Adaptive Board-Level Functional Fault Diagnosis Using Decision Trees

Adaptive Board-Level Functional Fault Diagnosis Using Incremental Decision Trees

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems ◽

10.1109/tcad.2015.2459046 ◽

2016 ◽

Vol 35 (2) ◽

pp. 323-336 ◽

Cited By ~ 5

Author(s):

Fangming Ye ◽

Zhaobo Zhang ◽

Krishnendu Chakrabarty ◽

Xinli Gu

Keyword(s):

Fault Diagnosis ◽

Decision Trees ◽

Board Level

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Online monitoring and fault diagnosis and isolation of Valve Regulated Lead Acid batteries in Uninterruptible Power Supplies using decision trees

2020 11th International Renewable Energy Congress (IREC) ◽

10.1109/irec48820.2020.9310410 ◽

2020 ◽

Author(s):

Ahmed Badawy ◽

Mohamed El-Habrouk ◽

Ragi Ali Rifaat Hamdy ◽

Karim Hassan Youssef

Keyword(s):

Fault Diagnosis ◽

Decision Trees ◽

Online Monitoring ◽

Power Supplies ◽

Lead Acid Batteries ◽

Uninterruptible Power Supplies ◽

Uninterruptible Power ◽

Lead Acid

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Information-Theoretic Framework for Evaluating and Guiding Board-Level Functional-Fault Diagnosis

IEEE Design and Test ◽

10.1109/mdat.2014.2313080 ◽

2014 ◽

Vol 31 (3) ◽

pp. 65-75 ◽

Cited By ~ 6

Author(s):

Fangming Ye ◽

Krishnenedu Chakrabarty ◽

Zhaobo Zhang ◽

Xinli Gu

Keyword(s):

Fault Diagnosis ◽

Information Theoretic ◽

Board Level

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Fault diagnosis method of submersible screw pump based on random forest

PLoS ONE ◽

10.1371/journal.pone.0242458 ◽

2020 ◽

Vol 15 (11) ◽

pp. e0242458

Author(s):

Minzheng Jiang ◽

Tiancai Cheng ◽

Kangxing Dong ◽

Shufan Xu ◽

Yulong Geng

Keyword(s):

Fault Diagnosis ◽

Random Forest ◽

Decision Trees ◽

Processing System ◽

Random Forest Model ◽

Categorical Variables ◽

Oil Well ◽

Screw Pump ◽

Forest Model ◽

Diagnosis Method

The difficulty in directly determining the failure mode of the submersible screw pump will shorten the life of the system and the normal production of the oil well. This thesis aims to identify the fault forms of submersible screw pump accurately and efficiently, and proposes a fault diagnosis method of the submersible screw pump based on random forest. HDFS storage system and MapReduce processing system are established based on Hadoop big data processing platform; Furthermore, the Bagging algorithm is used to collect the training set data. Also, this thesis adopts the CART method to establish the sample library and the decision trees for a random forest model. Six continuous variables, four categorical variables and fault categories of submersible screw pump oil production system are used for training the decision trees. As several decision trees constitute a random forest model, the parameters to be tested are input into the random forest models, and various types of decision trees are used to determine the failure category in the submersible screw pump. It has been verified that the accuracy rate of fault diagnosis is 92.86%. This thesis can provide some meaningful guidance for timely detection of the causes of downhole unit failures, reducing oil well production losses, and accelerating the promotion and application of submersible screw pumps in oil fields.

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