scholarly journals Vibrodiagnostics Faults Classification for the Safety Enhancement of Industrial Machinery

Machines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 222
Author(s):  
Daniel Zuth ◽  
Petr Blecha ◽  
Tomas Marada ◽  
Rostislav Huzlik ◽  
Jiri Tuma ◽  
...  
Keyword(s):  
Success Rate ◽  
Suitable Condition ◽  
Numerical Control ◽  
Classification Model ◽  
Classification Models ◽  
Machining Center ◽  
Data Source ◽  
Safe Condition ◽  
Tool Damage ◽  
Machine Operator

The current digitization of industrial processes is leading to the development of smart machines and smart applications in the field of engineering technologies. The basis is an advanced sensor system that monitors selected characteristic values of the machine. The obtained data need to be further analysed, correctly interpreted, and visualized by the machine operator. Thus the machine operator can gain a sixth sense for keeping the machine and the production process in a suitable condition. This has a positive effect on reducing the stress load on the operator in the production of expensive components and in monitoring the safe condition of the machine. The key element here is the use of a suitable classification model for data evaluation of the monitored machine parameters. The article deals with the comparison of the success rate of classification models from the MATLAB Classification Learner App. Classification models will compare data from the frequency and time domain, the data source is the same. Both data samples are from real measurements on the CNC vertical machining center (CNC-Computer Numerical Control). Three basic states representing machine tool damage are recognized. The data are then processed and reduced for the use of the MATLAB Classification Learner app, which creates a model for recognizing faults. The article aims to compare the success rate of classification models when the data source is a dataset in time or frequency domain and combination.

scholarly journals Children’s Activity Classification for Domestic Risk Scenarios Using Environmental Sound and a Bayesian Network

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 884
Author(s):  
Antonio García-Domínguez ◽  
Carlos E. Galván-Tejada ◽  
Ramón F. Brena ◽  
Antonio A. Aguileta ◽  
Jorge I. Galván-Tejada ◽  
...  
Keyword(s):  
Feature Selection ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Model ◽  
Activity Classification ◽  
Environmental Sound ◽  
Non Invasive ◽  
Akaike Criterion ◽  
Data Source ◽  
Feature Selection Techniques

Children’s healthcare is a relevant issue, especially the prevention of domestic accidents, since it has even been defined as a global health problem. Children’s activity classification generally uses sensors embedded in children’s clothing, which can lead to erroneous measurements for possible damage or mishandling. Having a non-invasive data source for a children’s activity classification model provides reliability to the monitoring system where it is applied. This work proposes the use of environmental sound as a data source for the generation of children’s activity classification models, implementing feature selection methods and classification techniques based on Bayesian networks, focused on the recognition of potentially triggering activities of domestic accidents, applicable in child monitoring systems. Two feature selection techniques were used: the Akaike criterion and genetic algorithms. Likewise, models were generated using three classifiers: naive Bayes, semi-naive Bayes and tree-augmented naive Bayes. The generated models, combining the methods of feature selection and the classifiers used, present accuracy of greater than 97% for most of them, with which we can conclude the efficiency of the proposal of the present work in the recognition of potentially detonating activities of domestic accidents.

scholarly journals Research on Real-Time Multiple Single Garbage Classification Based on Convolutional Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Jian-ye Yuan ◽  
Xin-yuan Nan ◽  
Cheng-rong Li ◽  
Le-le Sun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Environmental Science ◽  
Fine Tuning ◽  
Classification Model ◽  
Classification Models ◽  
Data Set ◽  
Classification Tasks ◽  
Accuracy Rates

Considering that the garbage classification is urgent, a 23-layer convolutional neural network (CNN) model is designed in this paper, with the emphasis on the real-time garbage classification, to solve the low accuracy of garbage classification and recycling and difficulty in manual recycling. Firstly, the depthwise separable convolution was used to reduce the Params of the model. Then, the attention mechanism was used to improve the accuracy of the garbage classification model. Finally, the model fine-tuning method was used to further improve the performance of the garbage classification model. Besides, we compared the model with classic image classification models including AlexNet, VGG16, and ResNet18 and lightweight classification models including MobileNetV2 and SuffleNetV2 and found that the model GAF_dense has a higher accuracy rate, fewer Params, and FLOPs. To further check the performance of the model, we tested the CIFAR-10 data set and found the accuracy rates of the model (GAF_dense) are 0.018 and 0.03 higher than ResNet18 and SufflenetV2, respectively. In the ImageNet data set, the accuracy rates of the model (GAF_dense) are 0.225 and 0.146 higher than Resnet18 and SufflenetV2, respectively. Therefore, the garbage classification model proposed in this paper is suitable for garbage classification and other classification tasks to protect the ecological environment, which can be applied to classification tasks such as environmental science, children’s education, and environmental protection.

