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The purpose of this paper is to predict quantitative level of stray current leaking to the buried metallic structure by establishing convolution neural network (CNN) model.

First, corrosion experimental system of buried metallic structure is established. The research object of this paper is the polarization potential within 110 min, CNN model is used to predict the quantitative level of stray current leakage using the data from corrosion experimental system further. Finally, results are compared with the method using BP neural network.

Results show that the CNN model has better predictive effect and shorter prediction time than the BP model, the accuracy of which is 82.5507 per cent, and the prediction time is shortened by more than 10 times.

The established model can be used to forecast the level of stray current leakage in the subway system effectively, which provides a new theoretical basis for evaluating the stray current corrosion hazard of buried metallic structure.

At present, one of the key equipment in pillar industries is a large rotating machinery. Conducting regular health monitoring is important for ensuring safe operation of the large rotating machinery. Because vibrations sensors play an important role in the workings of the rotating machinery, measuring its vibration signal is an important task in health monitoring. This paper aims to present these.

In this work, the contact vibration sensor and the non-contact vibration sensor have been discussed. These sensors consist of two types: the electric vibration sensor and the optical fiber vibration sensor. Their applications in the large rotating machinery for the purpose of health monitoring are summarized, and their advantages and disadvantages are also presented.

Compared with the electric vibration sensor, the optical fiber vibration sensor of large rotating machinery has unique advantages in health monitoring, such as provision of immunity against electromagnetic interference, requirement of less insulation and provision of long-distance signal transmission.

Both contact vibration sensor and non-contact vibration sensor have been discussed. Among them, the electric vibration sensor and the optical fiber vibration sensor are compared. Future research direction of the vibration sensors is presented.

This paper aims to provide the proper preset temperatures of the convection reflow oven when reflowing a printed circuit board (PCB) assembly with varied sizes of components simultaneously.

In this study, computational fluid dynamics modeling is used to simulate the reflow soldering process. The training data provided to the machine learning (ML) model is generated from a programmed system based on the physics model. Support vector regression and an artificial neural network are used to validate the accuracy of ML models.

Integrated physical and ML models synergistically can accurately predict reflow profiles of solder joints and alleviate the expense of repeated trials. Using this system, the reflow oven temperature settings to achieve the desired reflow profile can be obtained at substantially reduced computation cost.

The prediction of the reflow profile subjected to varied temperature settings of the reflow oven is beneficial to process engineers when reflowing bulky components. The study of reflowing a new PCB assembly can be started at the early stage of board design with no need for a physical profiling board prototype.

This study provides a smart solution to determine the optimal preset temperatures of the reflow oven, which is usually relied on experience. The hybrid physics–ML model providing accurate prediction with the significantly reduced expense is used in this application for the first time.

The purpose of this study is to explore the mechanisms underlying the transformation of records management (RM) to digital processes in the context of electronic records management systems (ERMS). The aim is to facilitate the evaluation of the long-term performance of ERMS and the effectiveness of the current standards.

Qualitative methods, such as participant observation and the constructivist grounded theory, were applied on a case of ERMS implementation in the Chinese public sector.

The results revealed that the application of transition-oriented ERMS would stimulate restructuring in the RM pattern and expectation on the functions of ERMS, with information quality underlying as a key challenging factor. The above-stated factors together drive the digital transformation of RM. A model for this mechanism is provided in the present study.

The selected case serves as an example for the cases that are not conditional on enforcing the electronic documents and RMS. As preliminary research, only one case has been studied here. However, it is possible to conduct other case studies to develop a further understanding of the transformation process.

The novelty of the present study is that it draws attention to the challenges encountered in moving RM towards digital transformation, by providing a theoretical foundation for developing sustainable evaluations of the ERMS and the associated current standards.

This paper aims to study the component pick-and-place (P&P) defect patterns for different root causes based on automated optical inspection data and develop a root cause identification model using machine learning.

This study conducts experiments to simulate the P&P machine errors including nozzle size and nozzle pick-up position. The component placement qualities with different errors are inspected. This study uses various machine learning methods to develop a root cause identification model based on the inspection result.

The experimental results revealed that the wrong nozzle size could increase the mean and the standard deviation of component placement offset and the probability of component drop during the transfer process. Moreover, nozzle pick-up position can affect the rotated component placement offset. These root causes of defects can be traced back using machine learning methods.

This study provides operators in surface mount technology assembly lines to understand the P&P machine error symptoms. The developed model can trace back the root causes of defects automatically in real line production.

The findings are expected to lead the regular preventive maintenance to data-driven predictive and reactive maintenance.

The purpose of this paper is to investigate oil-gas slug formation in horizontal straight pipe and its associated pressure gradient, slug liquid holdup and slug frequency.

The abrupt change in gas/liquid velocities, which causes transition of flow patterns, was analyzed using incompressible volume of fluid method to capture the dynamic gas-liquid interface. The validity of present model and its methodology was validated using Baker’s flow regime chart for 3.15 inches diameter horizontal pipe and with existing experimental data to ensure its correctness.

The present paper proposes simplified correlations for liquid holdup and slug frequency by comparison with numerous existing models. The paper also identified correlations that can be used in operational oil and gas industry and several outlier models that may not be applicable.

The correlation may be limited to the range of material properties used in this paper.

Numerically derived liquid holdup and holdup frequency agreed reasonably with the experimentally derived correlations.

The models could be used to design pipeline and piping systems for oil and gas production.

The paper simulated all the seven flow regimes with superior results compared to existing methodology. New correlations derived numerically are compared to published experimental correlations to understand the difference between models.