Records with Subject: Process Monitoring
Showing records 1 to 25 of 316. [First] Page: 1 2 3 4 5 Last
Semantic Hybrid Signal Temporal Logic Learning-Based Data-Driven Anomaly Detection in the Textile Process
Xu Huo, Kuangrong Hao
February 19, 2024 (v1)
Keywords: anomaly detection, temporal logic, textile process, time-series data
The development of sensor networks allows for easier time series data acquisition in industrial production. Due to the redundancy and rapidity of industrial time series data, accurate anomaly detection is a complex and important problem for the efficient production of the textile process. This paper proposed a semantic inference method for anomaly detection by constructing the formal specifications of anomaly data, which can effectively detect exceptions in process industrial operations. Furthermore, our method provides a semantic interpretation of exception data. Hybrid signal temporal logic (HSTL) was proposed to improve the insufficient expressive ability of signal temporal logic (STL) systems. The epistemic formal specifications of fault offline were determined, and a data-driven semantic anomaly detector (SeAD) was constructed, which can be used for online anomaly detection, helping people understand the causes and effects of anomalies. Our proposed method was applied to time-seri... [more]
A Full-State Reliability Analysis Method for Remanufactured Machine Tools Based on Meta Action and a Markov Chain Using an Exercise Machine (EM) as an Example
Yueping Luo, Yongmao Xiao
February 19, 2024 (v1)
Keywords: EM, full state, MA unit (MAU), Markov chain, reliability analysis, RMT
The reliability of an RMT can be regarded as an important indicator customers can use to recognize its quality; however, it is difficult to implement a full-state reliability analysis of an RMT due to its complicated structural functions. Therefore, a full-state reliability analysis model is proposed herein based on meta action (MA) and a Markov chain for remanufactured exercise machine tools (REMTs). First, an analysis was carried out on individual levels by integrating the MAU decomposition method, and an MAU fault tree model was established layer by layer for the REMT. Second, full-state modeling was performed in view of the MAU characteristics of the REMT, whose operation processes are divided into MAU normal and failure states. A Markov decision-making process was introduced to integrate MAU states and establish our model, which was solved by means of an analytical method for the evaluation of reliability. Finally, an example of a remanufactured machine tool spindle is given to ve... [more]
Model-Based Condition Monitoring of Modular Process Plants
Philipp Wetterich, Maximilian M. G. Kuhr, Peter F. Pelz
February 10, 2024 (v1)
Keywords: condition monitoring, fault diagnosis, modularization, soft sensors
The process industry is confronted with rising demands for flexibility and efficiency. One way to achieve this is modular process plants, which consist of pre-manufactured modules with their own decentralized intelligence. Plants are then composed of these modules as unchangeable building blocks and can be easily re-configured for different products. Condition monitoring of such plants is necessary, but the available solutions are not applicable. The authors of this paper suggest an approach in which model-based symptoms are derived from a few measurements and observers that are based on the manufacturer’s knowledge. The comparisons of redundant observers lead to residuals that are classified to obtain symptoms. These symptoms can be communicated to the plant control and are inputs to an easily adaptable diagnosis. The implementation and validation at a modular mixing plant showcase the feasibility and potential of this approach.
