Comparison of Different Machine Learning Algorithms for Predictive Maintenance

It is common to utilize manufacturing equipment without a clear maintenance plan. Such a method typically results in unplanned downtime due to unforeseen breakdowns. By replacing parts frequently as part of scheduled maintenance, unplanned equipment failures are avoided. However, this results in mor...

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Bibliographic Details
Published in2023 International Conference for Advancement in Technology (ICONAT) pp. 1 - 7
Main Authors Dhanraj, Dubey, Sharma, Arjun, Kaur, Gagandeep, Mishra, Sashikala, Naik, Pranav, Singh, Anshita
Format Conference Proceeding
LanguageEnglish
Published IEEE 24.01.2023
Subjects
Artificial Intelligence
Classification
Costs
Electric breakdown
Industries
Machine Learning
Machine learning algorithms
Manufacturing
Prediction algorithms
Predictive maintenance
Regression
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Summary:It is common to utilize manufacturing equipment without a clear maintenance plan. Such a method typically results in unplanned downtime due to unforeseen breakdowns. By replacing parts frequently as part of scheduled maintenance, unplanned equipment failures are avoided. However, this results in more downtime and more expensive maintenance. Predictive maintenance helps avoiding such circumstances on prior basis for smooth functioning of industry. Predictive maintenance strategies that assist lower the cost of downtime and raise the availability (utilization rate) of industrial equipment are getting more attention In this paper study of AI-based algorithms for preventative maintenance keep an eye on two essential parts of machine systems: machine failure and the quality of tools. A data-driven modelling approach will be described for the investigation of tool wear and bearing failures.
DOI:10.1109/ICONAT57137.2023.10080334