways with these DEGs and phenotype analysis of mutants pointed to a couple of metabolic processes, which are dependent on QS in B. glumae, that were directly or indirectly related with bacterial motility. The consistency of observed bacterial phenotypes with GOs or metabolic pathways in QS-regulated genes implied that integration RNAseq with GO enrichment or pathways would be useful to study bacterial physiology and phenotypes

Feasibility research on development of product-independent assembly complexity model

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