scholarly journals Analysis and Diagnosis of Coal Shearer Machine Fault Based on Improved Support Vector Theory

2015 ◽  
Author(s):  
X. Zhang ◽  
X.M. Ma ◽  
Z.S. Yang
Keyword(s):  
Support Vector ◽  
Vector Theory ◽  
Machine Fault

scholarly journals IWSNs with On-sensor Data Processing for Machine Fault Diagnosis

2018 ◽  
Author(s):  
Liqun Hou ◽  
Junteng Hao ◽  
Yongguang Ma ◽  
Neil Bergmann
Keyword(s):  
Fault Diagnosis ◽  
Data Processing ◽  
Wavelet Transforms ◽  
High Speed ◽  
Sensor Data ◽  
Dynamic Monitoring ◽  
Support Vector ◽  
Industrial Wireless Sensor Networks ◽  
Machine Fault Diagnosis ◽  
Machine Fault

Machine fault diagnosis systems need to collect and transmit dynamic monitoring signals, like vibration and current signals, at high-speed. However, industrial wireless sensor networks (IWSNs) and Industrial Internet of Things (IIoT) are generally based on low-speed wireless protocols, such as ZigBee and IEEE802.15.4. To address this tension when implementing machine fault diagnosis applications in IIoT, this paper proposes a novel IWSN with on-sensor data processing. On-sensor wavelet transforms using four popular mother wavelets are explored for fault feature extraction, while an on-sensor support vector machine classifier is investigated for fault diagnosis. The effectiveness of the presented approach is evaluated by a set of experiments using motor bearing vibration data. The experimental results show that compared with raw data transmission, the proposed on-sensor fault diagnosis method can reduce the payload transmission data by 99.95%, and reduce the node energy consumption by about 10%, while the fault diagnosis accuracy of the proposed approach reaches 98%.

scholarly journals Rotating Machine Fault Diagnosis Based on Optimal Morphological Filter and Local Tangent Space Alignment

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Shaojiang Dong ◽  
Lili Chen ◽  
Baoping Tang ◽  
Xiangyang Xu ◽  
Zhengyuan Gao ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Tangent Space ◽  
Learning Algorithm ◽  
Support Vector ◽  
Morphological Filter ◽  
Rotating Machine ◽  
Machine Fault Diagnosis ◽  
Mode Decomposition ◽  
Machine Fault ◽  
Local Tangent

In order to identify the fault of rotating machine effectively, a new method based on the morphological filter optimized by particle swarm optimization algorithm (PSO) and the nonlinear manifold learning algorithm local tangent space alignment (LTSA) is proposed. Firstly, the signal is purified by the morphological filter; the filter’s structure element (SE) is selected by PSO method. Then the filtered signals are decomposed by the empirical mode decomposition (EMD) method, and the extract features are mapped into the LTSA to extract the character features; then the support vector machine (SVM) model is used to achieve the rotating machine fault diagnosis. The proposed method is evaluated by vibration signals measured from bearings with faults. Results show that the method can effectively remove the noise and extract the fault features, so the rotating machine fault diagnosis can be achieved effectively.

The Support Vector Machine Fault Diagnosis Algorithm Based on Cloud Computing

2014 ◽  
Vol 687-691 ◽  
pp. 761-765
Author(s):  
Chang Hong Zhang ◽  
Si Jia Cheng ◽  
Shu Hao Cao
Keyword(s):  
Support Vector Machine ◽  
Cloud Computing ◽  
Fault Diagnosis ◽  
Large Scale ◽  
Support Vector ◽  
Machine Fault Diagnosis ◽  
Diagnosis Method ◽  
Diagnosis Algorithm ◽  
Machine Fault ◽  
The Way

The paper puts forward the way to solve the problem of SVM training on the large scale firstly, Then perform the experiment to verify the feasibility of scheme. In the last section, SVM fault diagnosis method based on the Mapreduce is put forward.

scholarly journals IWSNs with On-Sensor Data Processing for Energy Efficient Machine Fault Diagnosis

2019 ◽  
Vol 15 (08) ◽  
pp. 42
Author(s):  
Liqun Hou ◽  
Junteng Hao ◽  
Yongguang Ma ◽  
Neil Bergmann
Keyword(s):  
Energy Consumption ◽  
Fault Diagnosis ◽  
Data Processing ◽  
Energy Efficient ◽  
Sensor Data ◽  
Support Vector ◽  
Font Size ◽  
Machine Fault Diagnosis ◽  
Transmission Data ◽  
Machine Fault

<span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">Machine fault diagnosis systems need to collect and transmit dynamic signals, like vibration and current, at high-speed. However, industrial wireless sensor networks (IWSNs) and Industrial Internet of Things (IIoT) are generally based on low-speed wireless protocols, such as ZigBee and IEEE802.15.4. Large amounts of transmission data will </span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">increase the energy consumption and </span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">shorten the lifetime of energy-constrained IWSN node</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">s as well</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">.</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">To address th</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">e</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">s</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">e</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US"> tension</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">s</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US"> when implementing machine fault diagnosis applications in </span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">IWSNs</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">, this paper proposes a</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">n</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">energy efficient </span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">IWSN with on-sensor data processing. On-sensor wavelet transforms using four popular mother wavelets are explored for fault feature extraction, while an on-sensor support vector machine classifier is investigated for fault diagnosis. The effectiveness of the presented approach is evaluated by a set of experiments using motor bearing vibration data. The experimental results show that compared with raw data transmission, the proposed on-sensor fault diagnosis method can reduce the payload transmission data by 99.95%, and reduce the node energy consumption by about 10%, while the fault diagnosis accuracy of the proposed approach reaches 98%.</span>

Large Tanning Machine Fault Detection Algorithm Research

2014 ◽  
Vol 602-605 ◽  
pp. 2035-2037
Author(s):  
Yi Li
Keyword(s):  
Fault Detection ◽  
Least Squares ◽  
External Environment ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Support Vector ◽  
Traditional Methods ◽  
Detection Process ◽  
Machine Fault ◽  
Random Fluctuations

In fault detection process of large tanning machine, fault signal fluctuations are susceptibly caused by interference of external environment. The traditional methods are difficult to accurately classify fault detection of such random fluctuations, resulting in latter detection with low accuracy. To avoid these shortcomings, support vector algorithm based on least squares is proposed for fault detection of large tanning machine. Experimental results show that the algorithm can improve the accuracy of fault detection.

Sign in / Sign up

Export Citation Format

Share Document