Risk Assessment of Coal Mine Gas Explosion Based on Fault Tree Analysis and Fuzzy Polymorphic Bayesian Network: A Case Study of Wangzhuang Coal Mine
Jinhui Yang, Jin Zhao, Liangshan Shao
January 12, 2024 (v1)
Keywords: coal mine gas explosion, fault tree analysis, fuzzy theory, polymorphic Bayesian network, risk assessment
The prevention and control of gas explosion accidents are important means to improving the level of coal mine safety, and risk assessment has a positive effect on eliminating the risk of gas explosions. Aiming at the shortcomings of current risk assessment methods in dynamic control, state expression and handling uncertainty, this study proposes a method combining fault tree analysis and fuzzy polymorphic Bayesian networks. The risk factors are divided into multiple states, the concept of accuracy is proposed to correct the subjectivity of fuzzy theory and Bayesian networks are relied on to calculate the risk probability and risk distribution in real time and to propose targeted prevention and control measures. The results show that the current risk probability of a gas explosion accident in Wangzhuang coal mine is as high as 35%, and among the risk factors, excessive ventilation resistance and spontaneous combustion of coal are sources of induced risk, and the sensitivity value of ele... [more]
A Sewer Pipeline Defect Detection Method Based on Improved YOLOv5
Tong Wang, Yuhang Li, Yidi Zhai, Weihua Wang, Rongjie Huang
January 12, 2024 (v1)
Keywords: attention mechanism, detection of sewer defects, GSConv, improved YOLOv5, involution, knowledge distillation
To address the issues of strong subjectivity, low efficiency, and difficulty in on-site model deployment encountered in existing CCTV defect detection of pipelines, this article proposes an object detection model based on an improved YOLOv5s algorithm. Firstly, involution modules and GSConv simplified models are introduced into the backbone network and feature fusion network, respectively, to enhance the detection accuracy. Secondly, a CBAM attention mechanism is integrated to improve the detection accuracy of overlapping targets in complex backgrounds. Finally, knowledge distillation is performed on the improved model to further enhance its accuracy. Experimental results demonstrate that the improved YOLOv5s achieved an mAP@0.5 of 80.5%, which is a 2.4% increase over the baseline, and reduces the parameter and computation volume by 30.1% and 29.4%, respectively, with a detection speed of 75 FPS. This method offers good detection accuracy and robustness while ensuring real-time detecti... [more]
Detection of Large Foreign Objects on Coal Mine Belt Conveyor Based on Improved
Kaifeng Huang, Shiyan Li, Feng Cai, Ruihong Zhou
January 5, 2024 (v1)
Keywords: belt conveyor, deep separable convolution, large foreign object recognition, MSAM, YOLOv5
An algorithm based on the YOLOv5 model is proposed to address safety incidents such as tearing and blockage at transfer points on belt conveyors in coal mines caused by foreign objects mixed in with the coal flow. Given the tough underground conditions and images acquired with low quality, recursive filtering and MSRCR image enhancement algorithms were utilized to preprocess the dynamic images collected by underground monitoring devices, substantially enhancing image quality. The YOLOv5 model has been improved by introducing a multi-scale attention module (MSAM) during the channel map slicing, thereby increasing the model’s resistance to interference from redundant image features. Deep separable convolution was utilized in place of conventional convolution to detect, identify, and process large foreign objects on the belt conveyor as well as to increase detection speed. The MSAM-YOLOv5 model was trained before being installed on the NVIDIA Jetson Xavier NX platform and utilized to iden... [more]
A Hybrid Cluster Variational Autoencoder Model for Monitoring the Multimode Blast Furnace System
Chenyu Chen, Jinhui Cai
November 30, 2023 (v1)
Keywords: Gaussian mixture model, multimode blast furnace system, process monitoring, variational autoencoder
Efficient monitoring of the blast furnace system is crucial for maintaining high production efficiency and ensuring product quality. This article introduces a hybrid cluster variational autoencoder model for monitoring the blast furnace ironmaking process which exhibits multimode behaviors. In contrast to traditional approaches, this method utilizes neural networks to learn data features and effectively handles the diverse feature types observed in different production modes. Through the utilization of a clustering process within the hidden layer of the variational autoencoder, the proposed technique facilitates efficient fault detection in the context of multimodal blast furnace data. Based on the variational autoencoder model, this study further establishes a unified monitoring index and defines a method for computing the control limits. The application of the model to real blast furnace data reveals its proficiency in accurately identifying faults across diverse modes; compared with... [more]
