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

Prof. Veena R. Pawar; Dr. Dev Ras Pandey

doi:10.32628/IJSRST52310583

Authors

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

DOI:

https://doi.org//10.32628/IJSRST52310583

Keywords:

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

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.

References

Pech, M., Vrchota, J., & Bednář, J. (2021 “Predictive maintenance and intelligent sensors in smart factories Sensors”, 21(4), 1470.
Nunes, P., Santos, J., & Rocha, E. (2023) “Challenges in predictive maintenance”–A review CIRP Journal of Manufacturing Science and Technology,40, 53-67.
Jimenez, J. J. M., Schwartz, S., Vingerhoeds, R., Grabot, B., & Salaün, M. (2020) “Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics”, Journal of Manufacturing Systems, 56, 539-557.
Theissler, A., Pérez-Velázquez, J., Kettelgerdes, M., & Elger, G. (2021) “Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry, Reliability engineering & system safety”, 215, 107864.
Kollmann, S., Estaji, A., Bratukhin, A., Wendt, A., & Sauter, T. (2020, June) “Comparison of Preprocessors for Machine Learning in the Predictive Maintenance Domain” In 2020 IEEE 29th International Symposium on Industrial Electronics (ISIE) (pp. 49- 54). IEEE.
Cofre-Martel, S., Lopez Droguett, E., & Modarres, M. (2021) “Big Machinery data preprocessing methodology for data-driven models in prognostics and health management. Sensors”,21(20), 6841.
Hung, Y. H. (2021) “Improved ensemble-learning algorithm for predictive maintenance in the manufacturing process”,Applied Sciences, 11(15), 6832.
Lee, S. Y., Connerton, T. P., Lee, Y. W., Kim, D., Kim, D., & Kim, J. H. (2022). Semi-GAN: “An improved GAN-based missing data Imputation method for the semiconductor industry”, IEEE Access, 10, 72328-72338.
Gao, L., Yu, K., & Lu, P. (2022) “Missing pavement performance data imputation using graph neural networks. Transportation research record”, 2676(12), 409-419.
Liu, Y., Dillon, T., Yu, W., Rahayu, W., & Mostafa, F. (2020) “Missing value imputation for industrial IoT sensor data with large gaps”,IEEE Internet of Things Journal, 7(8), 6855-6867.
Mir, A. A., Rafique, M., Hussain, L., Almasoud, A. S., Alajmi, M., Al-Wesabi, F. N., & Hilal, A. M. (2022), “An improved implementation method for accurately predicting imputed dataset-based radon time series”, Ieee Access, 10, 20590-20601.
El-Hasnony, I. M., Barakat, S. I., Elhoseny, M., & Mostafa, R. R. (2020). “Improved feature selection model for big data analytics”. IEEE Access, 8, 66989-67004.
Shafiq, M., Tian, Z., Bashir, A.K., Du, X., & Guizani, M. (2020) “IoT malicious traffic identification using wrapper-based feature selection Mechanisms” Computers & Security, 94, 101863.
Assagaf, I., Sukandi, A., Abdillah, A. A., Arifin, S., & Ga, J. L. (2023) “ Machine Predictive Maintenance by Using Support Vector Machines. Recent in Engineering Science and Technology”, 1(01), 31-35.
Kong, Z., Lu, Q., Wang, L., & Guo, G. (2023). :A simplified approach for data filling in incomplete soft sets. Expert Systems with Applications”, 213, 119248.
Chen, C. H., Tsung, C. K., & Yu, S. S. (2022) “ Designing a Hybrid Equipment-Failure Diagnosis Mechanism under Mixed- Type Data with Limited Failure Samples”. Applied Sciences, 12(18), 9286.
Matzka, S. (2020, September) “Explainable artificial intelligence for predictive maintenance applications”,. In 2020 third international conference on artificial intelligence for industries (ai4i) (pp. 69-74). IEEE.