Broken Steel Wire Rope Inspection Based on RBF Neural Network

2012 ◽  
Vol 614-615 ◽  
pp. 1734-1737 ◽  
Author(s):  
Zhen Chen ◽  
Yan Mei Liu
Keyword(s):  
Neural Network ◽  
Detection System ◽  
Rbf Neural Network ◽  
Steel Wire ◽  
Wire Rope ◽  
Experimental Results ◽  
Quantitative Detection ◽  
Design Scheme ◽  
Steel Rope ◽  
Steel Wires

In view of the problem of low detection precision exists in traditional quantitative detection system of broken wire for steel rope, the paper proposed a design scheme of quantitative detection system of broken wire for steel rope based on RBF neural network. The RBF Neural Network was introduced to establish the model of discerning quantitative broken steel wires. Experimental results showed that the method performed well in the identification of damage.

Rolling/Sliding Wear Behavior of High Carbon Steel Wire Rod (HSWR) of Traction Machine

2008 ◽  
Author(s):  
Beom-Taek Jang ◽  
Seock-Sam Kim
Keyword(s):  
Wear Behavior ◽  
Steel Wire ◽  
High Carbon Steel ◽  
Wire Rope ◽  
Wear Volume ◽  
Steel Wires ◽  
Volume Curve ◽  
Wide Range ◽  
Lubrication Condition ◽  
Worn Surface

Steel wires are critical load-bearing components in a wide range of applications such as elevator, cranes, mine haulage etc. The traction machine of elevator which transmits power to wire rope causes micro-slip between wire rope and sheave during reciprocating action. The lubrication condition of wire rope is also changed due to the lack of grease. This study focuses on the wear behavior of steel wire and effect of both dry and grease conditions by using the rolling/sliding contact wear tester done under various slip ratios and rolling speeds. The experimental results of the wear volume curve against the number of revolutions under the grease condition are compared with the results under dry condition. The worn surface of steel wire and the size of wear particles were observed by SEM. In order to quantify the wear amount of steel wire we established an equation and finally obtained the wear coefficient.

Shearing and Shaving Process of Stainless Steel Wire at Micro Scale

2020 ◽  
Vol 311 ◽  
pp. 74-79
Author(s):  
Chao Cheng Chang ◽  
Tzu Hsiang Hung ◽  
Jung Shu Chang
Keyword(s):  
Stainless Steel ◽  
Steel Wire ◽  
Load Cell ◽  
Experimental Results ◽  
Stainless Steel Wire ◽  
Displacement Sensor ◽  
Micro Scale ◽  
Steel Wires ◽  
Sheared Edge ◽  
Die Set

This study investigated the effects of the die clearance on the shearing and shaving processes of the stainless steel (SUS316LVM) wire at micro scale. A die set was developed and installed on a precession press equipped with a load cell and a displacement sensor to conduct experiments. By using different punches in the same die set, the specimens prepared from 316LVM stainless steel wires with 0.5 mm diameter were first sheared and then shaved. Experimental results show that the burnished area of the sheared edge increases with the reduction of the clearance between the punch and die in the shearing process. The clearance also significantly affects the load curves. Moreover, the shaving process does increase the burnished area on the shaved edge of the specimen. By an appropriate feed in the shaving process, it is possible to trim the extra material from the sheared edge that results in a nearly complete burnished surface on the shaved edge of the stainless steel wire. This research provides a basis for understanding of the die clearance effect on the shearing and shaving processes at micro scale.

Research on Wearable Knee Angle Detection System Based on RBF Neural Network

2021 ◽  
pp. 484-491
Author(s):  
Yajing Guo ◽  
Fan Yang ◽  
Hao Wang ◽  
Qing Zhao ◽  
Shuxuan Liu
Keyword(s):  
Neural Network ◽  
Detection System ◽  
Rbf Neural Network ◽  
Knee Angle

The Research of Intrusion Detection System Based on ANN on Cloud Platform

2012 ◽  
Vol 263-266 ◽  
pp. 2962-2965
Author(s):  
Xue Song Jiang ◽  
Xiu Mei Wei ◽  
Yu Shui Geng
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Rbf Neural Network ◽  
Back Propagation ◽  
Cloud Platform ◽  
Bp Network ◽  
Rbf Network ◽  
Learning Speed

Intrusion detection system (IDS) can find the intrusion information before the computer be attacked, and can hold up and response the intrusion in real time. Artificial neural network algorithms play a key role in IDS. The intrusion detection system (ANN) algorithms can analyze the captured data and judge whether the data is intrusion. In this paper we used Back Propagation (BP) network and Radical Basis Function (RBF) network to the IDS. The result of the experiment improve that The RBF neural network is better than BP neural network in the ability of approximation, classification and learning speed. During the procedure there is a large amount of computes. On cloud platform the calculation speed has been greatly increased. So that we can find the invasion more quickly and do the processing works accordingly.

