Authors :
Dr. T. Amalraj Victoire; N. Ruthri; P. Santhiya
Volume/Issue :
Volume 8 - 2023, Issue 7 - July
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/yck4uwyr
DOI :
https://doi.org/10.5281/zenodo.8157870
Abstract :
Predictive maintenance has emerged as a
powerful approach to optimize the maintenance of
complex systems by leveraging data-driven techniques.
Machine learning, in particular, has played a significant
role in advancing predictive maintenance capabilities,
enabling proactive identification of potential failures and
optimizing maintenance schedules. We discuss the
fundamental concepts of predictive maintenance, the
application of machine learning algorithms, and the
integration of data sources for accurate failure
prediction. Additionally, we explore various techniques
for feature engineering, anomaly detection, fault
diagnosis, and remaining useful life estimation.
Furthermore, we address the challenges associated with
implementing machine learning in real-world predictive
maintenance scenarios, including data quality,
interpretability, scalability, and the need for domain
expertise. We also discuss emerging trends, such as the
incorporation of deep learning, edge computing, and
explainable AI, that have the potential to further
enhance the effectiveness of machine learning in
predictive maintenance. Finally, we outline future
directions and potential research areas for advancing the
field, including the integration of sensor technologies, the
use of hybrid models, and the development of
standardized frameworks.
Keywords :
Predictive Maintenance, Machine Learning, Fault Prediction, Feature Engineering, Explainable AI.
Predictive maintenance has emerged as a
powerful approach to optimize the maintenance of
complex systems by leveraging data-driven techniques.
Machine learning, in particular, has played a significant
role in advancing predictive maintenance capabilities,
enabling proactive identification of potential failures and
optimizing maintenance schedules. We discuss the
fundamental concepts of predictive maintenance, the
application of machine learning algorithms, and the
integration of data sources for accurate failure
prediction. Additionally, we explore various techniques
for feature engineering, anomaly detection, fault
diagnosis, and remaining useful life estimation.
Furthermore, we address the challenges associated with
implementing machine learning in real-world predictive
maintenance scenarios, including data quality,
interpretability, scalability, and the need for domain
expertise. We also discuss emerging trends, such as the
incorporation of deep learning, edge computing, and
explainable AI, that have the potential to further
enhance the effectiveness of machine learning in
predictive maintenance. Finally, we outline future
directions and potential research areas for advancing the
field, including the integration of sensor technologies, the
use of hybrid models, and the development of
standardized frameworks.
Keywords :
Predictive Maintenance, Machine Learning, Fault Prediction, Feature Engineering, Explainable AI.
