000			a
999			_c22716 _d22716
003			OSt
005			20250425093428.0
008			250425b xxu||||| |||| 00| 0 eng d
040			_aAIKTC-KRRC _cAIKTC-KRRC
100			_925988 _aSelvalakshmi, B.
245			_aPredictive maintenance in industrial systems using data mining with fuzzy logic systems
250			_aVol.14(4), Apr
260			_aChennai _bICT Academy _c2024
300			_a3361-3367p.
520			_aIn 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	_94622 _aComputer Engineering
700			_925989 _aVijayalakshmi, P.
773	0		_dChennai ICT Academy _tICTACT Journal on Soft Computing (IJSC)
856			_uhttps://ictactjournals.in/paper/IJSC_Vol_14_Iss_4_Paper_9_3361_3367.pdf _yClick here
942			_2ddc _cAR