Prompt and precise diagnosis and prognosis of equipment failures are essential for industrial predictive maintenance (PdM), ensuring operational efficiency, minimizing downtime, and reducing costs. This study enhances PdM systems using Autoregressive Moving Average-Generalized Autoregressive Conditional Heteroskedasticity (ARMA-GARCH) models, comparing them with deep learning (DL) models. The ARMA-GARCH model offers a robust data-driven solution by capturing the temporal dependencies and volatility patterns in industrial sensor data, focusing on returns rather than raw values. This approach normalizes data, improves the detection of relative changes, and models volatility patterns effectively. Utilizing a publicly available dataset from Microsoft Azure, the ARMA-GARCH model’s effectiveness is empirically validated. The results demonstrate the ARMA-GARCH model’s utility in enhancing PdM capabilities, optimizing maintenance strategies, reducing unplanned downtime, and improving operational reliability in industrial settings. Additionally, the study emphasizes the importance of explainability in PdM, despite the high accuracy of DL predictions.