scholarly journals Railway Wheel Flat Detection System Based on a Parallelogram Mechanism

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3614 ◽  
Author(s):  
Run Gao ◽  
Qixin He ◽  
Qibo Feng
Keyword(s):  
Detection System ◽  
Field Measurements ◽  
Measuring System ◽  
Detection Methods ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Railway Wheel ◽  
Measuring Mechanism ◽  
Train Speed ◽  
Wheel Flat

Wheel flats are a key fault in railway systems, which can bring great harm to vehicle operation safety. At present, most wheel flat detection methods use qualitative detection and do not meet practical demands. In this paper, we used a railway wheel flat measurement method based on a parallelogram mechanism to detect wheel flats dynamically and quantitatively. Based on our experiments, we found that system performance was influenced by the train speed. When the train speed was higher than a certain threshold, the wheel impact force would cause vibration of the measuring mechanism and affect the detection accuracy. Since the measuring system was installed at the on-site entrance of the train garage, to meet the speed requirement, a three-dimensional simulation model was established, which was based on the rigid-flexible coupled multibody dynamics theory. The speed threshold of the measuring mechanism increased by the reasonable selection of the damping coefficients of the hydraulic damper, the measuring positions, and the downward displacements of the measuring ruler. Finally, we applied the selected model parameters to the parallelogram mechanism, where field measurements showed that the experimental results were consistent with the simulation results.

scholarly journals Design of Meteorological Element Detection Platform for Atmospheric Boundary Layer Based on UAV

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Yonghong Zhang ◽  
Tiantian Dong ◽  
Yunping Liu
Keyword(s):  
Remote Sensing ◽  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Detection System ◽  
Structure Design ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Meteorological Element ◽  
Local Storage ◽  
And Control

Among current detection methods of the atmospheric boundary layer, sounding balloon has disadvantages such as low recovery and low reuse rate, anemometer tower has disadvantages such as fixed location and high cost, and remote sensing detection has disadvantages such as low data accuracy. In this paper, a meteorological element sensor was carried on a six-rotor UAV platform to achieve detection of meteorological elements of the atmospheric boundary layer, and the influence of different installation positions of the meteorological element sensor on the detection accuracy of the meteorological element sensor was analyzed through many experiments. Firstly, a six-rotor UAV platform was built through mechanical structure design and control system design. Secondly, data such as temperature, relative humidity, pressure, elevation, and latitude and longitude were collected by designing a meteorological element detection system. Thirdly, data management of the collected data was conducted, including local storage and real-time display on ground host computer. Finally, combined with the comprehensive analysis of the data of automatic weather station, the validity of the data was verified. This six-rotor UAV platform carrying a meteorological element sensor can effectively realize the direct measurement of the atmospheric boundary layer and in some cases can make up for the deficiency of sounding balloon, anemometer tower, and remote sensing detection.

scholarly journals I2DS: Interpretable Intrusion Detection System Using Autoencoder and Additive Tree

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Wenfeng Xu ◽  
Yongxian Fan ◽  
Changyong Li
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Predictive Performance ◽  
Detection Performance ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Learning Models ◽  
Model Based ◽  
Additive Tree

Intrusion detection system (IDS), the second security gate behind the firewall, can monitor the network without affecting the network performance and ensure the system security from the internal maximum. Many researches have applied traditional machine learning models, deep learning models, or hybrid models to IDS to improve detection effect. However, according to Predicted accuracy, Descriptive accuracy, and Relevancy (PDR) framework, most of detection models based on model-based interpretability lack good detection performance. To solve the problem, in this paper, we have proposed a novel intrusion detection system model based on model-based interpretability, called Interpretable Intrusion Detection System (I2DS). We firstly combine normal and attack samples reconstructed by AutoEncoder (AE) with training samples to highlight the normal and attack features, so that the classifier has a gorgeous effect. Then, Additive Tree (AddTree) is used as a binary classifier, which can provide excellent predictive performance in the combined dataset while maintaining good model-based interpretability. In the experiment, UNSW-NB15 dataset is used to evaluate our proposed model. For detection performance, I2DS achieves a detection accuracy of 99.95%, which is better than most of state-of-the-art intrusion detection methods. Moreover, I2DS maintains higher simulatability and captures the decision rules easily.

scholarly journals Robust Self-Adaptation Fall-Detection System Based on Camera Height

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3768 ◽  
Author(s):  
Kong ◽  
Chen ◽  
Wang ◽  
Chen ◽  
Meng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Fall Detection ◽  
Daily Activities ◽  
Detection Methods ◽  
Depth Camera ◽  
Detection Accuracy ◽  
Data Set ◽  
Human Segmentation ◽  
Self Adaptation

