The integration of predictive maintenance (PdM) into industrial systems is a crucial aspect of achieving the operational goals of Industry 4.0. In the present study, we propose the development of a calibrated and interpretable machine learning model to issue multi-label predictions of key machine failure modes, with a strong emphasis on economic feasibility and reliability. We apply a forward-moving pipeline using the AI4I 2020 dataset, which includes grid search selection, cost-sensitive thresholding, and probabilistic calibration using both Brier and Expected Calibration Error (ECE) scores. We also quantify uncertainty through conformal prediction. In addition to classical classification measures, our scheme evaluates models based on life-cycle cost (LCC), payback period, and net present value (NPV), modeling the consequences of decisions based on realistic operational policies (e.g., preventive maintenance, inspection, or deferral). By comparing and contrasting a variety of algorithms, we demonstrate that prediction fidelity and economic payoff vary significantly. Notably, our methodology ensures interpretability and robustness by combining sensor noise stress testing with machine-type subgroup analysis. The results show not only higher effectiveness in terms of failure predictions, but also higher cost savings in the long term than reactive policies, making the strategic relevance of machine learning-based PdM in modern manufacturing plants.

Keywords

Predictive Maintenance, Industry 4.0, Cost-Optimized Machine Learning, Maintenance economics and Reliability engineering.