Prediction of remaining useful life is crucial for mechanical equipment operation and maintenance. It ensures safe equipment operation, reduces maintenance costs and economic losses, and promotes development. Most of the remaining useful life prediction studies focus on bearings, gearboxes, and engines; however, research on hydraulic pumps remains limited. This study focuses on gear pumps that are commonly used in the hydraulic field and develops a practical method of predicting remaining useful life. The deep sparse autoencoder is used to extract multi−dimensional features. Subsequently, the feature vectors are inputted to the support vector data description to calculate the machine degradation degree at the corresponding time and obtain the health indicator curve of the machine’s life cycle. In building the health state degradation curve, data are processed in an unsupervised manner to avoid the influence of artificial feature selection on the test. The method is validated on the pub... [more]

Coiled tubing (CT) is widely used for horizontal well fracturing, squeeze cementing, and sand and solid washing in the oil and gas industry. During CT operation, a gas−liquid two-phase flow state appears in the tubing. Due to the secondary flow, this state produces a more extensive flow-friction pressure loss, which limits its application. It is crucial to understand the gas−liquid flow behavior in a spiral tube for frictional pressure drop predictions in the CT technique. In this study, we numerically investigated the velocity distribution and phase distribution of a gas−liquid flow in CT. A comparison of experimental data and simulated results show that the maximum average error is 2.14%, verifying the accuracy of the numerical model. The gas and liquid velocities decrease first and then rise along the axial direction due to the effect of gravity. Due to the difference in the gas and liquid viscosity, i.e., the flow resistance of the gas and liquid is different, the gas−liquid slip v... [more]

Predicting the harvest time of wheat in large areas is important for guiding the scheduling of wheat combine harvesters and reducing losses during harvest. In this study, Zhumadian, Zhengzhou and Anyang, the main winter-wheat-producing areas in Henan province, were selected as the observation points, and the main producing areas were from south to north. Based on Landsat 8 satellite remote sensing images, the changes in NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), and NDWI (Normalized Difference Water Index) were analyzed at different growth stages of winter wheat in 2020. Multiple regression analysis and Back Propagation (BP) neural network machine learning methods were used to establish prediction models for the harvest time of winter wheat at different growth stages. The results showed that the prediction model based on a BP neural network had high accuracy. The RMSE, MAE and MAPE of the training set and the test set were 0.531 and 0.5947, 0.3001 a... [more]

Manufacturing and energy sectors provide vast amounts of maintenance data and information which can be used proactively for performance monitoring and prognostic analysis which lead to improve maintenance planning and scheduling activities. This leads to reduced unplanned shutdowns, maintenance costs and any fatal events that could affect the operations of the overall system. Performance and condition monitoring are among the most used strategies for prognostic and health management (PHM), in which different methods and techniques can be implemented to analyse maintenance and online data. Offshore wind turbines (WTs) are complex systems increasingly needing maintenance. This study proposes a performance monitoring system to monitor the performance of the WT power generation process by exploiting artificial neural networks (ANN) composed of different network designs and training algorithms, using simulated supervisory control and data acquisition (SCADA) data. The performance monitoring... [more]

The collision behavior and ice formation of a water droplet are affected by its falling process. In this paper, the two-phase flow of air and a water droplet at a specific temperature is adopted to investigate the processes of falling and freezing of a single water droplet. To track the air−water droplet interface and the temperature distribution, the level-set method and the non-isothermal flow coupling method are used, and the freezing model is added into the water’s control equations. The numerical results indicated that with the initial temperature at 283.15 K and the spherical shape, the water droplet changes to the shape of a straw hat at 293.15 K and a drum at 293.15 K but an oval face in freezing temperatures at 0.10 s. There is an obvious drop in the downward velocity when the water droplet falls in mild temperatures at 0.09 s. The downward velocity of the water droplet in air at sub-zero temperatures has a continuous increase during the time span from 0 s to 0.10 s. There is... [more]