Liquid−Liquid Two-Phase Flow and Size Prediction of Slug Droplets in Microchannels
Wei Du, Yingfeng Duan, Lina Wang, Dayu Liu
September 21, 2023 (v1)
Keywords: downstream orifice, droplet, microchannels, size prediction, two-phase flow
The liquid−liquid two-phase flow and size prediction of slug droplets in flow-focusing microchannels with different downstream orifice sizes were investigated experimentally. Aqueous solution of 50%-glycerol and mineral oil with 4 wt.% surfactant sorbitanlauric acid ester (Span 20) were used as the dispersed and continuous phases, respectively. Three characteristic flow patterns were identified: slug flow, dripping flow, and jetting flow. The slug flow region decreased but the jetting flow region increased with the decrease in the size of the channel orifice. Afterwards, the universal flow pattern maps of the liquid−liquid two-phase in three microchannels were obtained based on dimensionless analysis. Furthermore, two slug droplet formation regions were found: when φ−1Cac < 0.01, the droplet formation was mainly driven by the squeezing force Fp, while when φ−1Cac > 0.01, both the squeezing force Fp and shear force Fτ contributed to droplet formation. Additionally, the prediction correl... [more]
A Model Based on the Random Forest Algorithm That Predicts the Total Oil−Water Two-Phase Flow Rate in Horizontal Shale Oil Wells
Huimin Zhou, Junfeng Liu, Jiegao Fei, Shoubo Shi
September 20, 2023 (v1)
Keywords: flow rate prediction, horizontal well, multi-location local flow velocity, oil–water two-phase, random forest algorithm
Due to variables like wellbore deviation variation and flow rate, the local flow velocity in the output wellbore of horizontal shale oil wells varied significantly at various points in the wellbore cross-section, making it challenging to calculate the total single-layer production with accuracy. The oil−water two-phase flow rate calculation techniques for horizontal wells developed based on particular flow patterns and array spinners had excellent applicability in their respective niches but suffered from poor generalizability and demanding experience levels for logging interpreters. In this study, we employed five spinners in a triangular walled array instrument to create the multi-decision tree after figuring out how many leaf nodes there were and examining the defining characteristics of the observed values gathered under various experimental setups. The construction of the entire oil−water two-phase flow prediction model was made possible when the random forest regression approach... [more]
Research and Test on the Device of Downhole Near-Bit Temperature and Pressure Measurement While Drilling
Ming Lu, Hualin Liao, Huajian Wang, Yuhang He, Jiansheng Liu, Yifan Wang, Wenlong Niu
September 20, 2023 (v1)
Keywords: measurement principle, monitoring while drilling, pressure, sensor, temperature
The accurate acquisition of downhole engineering parameters, such as real-time pressure and temperature measurements, plays a crucial role in mitigating drilling risks and preventing accidents. In this study, we present the design of a real-time data acquisition and transmission system for drilling operations. The system utilizes a near-bit measurement method to simultaneously measure downhole parameters, including mud pressure and temperature. By analyzing the pressure and temperature frequencies obtained from a quartz crystal pressure gauge and compensating for temperature effects, accurate pressure values are obtained. The resistance value of a PT1000 sensor is measured, and a second-order fitting is performed using laboratory scale coefficients to determine the temperature values. The data acquisition system employs an advanced microcontroller as the main control chip, along with an A/D conversion chip. Additionally, signal amplification, data storage modules, data transmission mod... [more]
Automatic Electrical System Fault Diagnosis Using a Fuzzy Inference System and Wavelet Transform
Yong Zhang, Guangjun He, Guangjian Li
September 20, 2023 (v1)
Keywords: electrical signal, fault diagnosis, fuzzy inference, power distribution, wavelet transform
