Whole process fault diagnosis and prognosis of complex equipments in whole life-circle

2013 ◽  
pp. 1003-1006
Author(s):  
Rui Guo ◽  
Guo-Qiang Wang ◽  
Guang-Yao Lian ◽  
Shi-Lei Li
Keyword(s):  
Fault Diagnosis ◽  
Whole Process ◽  
Diagnosis And Prognosis ◽  
Process Fault Diagnosis ◽  
Fault Diagnosis And Prognosis ◽  
Life Circle

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 [...]

Self-Powered RFID Sensor Tag for Fault Diagnosis and Prognosis of Transformer Winding

2017 ◽  
Vol 17 (19) ◽  
pp. 6418-6430 ◽  
Author(s):  
Tao Wang ◽  
Yigang He ◽  
Qiwu Luo ◽  
Fangming Deng ◽  
Chaolong Zhang
Keyword(s):  
Fault Diagnosis ◽  
Diagnosis And Prognosis ◽  
Transformer Winding ◽  
Self Powered ◽  
Fault Diagnosis And Prognosis

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.

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