Research on S-gear design, manufacturing and measuring based on NC machining center

2020 ◽  
pp. 095440542098134
Author(s):  
Shao-ying Ren ◽  
Yan-zhong Wang ◽  
Yuan Li
Keyword(s):  
Convex Surface ◽  
Coordinate Measuring Machine ◽  
Nc Machining ◽  
Tooth Profile ◽  
Numerical Control ◽  
Machining Center ◽  
Involute Gear ◽  
Measuring Machine ◽  
Gear Contact ◽  
Nc Machining Center

This article presents a method of design, manufacturing, and measuring S-gear. S-gear is a kind of gear whose tooth profile is an S-shaped curve. The sine (cosine) gear, cycloid gear, polynomial gear, and circular arc gear are all S-gears in essence. In the S-gear transmission, the concave surface of one gear and the convex surface of the other gear contact each other. Therefore, the power transmitted by S-gear is much larger than that of the convex-convex-contact involute gear. Some scholars have studied the characteristics of S-gear, but few have explored its manufacturing. In this article, the Numerical Control (NC) machining technology of S-gear is studied in detail for its industrial application. The polynomial curve is used to construct the tooth profile of the S-gear based on the Gear Meshing Theory. The mathematical model of polynomial S-gear is established, by which involute gear can be represented as a special S-gear. The steps of generating NC codes are described. Then, the S-gear sample is processed with an NC machining center. Finally, the sample is measured with a Coordinate Measuring Machine (CMM), and the measurement results show that the accuracy of the S-gear processed by the NC machining center reaches ISO6. This research provides a feasible approach for the design, manufacturing, and measuring of S-gear.

Dynamic Characteristics Analysis for the Headstock of a Vertical Machining Center

2016 ◽  
Vol 836-837 ◽  
pp. 348-358
Author(s):  
Zhe Li ◽  
Song Zhang ◽  
Yan Chen ◽  
Peng Wang ◽  
Ai Rong Zhang
Keyword(s):  
Dynamic Characteristics ◽  
Natural Frequencies ◽  
Vibration Characteristics ◽  
Numerical Control ◽  
Vibration Test ◽  
Machining Accuracy ◽  
Modal Test ◽  
Machining Center ◽  
Characteristics Analysis ◽  
Nc Machine

Dynamic characteristics of numerical control (NC) machine tools, such as natural frequency and vibration property, directly affect machining efficiency and finished surface quality. In general, low-order natural frequencies of critical components have significant influences on machine tool’s performances. The headstock is the most important component of the machine tool. The reliability, cutting stability, and machining accuracy of a machining center largely depend on the structure and dynamic characteristics of the headstock. First, in order to obtain the natural frequencies and vibration characteristics of the headstock of a vertical machining center, modal test and vibration test in free running and cutting conditions were carried out by means of the dynamic signal collection and analysis system. According to the modal test, the first six natural frequencies of the headstock were obtained, which can not only guide the working speed, but also act as the reference of structural optimization aiming at frequency-shift. Secondly, by means of the vibration test, the vibration characteristics of the headstock were obtained and the main vibration sources were found out. Finally the corresponding vibration reduction plans were proposed in this paper. That provides the reference for improving the performance of the overall unit.