Electrical systems consist of varied components that are used for power distribution, supply, and transfer. During transmission, component failures occur as a result of signal interruptions and peak utilization. Therefore, fault diagnosis should be performed to prevent fluctuations in the power distribution. This article proposes a fluctuation-reducing fault diagnosis method (FRFDM) for use in power distribution networks. The designed method employs fuzzy linear inferences to identify fluctuations in electrical signals that occur due to peak load demand and signal interruptions. The fuzzy process identifies the fluctuations in electrical signals that occur during distribution intervals. The linear relationship between two peak wavelets throughout the intervals are verified across successive distribution phases. In this paper, non-recurrent validation for these fluctuations is considered based on the limits found between the power drop and failure. This modification is used for preventi... [more]
EW-YOLOv7: A Lightweight and Effective Detection Model for Small Defects in Electrowetting Display
Zihan Zheng, Ningxia Chen, Jianhao Wu, Zhixuan Xv, Shuangyin Liu, Zhijie Luo
September 20, 2023 (v1)
Keywords: detection model, electrowetting display, GhostNetV2, small defects, YOLOv7
In order to overcome the shortcomings of existing electrowetting display defect detection models in terms of computational complexity, structural complexity, detection speed, and detection accuracy, this article proposes an improved YOLOv7-based electrowetting display defect detection model. The model effectively optimizes the detection performance of display defects, especially small target defects, by integrating GhostNetV2 modules, Acmix attention mechanisms, and NGWD (Normalized Gaussian Wasserstein Distance) Loss. At the same time, it reduces the parameter size of the network model and improves the inference efficiency of the network. This article evaluates the performance of an improved model using a self-constructed electrowetting display defect dataset. The experimental results show that the proposed improved model achieves an average detection rate (mAP) of 89.5% and an average inference time of 35.9 ms. Compared to the original network, the number of parameters and computatio... [more]
Integrating TPM and Industry 4.0 to Increase the Availability of Industrial Assets: A Case Study on a Conveyor Belt
David Mendes, Pedro D. Gaspar, Fernando Charrua-Santos, Helena Navas
August 3, 2023 (v1)
Keywords: case study, conveyor belt, Industry 4.0 (I4.0), Internet of Things (IoT), lean philosophy, maintenance, maintenance management, process optimization, real-time monitoring, total productive maintenance (TPM)
As the global market becomes increasingly competitive and demanding, companies face the challenge of responding swiftly and efficiently to customer needs. To maintain a competitive advantage, organisations must optimise the usage of their assets. This study focuses on the critical role of maintenance management and presents a novel, cost-effective, and easily applicable model that integrates Industry 4.0 (I4.0) and Total Productive Maintenance (TPM) principles to enhance production processes. The proposed model incorporates a real-time monitoring system equipped with sensors, a gateway, and Internet of Things (IoT) services. These components enable data acquisition, transmission, storage, and visualisation through both mobile and fixed devices. The model’s effectiveness was validated through its implementation on a conveyor belt in a feed mill. The availability of the conveyor belt was around 89.5% before TPM implementation. After the implementation of TPM, it was possible to observe t... [more]
A Novel Dynamic Process Monitoring Algorithm: Dynamic Orthonormal Subspace Analysis
Weichen Hao, Shan Lu, Zhijiang Lou, Yonghui Wang, Xin Jin, Syamsunur Deprizon
August 2, 2023 (v1)
Keywords: dynamic process, key performance indicators, orthonormal subspace analysis, process monitoring
Orthonormal subspace analysis (OSA) is proposed for handling the subspace decomposition issue and the principal component selection issue in traditional key performance indicator (KPI)-related process monitoring methods such as partial least squares (PLS) and canonical correlation analysis (CCA). However, it is not appropriate to apply the static OSA algorithm to a dynamic process since OSA pays no attention to the auto-correlation relationships in variables. Therefore, a novel dynamic OSA (DOSA) algorithm is proposed to capture the auto-correlative behavior of process variables on the basis of monitoring KPIs accurately. This study also discusses whether it is necessary to expand the dimension of both the process variables matrix and the KPI matrix in DOSA. The test results in a mathematical model and the Tennessee Eastman (TE) process show that DOSA can address the dynamic issue and retain the advantages of OSA.