In this study, the effect of temperature on the velocity and trajectory of a hot sphere falling in a water block was experimentally investigated. The sphere, 12 mm in diameter, was thrown through the water inside an enclosure at the ambient temperature by an electromagnetic attachment mechanism, and the particle velocity was recorded by a high-speed camera at 2000 fps. Then, using an image-processing algorithm, the real-time particle location was extracted and its velocity was measured. The results of the cold sphere falling test were compared with those obtained from the numerical solving by the governing equations. An electric heater was used to heat the sphere up to 100, 200, and 300 °C in order to investigate the effect of temperature on the sphere. The sphere was thrown upon reaching the desired temperature. By increasing the temperature, the sphere’s velocity was increased up to around 40% of the velocity of the cold sphere. Further, the sphere was thrown from a point in the vici... [more]

Every year, diverse types of safety accidents cause major damage to human life and property. In particular, failure to suppress safety accidents caused by fires during the early stages can lead to large-scale accidents, which in turn can cause more serious damage than other types of accident. Therefore, this paper presents an analysis of the prevailing research trends and future directions for research on preventing safety accidents due to fire. Since fire outbreaks can occur in many types of places, the study was conducted by selecting the places and causes involved in frequent fires, using fire data from Korea. As half of these fires were found to occur in buildings, this paper presents an analysis of the causes of building fires, and then focuses on three themes: fire prevention based on fire and gas detection; fire prevention in electrical appliances; and fire prevention for next-generation electricity. In the gas detection of the first theme, the gas referred to does not denote a... [more]

The influence mechanism of the blade tip clearance (TC) of an inducer on the performance of a centrifugal pump at high speed was researched under different flow rate conditions in this work. An experiment on the pump’s external performance was carried out, and numerical calculation was also performed under four different TCs. The full characteristic performance curves, static pressure and pressure pulsation distributions of the pump were obtained. Through the research and analysis, it was found that the influence of the TC on the efficiency and the head of the centrifugal pump are related to the flow rate. Under the influence of a large flow rate, the increase in the TC is helpful to improve the efficiency and the head of the pump. The increase in the TC helps to weaken the gap jet effect on the inducer. The inlet jet of the inducer, caused by TC leakage, will form a low-pressure vortex zone at the inlet of the inducer. The splitter-bladed inducer’s pressure pulsation is affected by th... [more]

In this paper, a new prediction model for accurately recognizing and appropriately evaluating the trends of domestic chemical products and for improving the forecasting accuracy of the chemical products’ prices is proposed. The proposed model uses the minimum forecasting error as the evaluation objective to forecast the settlement price. Active contracts for polyethylene and polypropylene futures on the Dalian Commodity Futures Exchange for the next five days were used, the data were divided into a training set and test set through normalization, and the time window, batch processing size, number of hidden layers, and rejection rate of a long short-term memory (LSTM) network were optimized by an improved genetic algorithm (IGA). In the experiments, with respect to the shortcomings of the genetic algorithm, the crossover location determination and some gene exchange methods in the crossover strategy were improved, and the predicted results of the IGA−LSTM model were compared with those... [more]

What is presented in this research is an intelligent system for detecting the volume percentage of three-phase fluids passing through oil pipes. The structure of the detection system consists of an X-ray tube, a Pyrex galss pipe, and two sodium iodide detectors. A three-phase fluid of water, gas, and oil has been simulated inside the pipe in two flow regimes, annular and stratified. Different volume percentages from 10 to 80% are considered for each phase. After producing and emitting X-rays from the source and passing through the pipe containing a three-phase fluid, the intensity of photons is recorded by two detectors. The simulation is introduced by a Monte Carlo N-Particle (MCNP) code. After the implementation of all flow regimes in different volume percentages, the signals recorded by the detectors were recorded and labeled. Three frequency characteristics and five wavelet transform characteristics were extracted from the received signals of each detector, which were collected in... [more]