INTELLIGENT BREAST TUMOR DETECTION SYSTEM WITH TEXTURE AND CONTRAST FEATURES

2013 ◽  
Vol 25 (03) ◽  
pp. 1350008 ◽  
Author(s):  
Szu-Yin Wu ◽  
Chiun-Li Chin ◽  
Yu-Shun Cho ◽  
Yen-Ching Chang ◽  
Li-Pin Hsu
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Feature Extraction ◽  
Mortality Rate ◽  
Breast Tumor ◽  
Learning Algorithm ◽  
Detection System ◽  
Tumor Detection ◽  
Experimental Results ◽  
World Health

According to a research report by the World Health Organization (WHO), breast cancer is the most common type of cancer in women, while the mortality rate of breast cancer of females over 40 years old is extremely high. If detected early, it can be treated early, and the mortality rate of breast cancer can be reduced. Meanwhile, the image processing and pattern recognition technology has been adopted to select suspicious regions, provides alerts to assist in doctors' diagnosis, and reduces misdiagnosis rates due to fatigue of doctors, and improves diagnostic accuracy. Hence, this paper proposed an intelligent breast tumor detection system with texture and contrast features. This system consists of three parts: preprocessing, feature extraction, and learning algorithm. The goal of preprocessing is to obtain a good image quality and a real breast area. In the feature extraction, we extract the two features to describe the breast tumor. These features include Laws' Mask features which are the representation of the texture and modification average distance (MAD) feature which is the representation of the contrast. Each region of interest (ROI) image block will be extracted by these two features. And we will extract useful feature from all extracted features. We hope that a small quantity of feature can be used in our proposed system. Next, we use neural network as learning algorithm to detect the tumor with extracted features. Finally, in the experimental results, we use three databases to verify our proposed system, and two radiologists participated in that process and designed final verification study. Thus, we understand from the experimental results that a detection rate as high as 98% can be achieved by using only a few features and the simplest artificial neural network rather than a large number of features and a complex classifier. The success of the system will improve the accuracy of the existing detection methods, assist medical diagnosis, and decrease the time of the judgment effective by doctors.

scholarly journals PENGARUH CAIRAN ALUMINIZING TERHADAP KEKERASAN KAWAT BAJA

2017 ◽  
Vol 3 (2) ◽  
Author(s):  
Dwi Putranto N ◽  
Dody Prayitno
Keyword(s):  
Room Temperature ◽  
Steel Wire ◽  
Intermetallic Layer ◽  
Hardness Test ◽  
Wire Rope ◽  
Heavy Load ◽  
Steel Wires ◽  
The Wire ◽  
Wire Strand ◽  
The Impact

Wire rope is made from several steel  wires a combined form a strand, a couple of strands twisted around the core to form a steel rope. One example of its usage is on the bridge to provide support for a heavy load. The steel wire is composed of several parts that is, steel wire, core and wire strand. Increasing the hardness of steel wire have the impact, the hardness of the steel wire. In an effort to improve the hardness of steel wire, there are opportunities to increase the hardness of steel wire with aluminizing method. The aim of this research is to find out the hardness of Wire in aluminizing process with alloys Al - Cu - Sn. Moreover to the research also aims to focus on the addition of Sn element in Al - Cu liquid. The methodology research was preceded by spliting wires from the wire rope. After that cut the wire into the sample wire. Then soak the wire into Al - Cu – Sn liquid at a temperature of 700ºC for ± 3 minutes. Elements of Sn which contained in the composition of Al - Cu - Sn vary from 0 % , 10 % and 20 % , while for CU’s component is 10 % and the rest of is Al, and the latter only elements of Al - Sn, without adding Cu element. Wire samples were then take away and cooled at room temperature, then test the wire by using micro hardness test, the test data was analyzed with Anova and finally made a conclusion. The results of this of this research showed that for the violence that occurs in the intermetallic layer shows the increase in value of hardness obtained on steel wire.

scholarly journals Study on the Rock Fall Detection System using Steel Wire Cable Sensor and Satellite Phone-Experimental Results on the Applicability for a Cropped Slope-

2003 ◽  
Vol 44 (4) ◽  
pp. 223-233
Author(s):  
Fumito ITO ◽  
Masuyuki UJIHIRA ◽  
Masayuki YASUIKE ◽  
Yohei KAWAMURA ◽  
HIGUCHI Kiyoshi ◽  
...  
Keyword(s):  
Detection System ◽  
Steel Wire ◽  
Fall Detection ◽  
Experimental Results ◽  
Rock Fall
Sign in / Sign up

Export Citation Format

Share Document