Failure Characterization and Analysis of a Sport Utility Vehicles SUV Rear Door Damper Made by Nylon as Structural Element
Jorge Cruz-Salinas, Pedro Jacinto Paramo-Kañetas, Gonzalo Valdovinos-Chacón, Néstor Efrén Méndez Lozano, Marco Antonio Zamora-Antuñano, Sergio Arturo Gama-Lara
August 2, 2023 (v1)
Keywords: biodegradation, car rear door damper, crystallinity degree, failure analysis, thermal characterization
In this investigation, an automotive component made of nylon as a structural element was studied by several characterization techniques to identify material properties. Firstly, a Fourier transform infrared spectroscopy (FTIR) was carried out to obtain information about composition, then, differential scanning calorimetry (DSC) was used to extract useful information on sample thermal behavior. The humidity and volatile materials percentage could be assessed by thermogravimetry analysis (TGA). Morphology and topography were carried out by optical microscopy, moreover, X-ray Tomography allows it to display the sample’s inner part. Characterization shows that the component could have been contaminated or exposed to conditions that promote degradation after the manufacturing process. Finally, computerized X-ray tomography displayed that both samples showed a difference in porosity in a fractured sample and a healthy sample. All the above implies a change in the mechanical integrity of the... [more]
Developing a Hybrid Algorithm Based on an Equilibrium Optimizer and an Improved Backpropagation Neural Network for Fault Warning
Jiang Liu, Changshu Zhan, Haiyang Wang, Xingqin Zhang, Xichao Liang, Shuangqing Zheng, Zhou Meng, Guishan Zhou
July 13, 2023 (v1)
Keywords: BP neural network, deep learning, enhanced equilibrium optimizer, fault warning
In today’s rapidly evolving manufacturing landscape with the advent of intelligent technologies, ensuring smooth equipment operation and fostering stable business growth rely heavily on accurate early fault detection and timely maintenance. Machine learning techniques have proven to be effective in detecting faults in modern production processes. Among various machine learning algorithms, the Backpropagation (BP) neural network is a commonly used model for fault detection. However, due to the intricacies of the BP neural network training process and the challenges posed by local minima, it has certain limitations in practical applications, which hinder its ability to meet efficiency and accuracy requirements in real-world scenarios. This paper aims to optimize BP networks and develop more effective fault warning methods. The primary contribution of this research is the proposal of a novel hybrid algorithm that combines a random wandering strategy within the main loop of an equilibrium... [more]
Study on Radial Leakage Model and Law of Fractured Formation Drilling Fluid
Zelong Xie, Liang Zhu, Shubo Bi, Hui Ji, Tianyi Wang, Mengting Huang, Hao Zhang, Huimei Wu
June 7, 2023 (v1)
Keywords: cumulative leakage rate, drilling fluid leakage, fractured formation, herschel-bulkley flow pattern, leakage rate
Wellbore leakage mostly occurs in structurally developed fractured formations. Analyzing the real-time leakage rate during the drilling process plays an important role in identifying the leakage mechanism and its rules on-site. Based on the principles of fluid mechanics and using Herschel-Bulkley (H-B) drilling fluid, by reasonably simplifying the drilling fluid performance parameters, fracture roughness characteristic parameters, pressure difference between the wellbore and formation, and the radial extension length of drilling fluid, the radial leakage model is improved to improve the calculation accuracy. Using the Euler format in numerical analysis to solve the model and with the help of numerical analysis software, the radial leakage law of this flow pattern in the fractures is obtained. The results show that the deformation coefficient of the fracture index, fracture aperture, pressure difference, leakage rate, and cumulative leakage rate are positively correlated. The larger the... [more]
Design of a RGB-Arduino Device for Monitoring Copper Recovery from PCBs
Joan Morell, Antoni Escobet, Antonio David Dorado, Teresa Escobet
June 7, 2023 (v1)
Keywords: bioprocess optimization, chemical reactions, color sensing, copper recovery, mobile phone waste, non-invasive sensors, real-time monitoring system
The mobile phone industry, one of the fastest advancing sectors in production over the last few decades, has been associated with a high e-waste generation rate. Simultaneously, a high demand for the production of new electronic equipment has led to the scarcity of certain metals. In this context, many recent studies have focused on recovering certain metals from e-waste through the use of bioprocesses. Such recovery processes are based on the action of microorganisms that produce Fe(III) as an oxidant, in order to leach the copper contained in printed circuit boards. During the oxidation-reduction reaction between Fe(III) and metallic Cu, the color of the solution evolves from an initial reddish color, due to Fe(III), to a bluish-green color, due to the oxidized Cu. In this work, a hardware-software prototype is developed, through which the concentrations of the key analytes—Fe(III) and Cu(II)—can be determined in real time by monitoring the color of the solution. This is achieved thr... [more]