Brain tumours are considered one of the deadliest tumours in humans and have a low survival rate due to their heterogeneous nature. Several types of benign and malignant brain tumours need to be diagnosed early to administer appropriate treatment. Magnetic resonance (MR) images provide details of the brain’s internal structure, which allow radiologists and doctors to diagnose brain tumours. However, MR images contain complex details that require highly qualified experts and a long time to analyse. Artificial intelligence techniques solve these challenges. This paper presents four proposed systems, each with more than one technology. These techniques vary between machine, deep and hybrid learning. The first system comprises artificial neural network (ANN) and feedforward neural network (FFNN) algorithms based on the hybrid features between local binary pattern (LBP), grey-level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT) algorithms. The second system comprises pre-t... [more]

The COVID-19 global pandemic is still affecting the world, even considering vaccine applications in most countries, especially due to new variant outbreaks and the possibility that they may present immunological escape. Therefore, mass testing is relevant in infection monitoring and restriction policy evaluations, making low-cost and easy-to-use tests essential. Serological tests might also be useful in monitoring immune response after vaccination. The present work proposes a less-expensive ELISA test route, using a scanner instead of a spectrophotometer and using the saturation of the image as a surrogate for the absorbance of each sample. Images from multiple experiments were selected and correlated with their spectrophotometric absorbance. ELISA plate images were digitized by a simple table scanner and, then, preprocessed using Hue, Saturation, Value (HSV) transformation, aiming to determine which correlates best with the obtained absorbance. Saturation correlated better with absorb... [more]

Transfer printing technology has developed rapidly in the last decades, offering a potential demand for 2-DOF rotation stages. In order to remove decoupling modeling, improve motion accuracy, and simplify the control method, the 2-DOF decoupled rotation stages based on compliant mechanisms present notable merits. Therefore, a novel 2-DOF decoupled rotation stage is synthesized of which the critical components of decoupling are the topological arrangement and a novel decoupled compound joint. To fully consider the undesired deformation of rigid segments, an FEA-based neural network model is utilized to predict the rotation strokes and corresponding coupling ratios, and optimize the structural parameters. Then, FEA simulations are conducted to investigate the static and dynamic performances of the proposed 2-DOF decoupled rotation stage. The results show larger rotation strokes of 4.302 mrad in one-axis actuation with a 1.697% coupling ratio, and 4.184 and 4.151 mrad in two-axis actuatio... [more]

The size distribution of coke is important in order to decide the burden layer structure and the burden porosity in the shaft of a blast furnace (BF), which fluctuates daily and can be determined by several parameters. It is measured two or three times per shift by screening the raw material. However, the screening method used is random and takes a lot of time and manpower, resulting in the susceptive size distribution of the raw material and delayed operation of the BF. Therefore, in this paper, a new online approach used to measure the size distribution of particles was selected through comparison. Four common algorithms were used to detect the coke particles from images, including the Marker-based Watershed (MW), Histogram of Oriented Gradient + Support Vector Machine (HOG + SVM), Faster Region-based Convolutional Neural Networks (Faster R-CNN), and You Only Look Once (YOLOv3). The results show that the MW and HOG + SVM were not suitable for coke image detection. The average mean av... [more]

In a wind tunnel process, Mach number is the most important parameter. However, it is difficult to measure directly, especially in the multimode operation process, leading to difficulty in process monitoring. Thus, it is necessary to measure the Mach number indirectly by utilizing data-driven methods, and based on which, to monitor the operation status of the wind tunnel process. In this paper, therefore, a multimode wind tunnel flow field system monitoring strategy is proposed. Since the wind tunnel system is a strongly nonlinear system, the kernel partial least squares method, which can efficiently handle the nonlinear regression problem, is utilized. Firstly, the Mach number is predicted utilizing the kernel partial least squares method. Secondly, process monitoring statistics, i.e., the Hotelling T2 statistic and the square prediction error, the SPE statistic, and their control limits, are proposed to be applied to monitor the wind tunnel process on the basis of the prediction of t... [more]

