scholarly journals Magnetoelastic Ribbons as Vibration Sensors for Real-Time Health Monitoring of Rotating Metal Beams

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8122
Author(s):  
Georgios Samourgkanidis ◽  
Dimitris Kouzoudis
Keyword(s):  
Real Time ◽  
Great Accuracy ◽  
Health State ◽  
Rotating Blades ◽  
Second Stage ◽  
Vibration Sensors ◽  
Comparison Results ◽  
Magnetoelastic Sensors ◽  
Bending Modes ◽  
Experimental Values

In the current work, magnetoelastic material ribbons are used as vibration sensors to monitor, in real time and non-destructively, the mechanical health state of rotating beam blades. The magnetoelastic material has the form of a thin ribbon and is composed of Metglas alloy 2826 MB. The study was conducted in two stages. In the first stage, an experiment was performed to test the ability of the ribbon to detect and transmit the vibration behavior of four rotating blades, while the second stage was the same as the first but with minor damages introduced to the blades. As far as the first stage is concerned, the results show that the sensor can detect and transmit with great accuracy the vibratory behavior of the rotating blades, through which important information about the mechanical health state of the blade can be extracted. Specifically, the fast Fourier transform (FFT) spectrum of the recorded signal revealed five dominant peaks in the frequency range 0–3 kHz, corresponding to the first five bending modes of the blades. The identification process was accomplished using ANSYS modal analysis, and the comparison results showed deviation values of less than 1% between ANSYS and the experimental values. In the second stage, two types of damages were introduced to the rotating blades, an edge cut and a hole. The damages were scaled in number from one blade to another, with the first blade having only one side cut while the last blade had two side cuts and two holes. The results, as was expected, show a measurable shifting on the frequency values of the bending modes, thus proving the ability of the proposed magnetoelastic sensors to detect and transmit changes of the mechanical state of rotating blades in real time.

Study of Real-Time Monitoring System on the Underground Mining Activities Based on Explosion Source Location Technology

2012 ◽  
Vol 190-191 ◽  
pp. 1179-1182
Author(s):  
Xiu Zhi Meng ◽  
Zeng Zhi Zhang ◽  
Zong Sheng Wang
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Underground Mining ◽  
Coal Mines ◽  
Source Location ◽  
Environmental Damage ◽  
Mining Activities ◽  
Real Time Monitoring ◽  
P Waves ◽  
Vibration Sensors

The mining boundary ultra-layer & cross-border of some small coal mines in the profit-driven results in a many of safety accidents, waste of resources and environmental damage while the state can not achieve the full uninterrupted supervision because of the backward monitoring tools and equipment. In this situation the real-time monitoring system for underground mining activities is designed based on explosion source location technology. Small and medium-sized coal mines tunnel by blasting operations. The P waves are picked up by acceleration vibration sensors buried underground that are identified and dealt by using wavelet transform. The bursting point is located by the Geiger algorithm and displayed in the mine’s electronic map. The monitor system has good stability, small positioning error by field-proven.

scholarly journals Calculation and AFM Experimental Research on Slip Friction for Unlubricated Spherical Contact with Roughness Effect

Micromachines ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1428
Author(s):  
Shengguang Zhu ◽  
Liyong Ni
Keyword(s):  
Friction Force ◽  
Sliding Friction ◽  
Static Friction ◽  
Friction Model ◽  
Rigid Sphere ◽  
Roughness Effect ◽  
Force Microscopy ◽  
Comparison Results ◽  
Experimental Values ◽  
Calculation Models

Previous research on friction calculation models has mainly focused on static friction, whereas sliding friction calculation models are rarely reported. In this paper, a novel sliding friction model for realizing a dry spherical flat contact with a roughness effect at the micro/nano scale is proposed. This model yields the sliding friction by the change in the periodic substrate potential, adopts the basic assumptions of the Greenwood–Williamson random contact model about asperities, and assumes that the contact area between a rigid sphere and a nominal rough flat satisfies the condition of interfacial friction. It subsequently employs a statistical method to determine the total sliding friction force, and finally, the feasibility of this model presented is verified by atomic force microscopy friction experiments. The comparison results show that the deviations of the sliding friction force and coefficient between the theoretical calculated values and the experimental values are in a relatively acceptable range for the samples with a small plasticity index (Ψ ≤ 1).