Vision-based fall-detection methods have been previously studied but many have limitations in terms of practicality. Due to differences in rooms, users do not set the camera or sensors at the same height. However, few studies have taken this into consideration. Moreover, some fall-detection methods are lacking in terms of practicality because only standing, sitting and falling are taken into account. Hence, this study constructs a data set consisting of various daily activities and fall events and studies the effect of camera/sensor height on fall-detection accuracy. Each activity in the data set is carried out by eight participants in eight directions and taken with the depth camera at five different heights. Many related studies heavily depended on human segmentation by using Kinect SDK but this is not reliable enough. To address this issue, this study proposes Enhanced Tracking and Denoising Alex-Net (ETDA-Net) to improve tracking and denoising performance and classify fall and non-fall events. Experimental results indicate that fall-detection accuracy is affected by camera height, against which ETDA-Net is robust, outperforming traditional deep learning based fall-detection methods.

scholarly journals Quality Detection System of Transparent Nonel Tubes Based on Image Processing

2015 ◽  
Vol 9 (1) ◽  
pp. 697-702
Author(s):  
Guodong Sun ◽  
Wei Xu ◽  
Lei Peng
Keyword(s):  
Image Processing ◽  
Detection Method ◽  
Detection System ◽  
Recognition Rate ◽  
Human Vision ◽  
Detection Methods ◽  
Detection Accuracy ◽  
External Diameter ◽  
Quality Detection

The traditional quality detection method for transparent Nonel tubes relies on human vision, which is inefficient and susceptible to subjective factors. Especially for Nonel tubes filled with the explosive, missed defects would lead to potential danger in blasting engineering. The factors affecting the quality of Nonel tubes mainly include the uniformity of explosive filling and the external diameter of Nonel tubes. The existing detection methods, such as Scalar method, Analysis method and infrared detection technology, suffer from the following drawbacks: low detection accuracy, low efficiency and limited detection items. A new quality detection system of Nonel tubes has been developed based on machine vision in order to overcome these drawbacks. Firstly the system architecture for quality detection is presented. Then the detection method of explosive dosage and the relevant criteria are proposed based on mapping relationship between the explosive dosage and the gray value in order to detect the excessive explosive faults, insufficient explosive faults and black spots. Finally an algorithm based on image processing is designed to measure the external diameter of Nonel tubes. The experiments and practical operations in several Nonel tube manufacturers have proved the defect recognition rate of proposed system can surpass 95% at the detection speed of 100m/min, and system performance can meet the quality detection requirements of Nonel tubes. Therefore this quality detection method can save human resources and ensure the quality of Nonel tubes.

Design and Application of Virtual Instrument Detection System for Aerodynamic Instrument

2012 ◽  
Vol 508 ◽  
pp. 166-169
Author(s):  
Ping Zhang ◽  
Kun Li ◽  
Xiao Shu Sun ◽  
Xian Wen Gao
Keyword(s):  
Virtual Instrument ◽  
Control Method ◽  
Detection System ◽  
Complex Structure ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Fuzzy Pid Control ◽  
Output Pressure ◽  
Labview Software ◽  
Detection Technology

Aerodynamic instruments are widely used in various kinds of industrial equipment, which have many types and complex structure. The traditional instrument detection methods require replacing a variety of testing equipment frequently. Their testing efficiencies are low and the testing equipments are hard to be integrated together. Virtual instrument technology is a hot detection technology in recent years with high detection accuracy and integration, which has a good solution to the problems of traditional detection methods. First of all, this paper introduces an overall design scheme for the virtual detection system. Secondly, for properties of longer response time, overshoot, and difficulties in establishing precise mathematical model of the pneumatic control system, this paper uses fuzzy PID control method to control the pneumatic output pressure. Finally, the virtual detection system is realized based on LabVIEW software platform. This system is applied to the detection of fixed-wing aircraft aerodynamic instrument, and has satisfactory results.

scholarly journals In-Service Detection and Quantification of Railway Wheel Flat by the Reflective Optical Position Sensor

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4969
Author(s):  
Run Gao ◽  
Qixin He ◽  
Qibo Feng ◽  
Jianying Cui
Keyword(s):  
High Speed ◽  
Detection System ◽  
Quantitative Relationship ◽  
Real Field ◽  
Position Sensor ◽  
Analysis Model ◽  
Railway Wheel ◽  
Optical Position ◽  
Wheel Flat ◽  
The Impact

Railway wheel tread flat is one of the main faults of railway wheels, which brings great harm to the safety of vehicle operation. In order to detect wheel flats dynamically and quantitatively when trains are running at high speed, a new wheel flat detection system based on the self-developed reflective optical position sensor is demonstrated in this paper. In this system, two sensors were mounted along each rail to measure the wheel-rail impact force of the entire circumference by detecting the displacement of the collimated laser spot. In order to establish a quantitative relationship between the sensor signal and the wheel flat length, a vehicle-track coupling dynamics analysis model was developed using the finite element method and multi-body dynamics method. The effects of train speed, load, wheel flat lengths, as well as the impact positions on impact forces were simulated and evaluated, and the measured data can be normalized according to the simulation results. The system was assessed through simulation and laboratory investigation, and real field tests were conducted to certify its validity and correctness. The system can determine the position of the flat wheel and can realize the quantification of the detected wheel flat, which has extensive application prospects.

scholarly journals A Real-Time Vehicle Detection System under Various Bad Weather Conditions Based on a Deep Learning Model without Retraining

