modules for prediction-based operation control. This
is required to prevent machine detentions in timer-
less work allocations.
The algorithm described in this research is a
method towards a reliable and versatile method for
electrical machine failure prediction, which may be
applied to a variety of faults. This research suggests
an innovative Deep-learning method for electrical
machine preventive maintenance. As a novel idea,
this one may require further development and
investigation. This method reduces electrical
downtime by using the current characteristic
variances that result from failure log analysis in
electric devices. Future studies will also involve
applying this technique to more complex defects and
larger data samples.
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WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS
DOI: 10.37394/23209.2023.20.46
Sumit Kumar, Rakesh Ranjan,
Bhupati, Harish Dutt Sharma, Yogesh Misra