scholarly journals Support Vector Machine for Machine Fault Diagnosis and Prognosis

2008 ◽  
Vol 2 (1) ◽  
pp. 12-23 ◽  
Author(s):  
Bo-Suk YANG ◽  
Achmad WIDODO
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Support Vector ◽  
Diagnosis And Prognosis ◽  
Machine Fault Diagnosis ◽  
Machine Fault ◽  
Fault Diagnosis And Prognosis

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 Special Issue “Advances in Machine Learning and Deep Learning Based Machine Fault Diagnosis and Prognosis”

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 532
Author(s):  
Mohand Djeziri ◽  
Marc Bendahan
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Special Issue ◽  
Maintenance Strategies ◽  
Corrective Maintenance ◽  
Diagnosis And Prognosis ◽  
Machine Fault Diagnosis ◽  
Failure Prognosis ◽  
Fault Diagnosis And Prognosis

Fault diagnosis and failure prognosis aim to reduce downtime of the systems and to optimise their performance by replacing preventive and corrective maintenance strategies with predictive or conditional ones [...]

Machine Fault Diagnosis and Prognosis using Self-Organizing Map

2014 ◽  
pp. 129-148
Author(s):  
Kesheng Wang ◽  
Zhenyou Zhang ◽  
Yi Wang
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Manufacturing Systems ◽  
Maintenance Scheduling ◽  
Self Organizing Map ◽  
Corrective Maintenance ◽  
Diagnosis And Prognosis ◽  
Machine Fault ◽  
Fault Diagnosis And Prognosis ◽  
Self Organizing

This chapter proposes a Self-Organizing Map (SOM) method for fault diagnosis and prognosis of manufacturing systems, machines, components, and processes. The aim of this work is to optimize the condition monitoring of the health of the system. With this method, manufacturing faults can be classified, and the degradations can be predicted very effectively and clearly. A good maintenance scheduling can then be created, and the number of corrective maintenance actions can be reduced. The results of the experiment show that the SOM method can be used to classify the fault and predict the degradation of machines, components, and processes effectively, clearly, and easily.

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%.

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