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5731 ◽  
Author(s):  
Xiu-Zhi Chen ◽  
Chieh-Min Chang ◽  
Chao-Wei Yu ◽  
Yen-Lin Chen
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Vehicle Detection ◽  
Weather Conditions ◽  
Learning Model ◽  
Detection Methods ◽  
Computational Time ◽  
Detection Accuracy ◽  
Bad Weather ◽  
Deep Learning Model

Numerous vehicle detection methods have been proposed to obtain trustworthy traffic data for the development of intelligent traffic systems. Most of these methods perform sufficiently well under common scenarios, such as sunny or cloudy days; however, the detection accuracy drastically decreases under various bad weather conditions, such as rainy days or days with glare, which normally happens during sunset. This study proposes a vehicle detection system with a visibility complementation module that improves detection accuracy under various bad weather conditions. Furthermore, the proposed system can be implemented without retraining the deep learning models for object detection under different weather conditions. The complementation of the visibility was obtained through the use of a dark channel prior and a convolutional encoder–decoder deep learning network with dual residual blocks to resolve different effects from different bad weather conditions. We validated our system on multiple surveillance videos by detecting vehicles with the You Only Look Once (YOLOv3) deep learning model and demonstrated that the computational time of our system could reach 30 fps on average; moreover, the accuracy increased not only by nearly 5% under low-contrast scene conditions but also 50% under rainy scene conditions. The results of our demonstrations indicate that our approach is able to detect vehicles under various bad weather conditions without the need to retrain a new model.

Development of the On-Line Straightness Detection System for Shafts

2014 ◽  
Vol 490-491 ◽  
pp. 1592-1595
Author(s):  
Mei Yang ◽  
Chun Yan Tian ◽  
You Quan Chen ◽  
Cheng Da Ning
Keyword(s):  
Fourier Transform ◽  
Machine Tool ◽  
Detection System ◽  
Measuring System ◽  
Error Signal ◽  
Math Model ◽  
Measuring Mechanism ◽  
On Line ◽  
Low Efficiency ◽  
Straightness Error

Aiming at the low efficiency and precision of nowadays domestic auto-straightening machine of shafts, a new kind of shafts straightness detection system is researched and developed. The supporting part of the measuring mechanism adopts the mechanical structure of auto-going-down of straightening points supporting pedestal. The whole convolute measuring system fixes on the machine tool orbit sliding table, whose movement can change the position for the straightening point and make positioning faster and more accurate. In addition, this detection system bases on applications math and builds math model by Fourier transform which can separate the elliptical error signal, straightness error signal and impulse error signal and then gained ideal straightness error of shafts.

scholarly journals Numerical Analysis of a Mobile Leakage-Detection System for a Water Pipeline Network

2021 ◽  
Vol 87 (1) ◽  
pp. 134-150
Author(s):  
Balbir Singh ◽  
Usman Ikhtiar ◽  
Mohamad Firzan ◽  
Dong Huizhen ◽  
Kamarul Arifin Ahmad
Keyword(s):  
Drag Coefficient ◽  
Detection System ◽  
Leak Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
New Model ◽  
Pressure Drag ◽  
Pipeline Networks ◽  
Water Pipeline ◽  
And Control

The leakages in water pipeline networks sometimes negatively affect the environment, health, and economy. Therefore, leak detection methods play a crucial role in detecting and localizing leaks. These methods are categorized into internal and external detection methods, each having its advantages and certain limitations. The internal system has its detection based on the field sensors to monitor internal pipeline parameters such as temperature and pressure, thereby inferring a leak. However, the mobility of the sensing module in the pipeline is affected by the model drag coefficient. The low drag coefficient causes the module to quickly lost control in the pipeline leading to false detection. Therefore, this study is about designing and numerically analysing a new model to achieve a higher drag value of the sensing system. The drag value of various models is determined with the help of CFD simulations in ANSYS. The outcome of this study is a new model with a drag value of 0.6915. It was achieved by implementing an aerodynamic shape, a more significant surface contact area in the middle, and canted fins at the front of the . Both pressure, drag, and skin friction were increased, so a higher drag value of the sensing module can be achieved. Through this, the mobility and control of modules in the pipeline can be improved, improving leak detection accuracy.

Automobile Frame Side Rail Detection System Based on Machine Vision

2012 ◽  
Vol 605-607 ◽  
pp. 1527-1530
Author(s):  
Yu Zhuo Men ◽  
Hai Bo Yu ◽  
Hua Wang ◽  
Jin Gang Gao ◽  
Xin Pan
Keyword(s):  
Machine Vision ◽  
Detection System ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Large Size ◽  
Side Rail ◽  
On Line ◽  
Low Efficiency ◽  
System Prototype ◽  
Very High

A machine vision on-line detection system for automobile frame side fail mounting holes is proposed in this article to solve the backward and low-efficiency problems for detection methods of large-size automobile frame side fail mounting holes. Many images captured by CDD camera are processed and analyzed by virtue of algorithms such as image stitching, threshold segmentation, edge detection, feature extraction, etc.. The developed detection system prototype has very high detection accuracy.

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