Authors :
Asit Das; Karanam Srihari; Susmitha Pasupuleti; Indira Kumar; Bharani Kumar Depuru
Volume/Issue :
Volume 8 - 2023, Issue 12 - December
Google Scholar :
http://tinyurl.com/mrzaxpah
Scribd :
http://tinyurl.com/35jfkyjb
DOI :
https://doi.org/10.5281/zenodo.10477279
Abstract :
In the sphere of industrial processes, the
constant operation of machinery is paramount, and any
downtime resonates with substantial losses in
productivity, efficiency, and profitability. The industry
confronts the intricate challenge of minimizing machine
downtime attributed to breakdowns, unscheduled
maintenance, operator errors, and environmental
factors. This predicament creates a cascade of adverse
effects, diminished efficiency, missed production targets,
heightened maintenance costs, and reduced profitability.
This research paper charts a strategic roadmap
designed to address the challenge of minimizing
unplanned machine downtime. With a focus on key
objectives, including maximizing machine productivity,
minimizing downtime through root cause identification
and preventive measures, reducing maintenance costs,
and ultimately enhancing overall efficiency for improved
competitiveness and profitability. However, a set of
constraints introduces complexity to the implementation
of these objectives. Budgetary constraints, time
limitations, resource scarcity, regulatory requirements,
and operational constraints intricately weave a tapestry
that demands thoughtful navigation.
The proposed strategy encompasses a multifaceted
approach integrating preventive measures, root cause
analysis, and efficiency optimization. The paper
navigates through these strategies, taking into account
the identified constraints, offering a holistic framework
to enhance machine reliability and performance.
Through a judicious balance of technological innovation,
preventive maintenance, and operational optimization,
the industry aspires to revolutionize manufacturing
processes, mitigate downtime challenges, and emerge as
a more competitive and profitable entity in the industrial
domain.
Moreover, in the realm of machine downtime
classification, this study employs diverse models such as
Logistic Regression, Naive Bayes, K-Nearest Neighbors
(KNN), Decision Tree, AdaBoost Classifier, Gradient
Boosting, Random Forest, Extra Tree Classifier, and
HistGradient Boosting. The evaluation criteria include
accuracies, recall, precision and F1 scores, offering a
comprehensive assessment of each model's effectiveness
in predicting and preventing machine downtime.
Notably, Random Forest outperforms other models,
adding a significant layer of insight for industries
seeking efficient measures in machine downtime
management.
Keywords :
Machine Downtime Prediction, Python, PowerBI, Machine learning models, Streamlit, Predictive Maintenance, Operational Uptime Optimization, Industrial Equipment Efficiency, Production Loss Prevention, Manufacturing Productivity Enhancement.
In the sphere of industrial processes, the
constant operation of machinery is paramount, and any
downtime resonates with substantial losses in
productivity, efficiency, and profitability. The industry
confronts the intricate challenge of minimizing machine
downtime attributed to breakdowns, unscheduled
maintenance, operator errors, and environmental
factors. This predicament creates a cascade of adverse
effects, diminished efficiency, missed production targets,
heightened maintenance costs, and reduced profitability.
This research paper charts a strategic roadmap
designed to address the challenge of minimizing
unplanned machine downtime. With a focus on key
objectives, including maximizing machine productivity,
minimizing downtime through root cause identification
and preventive measures, reducing maintenance costs,
and ultimately enhancing overall efficiency for improved
competitiveness and profitability. However, a set of
constraints introduces complexity to the implementation
of these objectives. Budgetary constraints, time
limitations, resource scarcity, regulatory requirements,
and operational constraints intricately weave a tapestry
that demands thoughtful navigation.
The proposed strategy encompasses a multifaceted
approach integrating preventive measures, root cause
analysis, and efficiency optimization. The paper
navigates through these strategies, taking into account
the identified constraints, offering a holistic framework
to enhance machine reliability and performance.
Through a judicious balance of technological innovation,
preventive maintenance, and operational optimization,
the industry aspires to revolutionize manufacturing
processes, mitigate downtime challenges, and emerge as
a more competitive and profitable entity in the industrial
domain.
Moreover, in the realm of machine downtime
classification, this study employs diverse models such as
Logistic Regression, Naive Bayes, K-Nearest Neighbors
(KNN), Decision Tree, AdaBoost Classifier, Gradient
Boosting, Random Forest, Extra Tree Classifier, and
HistGradient Boosting. The evaluation criteria include
accuracies, recall, precision and F1 scores, offering a
comprehensive assessment of each model's effectiveness
in predicting and preventing machine downtime.
Notably, Random Forest outperforms other models,
adding a significant layer of insight for industries
seeking efficient measures in machine downtime
management.
Keywords :
Machine Downtime Prediction, Python, PowerBI, Machine learning models, Streamlit, Predictive Maintenance, Operational Uptime Optimization, Industrial Equipment Efficiency, Production Loss Prevention, Manufacturing Productivity Enhancement.