Evaluating fetal head dimension changes during labor using open magnetic resonance imaging

2017 ◽  
Vol 45 (3) ◽  
Author(s):  
Christian Bamberg ◽  
Jan Deprest ◽  
Nikhil Sindhwani ◽  
Ulf Teichgräberg ◽  
Felix Güttler ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Real Time ◽  
Vaginal Delivery ◽  
Single Shot ◽  
Resonance Imaging ◽  
Midsagittal Plane ◽  
Fetal Head ◽  
Second Stage ◽  
Open Magnetic Resonance Imaging

AbstractAim:Fetal skull molding is important for the adaptation of the head to the birth canal during vaginal delivery. Importantly, the fetal head must rotate around the maternal symphysis pubis. The goals of this analysis were to observe a human birth in real-time using an open magnetic resonance imaging (MRI) scanner and describe the fetal head configuration during expulsion.Methods:Real-time cinematic MRI series (TSE single-shot sequence, TR 1600 ms, TE 150 ms) were acquired from the midsagittal plane of the maternal pelvis during the active second stage of labor at 37 weeks of gestation. Frame-by-frame analyses were performed to measure the frontooccipital diameter (FOD) and distance from the vertex to the base of the fetal skull.Results:During vaginal delivery in an occiput anterior position, the initial FOD was 10.3 cm. When expulsion began, the fetal skull was deformed and elongated, with the FOD increasing to 10.8 cm and 11.2 cm at crowning. In contrast, the distance from the vertex to the base of the skull was reduced from 6.4 cm to 5.6 cm at expulsion.Conclusions:Fetal head molding is the change in the fetal head due to the forces of labor. The biomechanics of this process are poorly understood. Our visualization of the normal mechanism of late second-stage labor shows that MRI technology can for the first time help define the changes in the diameters of the fetal head during active labor.

scholarly journals In-Situ Monitoring and Diagnosing for Fused Filament Fabrication Process Based on Vibration Sensors

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2589 ◽  
Author(s):  
Yongxiang Li ◽  
Wei Zhao ◽  
Qiushi Li ◽  
Tongcai Wang ◽  
Gong Wang
Keyword(s):  
Product Quality ◽  
Real Time ◽  
Case Studies ◽  
Complex Geometry ◽  
Identification Accuracy ◽  
In Situ Monitoring ◽  
Support Vector ◽  
Fused Filament Fabrication ◽  
Vibration Sensors

Fused filament fabrication (FFF) is one of the most widely used additive manufacturing (AM) technologies and it has great potential in fabricating prototypes with complex geometry. For high quality manufacturing, monitoring the products in real time is as important as maintaining the FFF machine in the normal state. This paper introduces an approach that is based on the vibration sensors and data-driven methods for in-situ monitoring and diagnosing the FFF process. The least squares support vector machine (LS-SVM) algorithm has been applied for identifying the normal and filament jam states of the FFF machine, besides fault diagnosis in real time. The identification accuracy for the case studies explored here using LS-SVM is greater than 90%. Furthermore, to ensure the product quality during the FFF process, the back-propagation neural network (BPNN) algorithm has been used to monitor and diagnose the quality defects, as well as the warpage and material stack caused by abnormal leakage for the products in-situ. The diagnosis accuracy for the case studies explored here using BPNN is greater than 95%. Results from the experiments show that the proposed approach can accurately recognize the machine failures and quality defects during the FFF process, thus effectively assuring the product quality.

