Predictive maintenance in industrial systems using data mining with fuzzy logic systems (Record no. 22716)
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| fixed length control field | a |
| 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<br/>strategy to minimize downtime and optimize operational efficiency.<br/>This study explores the utilization of data mining techniques,<br/>specifically fuzzy logic systems, for predictive maintenance. The<br/>background section examines the importance of predictive<br/>maintenance in industrial contexts and highlights the limitations of<br/>traditional approaches. The methodology section outlines the process<br/>of employing fuzzy logic systems for predictive maintenance, including<br/>data preprocessing, feature selection, fuzzy rule generation, and model<br/>evaluation. The contribution of this research lies in providing a<br/>comprehensive framework for implementing predictive maintenance<br/>using fuzzy logic systems, offering insights into the integration of data<br/>mining techniques with industrial systems. Results demonstrate the<br/>effectiveness of the proposed methodology in accurately predicting<br/>maintenance needs and minimizing unplanned downtime. Findings<br/>suggest that fuzzy logic systems can enhance predictive maintenance<br/>capabilities by handling uncertainties and vagueness inherent in<br/>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 | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
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| Dewey Decimal Classification | School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 25/04/2025 | 2025-0665 | 25/04/2025 | 25/04/2025 | Articles Abstract Database |