The current single gas prediction model is not sufficient for identifying and processing all the characteristics of mine gas concentration time series data. This paper proposes an ARIMA-LSTM combined forecasting model based on the autoregressive integrated moving average (ARIMA) model and the long short-term memory (LSTM) recurrent neural network. In the ARIMA-LSTM model, the ARIMA model is used to process the historical data of gas time series and obtain the corresponding linear prediction results and residual series. The LSTM model is used in further analysis of the residual series, predicting the nonlinear factors in the residual series. The prediction results of the combined model are compared separately with those of the two single models. Finally, RMSE, MAPE and R2 are used to evaluate the prediction accuracy of the three models. The results of the study show that the metrics of the combined ARIMA-LSTM model are R2 = 0.9825, MAPE = 0.0124 and RMSE = 0.083. The combined model has... [more]

The increased interest in greener sources of antioxidants has spurred the research on natural alternatives to enhance poultry production. This study aimed to investigate the effects of natural antioxidant extracts’ (hop β-acids extract) diet supplementation at different concentrations (0, 30, 60, and 120 mg kg−1) on the volatile compound profile of roasted chicken meat. A method based on headspace solid-phase micro-extraction coupled to gas chromatography-mass spectrometry (HS-SPME-GC-MS) was optimized by response surface design to extract the volatile compounds. The optimum extraction conditions were 80 °C and 45 min. A total of 95 volatile compounds were identified in roasted chicken meat, especially aldehydes, alkanes, alcohols, esters, and pyrazines. Principal component analysis (PCA) separated the samples as a function of β-acid supplementation, indicating that increased levels of supplementation lead to distinct volatile profiles in roasted chicken meat. Aldehydes such as octanal... [more]

Azobenzene-containing polymers (azo-polymers) have been a subject of extensive investigations during the last two and half decades, due to their remarkable ability to undergo pronounced alignment and deformation under irradiation with light. The molecular ordering and deformation in azo-polymers of various structures under irradiation with linearly polarized light was described in a series of theoretical works, based on the effect of the reorientation of azobenzene moieties due to the anisotropic character of the photoisomerization processes. In the present study, we generalize the previous orientation approach to describe the photo-alignment and deformation of azo-polymer networks under irradiation with elliptically polarized light. We demonstrate that, in general, the light-induced ordering and deformation have a biaxial symmetry defined by the polarization ellipse. Azobenzene chromophores have a tendency to align along the direction of light propagation, the orientation in the other... [more]

Axial flow fans are commonly used for a system or machinery cooling process. It also used for ventilating warehouses, factories, and garages. In the fan manufacturing industry, the demand for varying fan operating points makes design parameters complicated because many design parameters affect the fan performance. This study combines the deep neural network (DNN) with a genetic algorithm (GA) for axial flow design and development. The characteristic fan curve (P-Q Curve) can be generated when the relevant fan parameters are imported into this system. The system parameters can be adjusted to achieve the required characteristic curve. After the wind tunnel test is performed for verification, the data are integrated and corrected to reduce manufacturing costs and design time. This study discusses a small axial flow fan NACA and analyzes fan features, such as the blade root chord length, blade tip chord length, pitch angle, twist angle, fan diameter, and blade number. Afterwards, the wind... [more]

Multi-barreled composite foundations are generally used in offshore oil platform structure. However, there is still a lack of theoretical analyses and experimental research. This paper presents the results of a three-dimensional finite element analysis of a four-barreled suction pile foundation in heterogeneous clay foundation. The pile group effect and carrying capacity are numerically simulated. The effects of different pile embedment depths, pile spacings and non-uniformity coefficients of clay on the pile group effect are studied. Considering the changes in the foundation carrying capacity under vertical, horizontal and bending moment coupling loads, the foundation carrying capacity envelopes under horizontal and moment (H-M) and vertical, horizontal and moment (V-H-M) loading modes are drawn. The results show that pile spacing and embedment depth have great influence on the pile group effect. The bearing capacity envelope of foundations under V-H-M loading mode is greatly affected... [more]