Accurate Prediction Approach of 3D Trimming Line for Auto Panel Part

2013 ◽  
Vol 535-536 ◽  
pp. 235-238 ◽  
Author(s):  
Yi Dong Bao ◽  
Yang Sang ◽  
Hou Min Wang
Keyword(s):  
Inverse Analysis ◽  
Layer By Layer ◽  
Adjacent Element ◽  
Analysis Theory ◽  
Comparison Results ◽  
Distorted Mesh ◽  
One Step ◽  
Experimental Values ◽  
Prediction Approach ◽  
Auto Panel

It is difficult to obtain 3D trimming line using traditional prediction methods for auto panel parts. An initial geometrical development method with element layer is proposed based on one step inverse analysis theory for this problem. The flange mesh can be unfold onto the die surface layer by layer according to nodal adjacent element relation, then the above development mesh is smoothed by mesh smoothing method with sliding constraint surface in order to delete overlap and distorted mesh, the 3D initial mesh can be obtained for one step inverse analysis method. The accurate 3D trimming line of auto panel part can be achieved by plasticity iteration of one step inverse analysis. A typical real part of 3D trimming line prediction is selected to prove this method, the comparison results between the simulated and experimental values show that this method has enough precision and can handle complex parts, satisfies the engineering practical demands.

A Hybrid Inference Approach for Health Diagnostics With Unexampled Faulty States

2012 ◽  
Author(s):  
Prasanna Tamilselvan ◽  
Pingfeng Wang
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Power Transformer ◽  
Roller Bearing ◽  
Health State ◽  
Health States ◽  
System Failures ◽  
State Classification ◽  
Testing Stage ◽  
Health Diagnosis

System health diagnostics provides diversified benefits such as improved safety, improved reliability and reduced costs for the operation and maintenance of engineered systems. Successful health diagnostics requires the knowledge of system failures. However, with an increasing complexity it is extraordinarily difficult to have a well-tested system so that all potential faulty states can be realized and studied at product testing stage. Thus, real time health diagnostics requires automatic detection of unexampled faulty states through the sensory signals to avoid sudden catastrophic system failures. This paper presents a hybrid inference approach (HIA) for structural health diagnosis with unexampled faulty states, which employs a two-fold inference process comprising of preliminary statistical learning based anomaly detection and artificial intelligence based health state classification for real time condition monitoring. The HIA is able to identify and isolate the unexampled faulty states through interactively detecting the deviation of sensory data from the known health states and forming new health states autonomously. The proposed approach takes the advantages of both statistical approaches and artificial intelligence based techniques and integrates them together in a unified diagnosis framework. The performance of proposed HIA is demonstrated with a power transformer and roller bearing health diagnosis case studies, where Mahalanobis distance serves as a representative statistical inference approach.

SVM-BASED PHONEME CLASSIFICATION AND LIP SHAPE REFINEMENT IN REAL-TIME LIP-SYNCH SYSTEM

2006 ◽  
Vol 20 (07) ◽  
pp. 1029-1051 ◽  
Author(s):  
HANSEOK KO ◽  
DAVID K. HAN
Keyword(s):  
Real Time ◽  
Processing Time ◽  
Support Vector ◽  
Mel Frequency Cepstral Coefficients ◽  
Phoneme Classification ◽  
Second Stage ◽  
Single Frame ◽  
Time Operation ◽  
Real Time Operation ◽  
Coarse To Fine

In this paper, we present a real time lip-synch system that activates 2-D avatar's lip motion in synch with incoming speech utterance. To achieve the real time operation of the system, the processing time was minimized by "merge and split" procedures resulting in coarse-to-fine phoneme classification. At each stage of phoneme classification, the support vector machine (SVM) method was applied to reduce the computational load while maintaining the desired accuracy. The coarse-to-fine phoneme classification, is accomplished via two_stages of feature extraction: in the first stage, each speech frame is acoustically analyzed for three classes of lip opening using Mel Frequency Cepstral Coefficients (MFCC) as a feature; in the second stage, each frame is further refined for detailed lip shape using formant information. The method was implemented in 2-D lip animation and it was demonstrated that the system was effective in accomplishing real-time lip-synch. This approach was tested on a PC using the Microsoft Visual Studio with an Intel Pentium IV 1.4 Giga Hz CPU and 384 MB RAM. It was observed that the methods of phoneme merging and SVM achieved about twice the speed in recognition than the method employing the Hidden Markov Model (HMM). A typical latency time per a single frame observed using the proposed method was in the order of 18.22 milliseconds while an HMM method under identical conditions resulted about 30.67 milliseconds.