A CNN-Architecture-Based Photovoltaic Cell Fault Classification Method Using Thermographic Images
Chiwu Bu, Tao Liu, Tao Wang, Hai Zhang, Stefano Sfarra
May 23, 2023 (v1)
Keywords: automatic fault classification, CNN, deep learning, PV cell faults, thermography
Photovoltaic (PV) cells are a major part of solar power stations, and the inevitable faults of a cell affect its work efficiency and the safety of the power station. During manufacturing and service, it is necessary to carry out fault detection and classification. A convolutional-neural-network (CNN)-architecture-based PV cell fault classification method is proposed and trained on an infrared image data set. In order to overcome the problem of the original dataset’s scarcity, an offline data augmentation method is adopted to improve the generalization ability of the network. During the experiment, the effectiveness of the proposed model is evaluated by quantifying the obtained results with four deep learning models through evaluation indicators. The fault classification accuracy of the CNN model proposed here has been drawn by the experiment and reaches 97.42%, and it is superior to that of the models of AlexNet, VGG 16, ResNet 18 and existing models. In addition, the proposed model ha... [more]
Transformer Winding Fault Classification and Condition Assessment Based on Random Forest Using FRA
Mehran Tahir, Stefan Tenbohlen
May 23, 2023 (v1)
Keywords: condition assessment, decision tree (DT), frequency response analysis (FRA), Machine Learning, numerical indices, power transformer, random forest (RF)
At present, the condition assessment of transformer winding based on frequency response analysis (FRA) measurements demands skilled personnel. Despite many research efforts in the last decade, there is still no definitive methodology for the interpretation and condition assessment of transformer winding based on FRA results, and this is a major challenge for the industrial application of the FRA method. To overcome this challenge, this paper proposes a transformer condition assessment (TCA) algorithm, which is based on numerical indices, and a supervised machine learning technique to develop a method for the automatic interpretation of FRA results. For this purpose, random forest (RF) classifiers were developed for the first time to identify the condition of transformer winding and classify different faults in the transformer windings. Mainly, six common states of the transformer were classified in this research, i.e., healthy transformer, healthy transformer with saturated core, mecha... [more]
A Novel Workflow for Early Time Transient Pressure Data Interpretation in Tight Oil Reservoirs with Physical Constraints
Tongjing Liu, Liwu Jiang, Jinju Liu, Juan Ni, Xinju Liu, Pengxiang Diwu
May 2, 2023 (v1)
Keywords: early time transient data, new type curves, physical constraints, pseudo threshold pressure gradient (TPG), skin factor, tight oil reservoirs
In this work, a novel workflow has been proposed, validated and applied to interpret the early time transient pressure data in tight oil reservoirs with physical constraints. More specifically, the theoretical model was developed to obtain the transient pressure response for a vertical well in tight oil reservoirs with consideration of pseudo threshold pressure gradient (TPG). Then, a physical constraint between the skin factor and formation permeability has been proposed based on the physical meaning of percolation theory. This physical constraint can be applied to determine the lower limit of the skin factor which can reduce the uncertainty during the interpretation process. It is found that the influence range of the skin factor and permeability may partially overlap during the interpretation process without consideration of physical constraints. Additionally, it is found that the equivalent wellbore radius is more reasonable by considering the skin factor constraints. Furthermore,... [more]
Early Warning of High-Voltage Reactor Defects Based on Acoustic−Electric Correlation
Shuguo Gao, Chao Xing, Zhigang Zhang, Chenmeng Xiang, Haoyu Liu, Hongliang Liu, Rongbin Shi, Sihan Wang, Guoming Ma
May 2, 2023 (v1)
Keywords: adaptive noise reduction, joint diagnosis, k-nearest neighbors, reactor defect, relevance significance