Dengue hemorrhagic fever (DHF) is one of the most widespread and deadly diseases in several parts of Indonesia. An accurate forecast-based model is required to reduce the incidence rate of this disease. Time-series methods such as autoregressive integrated moving average (ARIMA) models are used in epidemiology as statistical tools to study and forecast DHF and other infectious diseases. The present study attempted to forecast the monthly confirmed DHF cases via a time-series approach. The ARIMA, seasonal ARIMA (SARIMA), and long short-term memory (LSTM) models were compared to select the most accurate forecasting method for the deadly disease. The data were obtained from the Surabaya Health Office covering January 2014 to December 2016. The data were partitioned into the training and testing sets. The best forecasting model was selected based on the lowest values of accuracy metrics such as the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error... [more]

The rising use of renewable energy sources, particularly those that are weather-dependent like wind and solar energy, has increased the uncertainty of supply in these power systems. In order to obtain considerably more accurate results in the analysis of power systems, such as in the planning and operation, it is necessary to tackle the stochastic nature of these sources. Operators require adequate techniques and procedures to mitigate the negative consequences of the stochastic behavior of renewable energy generators. Thus, this paper presents a modification of the original probability distribution functions (PDFs) where the original PDFs are insufficient for wind speed and solar irradiance modeling because they have a significant error between the real data frequency distribution and the estimated distribution curve. This modification is using a mixture of probability distributions, which can improve the fitting of data and reduce this error. The main aim of this paper is to model wi... [more]

As a non-explosive low-disturbance rock breaking technology, carbon dioxide phase transition blasting (CDPTB) is widely used in rock breaking projects such as pressure relief and permeability enhancement in coal mines, open-pit mining, road subgrade excavation, foundation pit excavation, etc. In this paper, the principle and equipment of CDPTB are systematically analyzed, and the characteristics of a reusable fracturing tube and disposable fracturing tube are determined. Different energy calculation methods are analyzed to determine the magnitude or equivalent explosive equivalent of CDPTB. According to the characteristics of impact stress wave and high-pressure gas, the cracking mechanism of CDPTB is proposed. Under the action of medium-impact stress, rock mass will produce multi-point cracking, and high-pressure gas will produce a gas wedge effect in the initial fracture, which determines the comprehensive action path of the stress wave and high-pressure gas. In terms of fracture cha... [more]

Fault Detection and Isolation (FDI) methodology focuses on maintaining safe and reliable operating conditions within industrial practices which is of crucial importance for the profitability of technologies. In this work, the development of an FDI algorithm based on the use of dynamic principal component analysis (DPCA) and the Fréchet distance δdF metric is explored. The three-tank benchmark problem is studied and utilized to demonstrate the performance of the FDI method for six fault types. A DPCA transformation for the system was established, and fault detection was conducted based on the Q statistic. Fault isolation is also of critical importance for proper intervention to mitigate fault effects. To identify the type of detected faults, the fault responses within the PC subspace were analyzed using the δdF metric. The use of the Fréchet distance metric for the isolation of faults combined with DPCA for feature extraction is a novel technique to the best of the authors’ knowledge th... [more]

In a laterally asymmetric intake pumping station, the flow direction in the forebay is not consistent with flow in the intake channel. Thus, the adverse flow patterns, such as bias flow, large-scale vortex and asymmetric flow occur frequently in the forebay and sump. Based on the Reynolds-averaged Navier-Stokes (RANS) equation and the RNG k-ε turbulence model, a recent flow pattern in a laterally asymmetric intake pumping station was numerically simulated and analyzed, and effective vortex elimination measures were proposed. For the original scheme, seriously biased flow combined with large-scale vortices were observed in the forebay and several vortices occurred in the sump. To suppress the clash inflow in the south and north intake channel, the “straight diversion pier + curved wing wall” and “straight diversion pier + curved wing wall + V-shaped diversion pier” were installed separately. The” symmetrical 川-shaped diversion pier” and “symmetrical 川-shaped diversion pier + circular co... [more]