AER-HYBRITECH: Averaging Ensemble Regression with Hybrid Encoding and Enhanced Feature Selection Technique for Predictive Maintenance

Abstract

Predictive maintenance is critical to modern industrial operations, preventing unexpected equipment failures and minimizing downtime. Existing methods often encounter challenges related to data preprocessing, missing data imputation, and feature selection. This paper presents "AER-HYBRITECH," a novel approach that addresses these challenges and enhances the predictive maintenance process. Traditional methods overlook the intricate relationships within the data, resulting in suboptimal predictive performance. To bridge this gap, the proposed AER-HYBRITECH algorithm is introduced. AER-HYBRITECH stands out in several ways. Firstly, it utilizes a hybrid encoding technique that converts categorical data into a more informative numerical representation by incorporating the average values of label-encoded data and its frequency, leading to improved feature utilization. Furthermore, it introduces the AER-MDI (Averaging Ensemble Regression-based Missing Data Imputation) technique, which combines M5P, REPTree, and linear regression models to impute missing data, ensuring a more complete dataset. The algorithm also implements Min-Max normalization to scale numeric features, making them compatible for further analysis. One of the key innovations of AER-HYBRITECH is its enhanced hybrid feature selection (EHFS) approach. The AER-HYBRITECH algorithm transforms and preprocesses the data and ensures that predictive maintenance models are built on a solid foundation, resulting in more accurate predictions and reduced maintenance costs.