Manuscript Number : IJSRST52310583

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

Authors(2) :-Ms. Veena R. Pawar, Dr. Dev Ras Pandey

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.

Authors and Affiliations

Ms. Veena R. Pawar
Department of Computer Science and Engineering, Kalinga University, Naya Raipur, Chhattisgarh, India
Dr. Dev Ras Pandey
Department of Computer Science and Engineering, Kalinga University, Naya Raipur, Chhattisgarh, India

Predictive Maintenance, Machine Learning, Preprocessing, Imputation, Feature Selection, Normalization

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Publication Details

Published in : Volume 10 | Issue 6 | November-December 2023
Date of Publication : 2023-12-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 234-248
Manuscript Number : IJSRST52310583
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Ms. Veena R. Pawar, Dr. Dev Ras Pandey, " AER-HYBRITECH: Averaging Ensemble Regression with Hybrid Encoding and Enhanced Feature Selection Technique for Predictive Maintenance", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 10, Issue 6, pp.234-248, November-December-2023. Available at doi : https://doi.org/10.32628/IJSRST52310583    
Journal URL : https://ijsrst.com/IJSRST52310583
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