Comparison of Manufacturing Data Analysis For 5 & 3-Axis Vertical Machining Center for the Time and Tool Benefits of Industries

2018 ◽  
Vol 7 (4.5) ◽  
pp. 196 ◽  
Author(s):  
S. P. Sundar Singh Sivam ◽  
K. Saravanan ◽  
N. Pradeep ◽  
S. Rajendra Kumar ◽  
Sathiyamoorthy Karuppiah
Keyword(s):  
Numerical Control ◽  
Manufacturing Cost ◽  
Cnc Machine ◽  
Operational Parameters ◽  
Identical Element ◽  
High Productivity ◽  
Machining Center ◽  
Novel Approach ◽  
Complicated Geometries ◽  
Machining Operations

The dawn of globalization of business and competitiveness in manufacturing has forced firms to enhance their manufacturing facilities to reply to plug necessities. One of the crucial factors for this is often machine evaluation that involves a crucial decision using general and obscure information. The Primarily mass production aims high productivity so as to reduce cost and interchangeability to facilitate simple assembly which necessitates the production devices to increase the speed of manufacture. Across the business, the assembly challenges pivot around cutting lead times, increasing throughout and obtaining products to market as quickly as possible alongside some less challenging problems like shorter runs, higher product combine, tighter tolerances, a lot of complicated geometries in harder materials, and complete machining during a single handling. Advancements in technology have resulted in a creation of a lot of responsive tools referred to as MTM systems that are computer numerical Control (CNC) systems capable of acting a variety of operations with multiple tools and spindles in a single setup. The following project aims at reduction of manufacturing cost by modifying the process layout and operational parameters by novel approach for an identical element for 3 and 5- Axis Vertical Machining center. Nowadays, machining layout and operational sequence plays a significant role in automotive business to produce products at competitive price in market which consists of Machines, Tools, fixtures, computer interface, trained professionals and form of products. MAKINO PS60 is a Multi axis CNC machine (BRIDGE PORT), that helps to perform the machining operations on the roles at 5 totally different axes to create the required profiles whose implementation can pave means for a few terribly important benefits like, seven machines are replaced by Single machine, Man power are reduced from 9 to 3 per day, Tools usage reduced from 40 to 30 per day & production cost can reduce up to 60%.  

MicroRNA Profiling as a Methodology to Diagnose Ménière’s Disease: Potential Application of Machine Learning

2020 ◽  
pp. 019459982094064
Author(s):  
Matthew Shew ◽  
Helena Wichova ◽  
Andres Bur ◽  
Devin C. Koestler ◽  
Madeleine St Peter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hearing Loss ◽  
Meniere’S Disease ◽  
Menière’S Disease ◽  
Classification Model ◽  
Meniere's Disease ◽  
Ménière’S Disease ◽  
Classification Models ◽  
Menière's Disease ◽  
Ménière's Disease

Objective Diagnosis and treatment of Ménière’s disease remains a significant challenge because of our inability to understand what is occurring on a molecular level. MicroRNA (miRNA) perilymph profiling is a safe methodology and may serve as a “liquid biopsy” equivalent. We used machine learning (ML) to evaluate miRNA expression profiles of various inner ear pathologies to predict diagnosis of Ménière’s disease. Study Design Prospective cohort study. Setting Tertiary academic hospital. Subjects and Methods Perilymph was collected during labyrinthectomy (Ménière’s disease, n = 5), stapedotomy (otosclerosis, n = 5), and cochlear implantation (sensorineural hearing loss [SNHL], n = 9). miRNA was isolated and analyzed with the Affymetrix miRNA 4.0 array. Various ML classification models were evaluated with an 80/20 train/test split and cross-validation. Permutation feature importance was performed to understand miRNAs that were critical to the classification models. Results In terms of miRNA profiles for conductive hearing loss versus Ménière’s, 4 models were able to differentiate and identify the 2 disease classes with 100% accuracy. The top-performing models used the same miRNAs in their decision classification model but with different weighted values. All candidate models for SNHL versus Ménière’s performed significantly worse, with the best models achieving 66% accuracy. Ménière’s models showed unique features distinct from SNHL. Conclusions We can use ML to build Ménière’s-specific prediction models using miRNA profile alone. However, ML models were less accurate in predicting SNHL from Ménière’s, likely from overlap of miRNA biomarkers. The power of this technique is that it identifies biomarkers without knowledge of the pathophysiology, potentially leading to identification of novel biomarkers and diagnostic tests.

