Integration of Machine Learning Algorithms in Mechatronic Systems for Predictive Maintenance Article Swipe
The integration of machine learning algorithms in mechatronic systems has emerged as a promising approach for achieving efficient and reliable predictive maintenance strategies. This abstract provides an overview of the application of machine learning techniques in mechatronic systems for predictive maintenance, highlighting the benefits, challenges, and future directions in this field. Predictive maintenance plays a crucial role in ensuring the optimal performance and longevity of mechatronic systems, such as industrial machinery, automotive systems, and robotics. Traditional maintenance approaches rely on predetermined maintenance schedules or reactive maintenance, which can result in unnecessary downtime, high maintenance costs, and unexpected failures. To address these limitations, the integration of machine learning algorithms has gained significant attention in recent years. Machine learning algorithms offer the ability to analyse large volumes of data collected from various sensors embedded in mechatronic systems. These algorithms can identify patterns, anomalies, and trends within the data, enabling predictive maintenance decisions. By utilizing historical data, machine learning algorithms can learn the normal behaviour of the system and predict potential failures or maintenance requirements in advance.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.17762/msea.v70i2.2475
- https://philstat.org/index.php/MSEA/article/download/2475/1946
- OA Status
- bronze
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4380482027
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4380482027Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.17762/msea.v70i2.2475Digital Object Identifier
- Title
-
Integration of Machine Learning Algorithms in Mechatronic Systems for Predictive MaintenanceWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-02-26Full publication date if available
- Authors
-
Rohit PandeyList of authors in order
- Landing page
-
https://doi.org/10.17762/msea.v70i2.2475Publisher landing page
- PDF URL
-
https://philstat.org/index.php/MSEA/article/download/2475/1946Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://philstat.org/index.php/MSEA/article/download/2475/1946Direct OA link when available
- Concepts
-
Mechatronics, Predictive maintenance, Machine learning, Downtime, Artificial intelligence, Preventive maintenance, Computer science, Field (mathematics), Robotics, Automotive industry, Algorithm, Engineering, Reliability engineering, Robot, Aerospace engineering, Pure mathematics, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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