Fault Diagnosis of Self-aligning Conveyor Idler in Coal Handling Belt Conveyor System by Statistical Features Using Random Forest Algorithm

2020 ◽  
pp. 207-219
Author(s):  
S. Ravikumar ◽  
V. Muralidharan ◽  
P. Ramesh ◽  
Cheran Pandian
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Statistical Features ◽  
Random Forest Algorithm ◽  
Belt Conveyor ◽  
Conveyor System

Parallelization Analysis of Dissolved Gases in Transformer Oil Based on Random Forest Algorithm

2014 ◽  
Vol 519-520 ◽  
pp. 98-101
Author(s):  
De Wen Wang ◽  
Zhi Wei Sun
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Power Transformer ◽  
Gas Analysis ◽  
Transformer Oil ◽  
Dissolved Gases ◽  
Random Forest Algorithm ◽  
Dissolved Gas ◽  
Important Method ◽  
Transformer Fault

Dissolved gas analysis (DGA) in oil is an important method for transformer fault diagnosis. This paper use random forest parallelization algorithm to analysis the dissolved gases in transformer oil. This method can achieve a fast parallel fault diagnosis for power equipment. Experimental results of the diagnosis of parallelization of random forest algorithm with DGA samples show that this algorithm not only can improve the accuracy of fault diagnosis, and more appropriate for dealing with huge amounts of data, but also can meet the smart grid requirements for fast fault diagnosis for power transformer. And this result also verifies the feasibility and effectiveness of the algorithm.

Fault diagnosis of self-aligning troughing rollers in belt conveyor system using k-star algorithm

Measurement ◽  
2019 ◽  
Vol 133 ◽  
pp. 341-349 ◽  
Author(s):  
S. Ravikumar ◽  
H. Kanagasabapathy ◽  
V. Muralidharan
Keyword(s):  
Fault Diagnosis ◽  
Belt Conveyor ◽  
Conveyor System
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