Traditional high-voltage reactor monitoring and diagnosis research has problems such as high sampling demand, difficulty in noise reduction on site, many false alarms, and lack of on-site data. In order to solve the above problems, this paper proposes an acoustic−electric fusion high-voltage reactor acquisition system and defect diagnosis method based on reactor pulse current and ultrasonic detection signal. Using the envelope peak signal as the basic detection data, the sampling requirement of the system is reduced. To fill the missing data with partial discharge (PD) information, a method based on k-nearest neighbor (KNN) is proposed. An adaptive noise reduction method is carried out, and a noise threshold calculation method is given for the field sensors. A joint analysis method of acoustic and electrical signals based on correlation significance is established to determine whether a discharge event has occurred based on correlation significance. Finally, the method is applied to a... [more]
Integration of Thermal and RGB Data Obtained by Means of a Drone for Interdisciplinary Inventory
Joanna Paziewska, Antoni Rzonca
April 28, 2023 (v1)
Keywords: data integration, dense matching, thermal imagery, UAV
Thermal infrared imagery is very much gaining in importance in the diagnosis of energy losses in cultural heritage through non-destructive measurement methods. Hence, owing to the fact that it is a very innovative and, above all, safe solution, it is possible to determine the condition of the building, locate places exposed to thermal escape, and plan actions to improve the condition of the facility. The presented work is devoted to the technology of creating a dense point cloud and a 3D model, based on data obtained from UAV. It has been shown that it is possible to build a 3D point model based on thermograms with the specified accuracy by using thermal measurement marks and the dense matching method. The results achieved in this way were compared and, as the result of this work, the model obtained from color photos was integrated with the point cloud created on the basis of the thermal images. The discussed approach exploits measurement data obtained with three independent devices (t... [more]
Research on Calculation Method for Discharge Capacity of Draining Well in Tailing Ponds Based on “Simplification-Fitting” Method
Sha Wang, Guodong Mei, Lijie Guo, Xuyang Xie, Krzysztof Skrzypkowski
April 28, 2023 (v1)
Keywords: discharge capacity, draining well, empirical formula, simplification fitting, window type
The existing empirical formulas concerning draining systems are complex in their expression: there are difficulties in locating the intersection point among different flow patterns and parameters vary depending on the water level, resulting in a large amount of data to be processed and low calculation efficiency. To solve these problems, a “simplification-fitting” method was proposed herein to calculate the discharge capacity of a window-type draining well, and optimal and reasonable locations were selected as discrete points of water level to deduce the increasing progressive relationship of free flow discharge capacity among discrete points according to the window size and longitudinal layout of window-type draining wells. Additionally, the algorithm simplified the discharge formulas of half-pressure flow and pressure flow and defined the convergence criteria for water level-discharge capacity to further simplify the expression of pressure flow. The comparison and contrast between th... [more]
A Framework for Multivariate Statistical Quality Monitoring of Additive Manufacturing: Fused Filament Fabrication Process
Moath Alatefi, Abdulrahman M. Al-Ahmari, Abdullah Yahia AlFaify, Mustafa Saleh
April 28, 2023 (v1)
Keywords: additive manufacturing, control chart, fused deposition modeling process, fused filament fabrication, heuristic optimization, multivariate quality characteristics, process monitoring, transformation methods
Advances in additive manufacturing (AM) processes have increased the number of relevant applications in various industries. To keep up with this development, the process stability of AM processes should be monitored, which is conducted through the assessment of the outputs or product characteristics. However, the use of univariate control charts to monitor an AM process might lead to misleading results, as most additively manufactured products have more than one correlated quality characteristic (QC). This paper proposes a framework for monitoring the multivariate quality characteristics of AM processes, and the proposed framework was applied to monitor a fused filament fabrication (FFF) process. In particular, specimens were designed and produced using the FFF process, and their QCs were identified. Then, critical quality characteristic data were collected using a precise measurement system. Furthermore, we propose a transformation algorithm to ensure the normality of the collected da... [more]
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