Design of the B-Axis of the Water Chamber Special Turn-Milling Center

2014 ◽  
Vol 621 ◽  
pp. 294-303
Author(s):  
Feng He Wu ◽  
Shu Zhi Li ◽  
Chong Min Jiang ◽  
Dong Dong Gao
Keyword(s):  
Nuclear Power ◽  
Roller Bearing ◽  
Pressure Vessels ◽  
Numerical Control ◽  
Central Axis ◽  
Double Row ◽  
Tapered Roller Bearing ◽  
Machining Center ◽  
Water Chamber ◽  
Turn Milling

As the key part of nuclear pressure vessels, the water chamber head has the features of large size, complex shape and difficult processability. The processes of conventional machining method are too decentralized, the cumulative error is large, and the processing cycle is long. In order to meet the efficiency and high-precision machining requirements of the nuclear power water chamber head, a design scheme of special turn-milling machining center and B-axis components is proposed. The part characteristics and machining difficulties of the water chamber head are analyzed in the paper. The machining areas and processes are subdivided, the numerical control technological process is presented. In order to complete the finish machining of the water chamber's inner and outer surfaces and its inclined holes and sloping faces, the movable gantry milling machining center with pendulant ram is designed. According to the head processing requirements, three design schemes of B-axis components assembly, single row cylindrical roller bearing type, double row tapered roller bearing type and the central axis unloading type, are proposed. Through the theory analysis and statics and modal comparative analysis, the central axis unloading type is adopted as the optimal proposal in B-axis component design. Simulation and experimental results show that the turn-milling machining center based on the unloading B-axis assembly can satisfy the precision requirement of the water chamber head.

Visual exploration of classification models for various data types in risk assessment

2012 ◽  
Vol 11 (3) ◽  
pp. 237-251 ◽  
Author(s):  
Malgorzata Migut ◽  
Marcel Worring
Keyword(s):  
Risk Assessment ◽  
Visual Analytics ◽  
Ordinal Data ◽  
Visual Exploration ◽  
Classification Model ◽  
Classification Models ◽  
Data Types ◽  
Suggested Approach ◽  
Wrong Decision ◽  
Data Elements

In risk assessment applications well-informed decisions need to be made based on large amounts of multi-dimensional data. In many domains, not only the risk of a wrong decision, but also of the trade-off between the costs of possible decisions are of utmost importance. In this paper we describe a framework to support the decision-making process, which tightly integrates interactive visual exploration with machine learning. The proposed approach uses a series of interactive 2D visualizations of numerical and ordinal data combined with visualization of classification models. These series of visual elements are linked to the classifier’s performance, which is visualized using an interactive performance curve. This interaction allows the decision-maker to steer the classification model and instantly identify the critical, cost-changing data elements in the various linked visualizations. The critical data elements are represented as images in order to trigger associations related to the knowledge of the expert. In this way the data visualization and classification results are not only linked together, but are also linked back to the classification model. Such a visual analytics framework allows the user to interactively explore the costs of his decisions for different settings of the model and, accordingly, use the most suitable classification model. More informed and reliable decisions result. A case study in the forensic psychiatry domain reveals the usefulness of the suggested approach.

scholarly journals Identification of Judicial Outcomes in Judgments: A Generalized Gini-PLS Approach

Stats ◽  
2020 ◽  
Vol 3 (4) ◽  
pp. 427-443
Author(s):  
Gildas Tagny-Ngompé ◽  
Stéphane Mussard ◽  
Guillaume Zambrano ◽  
Sébastien Harispe ◽  
Jacky Montmain
Keyword(s):  
Law Enforcement ◽  
Success Rate ◽  
Text Classification ◽  
Binary Classification ◽  
Classification Algorithms ◽  
Classification Models ◽  
Target Variable ◽  
Technical Aspects ◽  
Legal Documents ◽  
Judicial Outcomes

This paper presents and compares several text classification models that can be used to extract the outcome of a judgment from justice decisions, i.e., legal documents summarizing the different rulings made by a judge. Such models can be used to gather important statistics about cases, e.g., success rate based on specific characteristics of cases’ parties or jurisdiction, and are therefore important for the development of Judicial prediction not to mention the study of Law enforcement in general. We propose in particular the generalized Gini-PLS which better considers the information in the distribution tails while attenuating, as in the simple Gini-PLS, the influence exerted by outliers. Modeling the studied task as a supervised binary classification, we also introduce the LOGIT-Gini-PLS suited to the explanation of a binary target variable. In addition, various technical aspects regarding the evaluated text classification approaches which consists of combinations of representations of judgments and classification algorithms are studied using an annotated corpora of French justice decisions.

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