A Comparative Analysis of Data Mining Techniques for Fault Detection and Diagnosis

Fault detection and diagnosis play crucial roles in various fields, such as manufacturing, engineering, and system monitoring. These processes are essential for maintaining the reliability and efficiency of systems by identifying and addressing issues before they escalate into significant problems. To address these challenges, data mining techniques have been widely adopted to analyze extensive datasets. Data mining involves extracting useful information from large volumes of data, which can then be used to detect patterns, anomalies, and correlations that may indicate faults. This study presents a comparative examination of three commonly used data mining techniques—classification, clustering, and association rule mining—for fault detection and diagnosis. Understanding the strengths and limitations of these techniques enables practitioners to make informed decisions when selecting the most suitable data mining approach for fault detection and diagnosis tasks. This comparative analysis contributes to the advancement of fault detection and diagnosis methodologies by highlighting the contexts in which each technique excels or falls short. Ultimately, this leads to more effective and efficient fault management in complex systems, enhancing overall system performance and reducing downtime.