Machine Learning-Based Predictive Maintenance Classification for Industrial Equipment Failure Detection
DOI:
https://doi.org/10.5281/Keywords:
Machine Learning, Industrial Machinery, Predictive Maintenance, Condition Monitoring, Failure PredictionAbstract
Industrial equipment optimization is crucial in modern industries for boosting operating efficiency, decreasing downtime, and minimizing maintenance costs. Predictive maintenance helps achieve these goals by predicting equipment failures and scheduling repairs using machine learning. For the purpose of detecting machine failures, this study employs machine learning-based predictive maintenance using operational data from a subset of industrial equipment included in the AI4I 2020 Predictive Maintenance dataset. Data is cleaned, outliers are detected and removed, One-Hot Encoding is applied, and class balancing is achieved utilizing SMOTE techniques to improve data quality, generate a more balanced class distribution, and improve the performance of the predictive model. Classification of industrial equipment failures was investigated using a variety of models, including Decision Tree (DT), Logistic Regression (LR), CNN, K-Nearest Neighbors (KNN), Random Forest and XGBoost. The testing findings showed that out of all the models tested, XGBoost achieved 98.4% accuracy and Random Forest 98.3%. Overall, the results showed the high accuracy of predictive maintenance and detection of industrial equipment failures that can be achieved using advanced ensemble and deep learning methods.
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