scholarly journals Subsurface Scattering-Based Object Rendering Techniques for Real-Time Smartphone Games

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Won-Sun Lee ◽  
Seung-Do Kim ◽  
Seongah Chin
Keyword(s):  
Distribution Function ◽  
Computer Graphics ◽  
Real Time ◽  
Scattering Method ◽  
Bidirectional Reflectance Distribution Function ◽  
Subsurface Scattering ◽  
Bidirectional Reflectance ◽  
Comparison Results ◽  
Bidirectional Reflectance Distribution ◽  
Rendering Techniques

Subsurface scattering that simulates the path of a light through the material in a scene is one of the advanced rendering techniques in the field of computer graphics society. Since it takes a number of long operations, it cannot be easily implemented in real-time smartphone games. In this paper, we propose a subsurface scattering-based object rendering technique that is optimized for smartphone games. We employ our subsurface scattering method that is utilized for a real-time smartphone game. And an example game is designed to validate how the proposed method can be operated seamlessly in real time. Finally, we show the comparison results between bidirectional reflectance distribution function, bidirectional scattering distribution function, and our proposed subsurface scattering method on a smartphone game.

scholarly journals Mobile-Based Technology for Data Management of Students Fieldwork Practices

2021 ◽  
Vol 9 (1) ◽  
pp. 15-28
Author(s):  
Nandang Hermanto ◽  
Alya Khansa Dzakkiyah
Keyword(s):  
Data Management ◽  
Real Time ◽  
Learning Process ◽  
Teaching And Learning ◽  
Business Environment ◽  
Functional Testing ◽  
Second Stage ◽  
Testing Phase ◽  
School Committee ◽  
Real Work

Fieldwork Practices (PKL) is one of the activities in the vocational teaching and learning process which takes place in a direct business environment. The main purpose of implementing PKL is to introduce students to the real work. The implementation of PKL at SMK Ma'arif NU 1 Sumpiuh is carried out for 6 months which is divided into 2 implementation activities, namely 3 months in odd semester and 3 months in even semester. The purpose of this study is to provide a suggestion to facilitate the process of recording PKL student data both in odd and even periods. In addition, the proposed system also provides details on the location of PKL and provides if in the second stage of PKL implementation there are students who change the location. The PKL application has gone through the system functional testing phase and has been running according to the needs of the PKL activity process. The technology used in this study uses a mobile so that all activities both from the school, committee and students can be monitored in real time by all related parties.

scholarly journals Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication

2021 ◽  
Vol 11 (23) ◽  
pp. 11530
Author(s):  
Pangwei Wang ◽  
Xiao Liu ◽  
Yunfeng Wang ◽  
Tianren Wang ◽  
Juan Zhang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Short Term ◽  
Spatiotemporal Features ◽  
Traffic State ◽  
State Prediction ◽  
Comparison Results ◽  
Perception System

Real-time and reliable short-term traffic state prediction is one of the most critical technologies in intelligent transportation systems (ITS). However, the traffic state is generally perceived by single sensor in existing studies, which is difficult to satisfy the requirement of real-time prediction in complex traffic networks. In this paper, a short-term traffic prediction model based on complex neural network is proposed under the environment of vehicle-to-everything (V2X) communication systems. Firstly, a traffic perception system of multi-source sensors based on V2X communication is proposed and designed. A mobile edge computing (MEC)-assisted architecture is then introduced in a V2X network to facilitate perceptual and computational abilities of the system. Moreover, the graph convolutional network (GCN), the gated recurrent unit (GRU), and the soft-attention mechanism are combined to extract spatiotemporal features of traffic state and integrate them for future prediction. Finally, an intelligent roadside test platform is demonstrated for perception and computation of real-time traffic state. The comparison experiments show that the proposed method can significantly improve the prediction accuracy by comparing with the existing neural network models, which consider one of the spatiotemporal features. In particular, for comparison results of the traffic state prediction and the error value of root mean squared error (RMSE) is reduced by 39.53%, which is the greatest reduction in error occurrences by comparing with the GCN and GRU models in 5, 10, 15 and 30 minutes respectively.

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