Predictive maintenance in industrial systems using data mining with fuzzy logic systems (Record no. 22716)
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| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250425093428.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250425b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 25988 |
| Author | Selvalakshmi, B. |
| 245 ## - TITLE STATEMENT | |
| Title | Predictive maintenance in industrial systems using data mining with fuzzy logic systems |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.14(4), Apr |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Chennai |
| Name of publisher, distributor, etc. | ICT Academy |
| Year | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 3361-3367p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | 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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4622 |
| Topical term or geographic name entry element | Computer Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 25989 |
| Co-Author | Vijayalakshmi, P. |
| 773 0# - HOST ITEM ENTRY | |
| Place, publisher, and date of publication | Chennai ICT Academy |
| Title | ICTACT Journal on Soft Computing (IJSC) |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://ictactjournals.in/paper/IJSC_Vol_14_Iss_4_Paper_9_3361_3367.pdf |
| Link text | Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Permanent Location | Current Location | Shelving location | Date acquired | Barcode | Date last seen | Price effective from | Koha item type |
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| School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 2025-04-25 | 2025-0665 | 2025-04-25 | 2025-04-25 | Articles Abstract Database |