Machine Learning For Bearing Fault Detection | Feature Extraction | Data Preprocessing | PART-1

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#machinelearningtutorial #machinelearning #DatadrivenPrognostics #DatadrivenFaultDiagnosis

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This video series demonstrates the use of a machine-learning algorithm to predict the faults in a bearing. This first section will see the data preprocessing part and how we can read the data from the given text file and do the feature extraction from the obtained data to get meaningful features. In this case, we have extracted time domain features from the given acceleration data and saved them in a CSV file. In the later part of this series, we will use the preprocessed data to train machine learning algorithms that will be able to predict the faults in a Bearing.

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Intelligent Machines

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sir, you applied the PCA method can u tell me how to apply the SVM method

AjaySharma-urqq

Thanks a lot for the video. Excellent work

niyongabojulius

Hi Sir, do you have feature extraction code for "Test 1" I can refer to. I am actually confused what to do when two channels are extracting data points for each bearing. You have only discussed about feature extraction of "Test 2 datasets" where only single channels was devoted to each bearings.

krishraj

Can you please check the dataset know the files are completely different showing in bearing dataset related temp and all that
If possible send the proper link of dataset which is used in videos

FizaBagwan

Thank you so much sir for this wonderful guidance, can i get that data set file on my email, their is no such a dataset file in that link

b_akashraymul

Hello Great explanations sir, could you please provide Part 2

PankajKumar-trib

why in syntax 17 just go to df2, df3 and df4, because on my laptop I can't find the directory

silvyarahmatiaraputri

can you please provide the part 2 of this section

saidadahmane

voice quality is very poor, could you please tell me what kind of data or tests were performed

ymonga

when I run it says name dataset is not defined

AjaySharma-urqq

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