Optimizing Electrical Equipment Maintenance using Data Mining and Principal Component Analysis

In the modern industrial environment, the dependability and efficacy of electrical apparatus are essential for operational success. This paper provides a thorough methodology for enhancing maintenance using data mining tools and principal component analysis (PCA). Utilizing past performance data and maintenance records can help identify critical variables that affect equipment failures and maintenance requirements. Data mining techniques, such as clustering and classification, analyze extensive databases to uncover patterns and trends that inform maintenance schedules. PCA is used to reduce data dimensionality, facilitating the identification of the most critical factors influencing equipment performance. This decrease enhances data processing, enabling maintenance personnel to focus on critical areas that require attention. The proposed technique improves predictive maintenance capabilities while minimizing downtime and maintenance expenses. Case study results demonstrate the effectiveness of this method in extending equipment life and improving operational efficiency. These findings underscore the need to incorporate modern data analysis methods into maintenance management, facilitating more proactive and informed decision-making in the maintenance and repair of electrical equipment.