Predictive Power Machine Learning Models For Machine Failure Status

Abstract

Predictive Maintenance Has Emerged As A Cornerstone For Maximizing Operational Efficiency And Minimizing Unexpected Machine Breakdowns In Industrial Environments. This Paper Presents An Integrated End-to-end Machine Learning Framework For Forecasting Machine Failure Status Using Ensemble Classification Algorithms—Random Forest, Gradient Boosting, And Naïve Bayes. The System Addresses Critical Limitations Of Legacy Threshold-based Monitoring And Uncalibrated Deep Learning Models, Specifically Targeting Overconfidence And Poor Confidence Separation Between Correctly Classified And Misclassified Samples. A Structured Preprocessing Pipeline Handles Missing Values, Duplicates, And Class Imbalance Via RandomOverSampler. Models Are Validated On An 80:20 Training-testing Split With Accuracy, Precision, Recall, And F1-score Benchmarks. The Best-performing Model Is Deployed Through A Django-based Web Application Enabling Real-time Sensor Input And Failure Prediction For Non-technical Operators. Experimental Results Demonstrate High Classification Accuracy With A Widened Confidence Gap, Making The System A Reliable Solution For Manufacturing, Energy, And Transportation Sectors.

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