Authors :
Rishikant Kumar; Manmohan Mishra; Suryali Suman; Parabjot Singh Bali
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/w852yb2r
Scribd :
https://tinyurl.com/ye26539s
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR1367
Abstract :
Now, a lot of different areas need predictive
maintenance (PdM). The goal is to cut down on downtime
and make work go faster by finding out when things will
break. This study looks at how machine learning can be
used to figure out when to fix manufacturing systems. The
study is all about using old business records, monitoring
data, and upkeep records to make good prediction
models. To make prediction tools that can quickly and
accurately find places where industrial machinery might
break down, we plan to carefully use advanced machine
learning techniques such as supervised learning, time
series analysis, and anomaly detection. Our idea could
make it easier to stick to repair plans. Breakdowns would
happen less often, and overall, running costs would go
down in many fields. To prove that our expected method
for maintenance works and can be used in the real world,
we use careful case studies and thorough empirical
validations. This research is a big step toward making
models for planned maintenance, giving ways for
proactive maintenance, and improving the dependability
and efficiency of industrial systems in the real world.
Keywords :
Machine Learning, Neural Networks, Predicted Maintenance, Preventative Maintenance, Downtime Costs, Maintenance Costs, Neural Networks.
Now, a lot of different areas need predictive
maintenance (PdM). The goal is to cut down on downtime
and make work go faster by finding out when things will
break. This study looks at how machine learning can be
used to figure out when to fix manufacturing systems. The
study is all about using old business records, monitoring
data, and upkeep records to make good prediction
models. To make prediction tools that can quickly and
accurately find places where industrial machinery might
break down, we plan to carefully use advanced machine
learning techniques such as supervised learning, time
series analysis, and anomaly detection. Our idea could
make it easier to stick to repair plans. Breakdowns would
happen less often, and overall, running costs would go
down in many fields. To prove that our expected method
for maintenance works and can be used in the real world,
we use careful case studies and thorough empirical
validations. This research is a big step toward making
models for planned maintenance, giving ways for
proactive maintenance, and improving the dependability
and efficiency of industrial systems in the real world.
Keywords :
Machine Learning, Neural Networks, Predicted Maintenance, Preventative Maintenance, Downtime Costs, Maintenance Costs, Neural Networks.
