Predictive maintenance in industrial systems using data mining with fuzzy logic systems
By: Selvalakshmi, B
.
Contributor(s): Vijayalakshmi, P.
Publisher: Chennai ICT Academy 2024Edition: Vol.14(4), Apr.Description: 3361-3367p.Subject(s): Computer EngineeringOnline resources: Click here
In:
ICTACT Journal on Soft Computing (IJSC)Summary: In industrial systems, predictive maintenance has emerged as a crucial
strategy to minimize downtime and optimize operational efficiency.
This study explores the utilization of data mining techniques,
specifically fuzzy logic systems, for predictive maintenance. The
background section examines the importance of predictive
maintenance in industrial contexts and highlights the limitations of
traditional approaches. The methodology section outlines the process
of employing fuzzy logic systems for predictive maintenance, including
data preprocessing, feature selection, fuzzy rule generation, and model
evaluation. The contribution of this research lies in providing a
comprehensive framework for implementing predictive maintenance
using fuzzy logic systems, offering insights into the integration of data
mining techniques with industrial systems. Results demonstrate the
effectiveness of the proposed methodology in accurately predicting
maintenance needs and minimizing unplanned downtime. Findings
suggest that fuzzy logic systems can enhance predictive maintenance
capabilities by handling uncertainties and vagueness inherent in
industrial data.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2025-0665 |
In industrial systems, predictive maintenance has emerged as a crucial
strategy to minimize downtime and optimize operational efficiency.
This study explores the utilization of data mining techniques,
specifically fuzzy logic systems, for predictive maintenance. The
background section examines the importance of predictive
maintenance in industrial contexts and highlights the limitations of
traditional approaches. The methodology section outlines the process
of employing fuzzy logic systems for predictive maintenance, including
data preprocessing, feature selection, fuzzy rule generation, and model
evaluation. The contribution of this research lies in providing a
comprehensive framework for implementing predictive maintenance
using fuzzy logic systems, offering insights into the integration of data
mining techniques with industrial systems. Results demonstrate the
effectiveness of the proposed methodology in accurately predicting
maintenance needs and minimizing unplanned downtime. Findings
suggest that fuzzy logic systems can enhance predictive maintenance
capabilities by handling uncertainties and vagueness inherent in
industrial data.
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