Predictive Maintenance Scheduling for Heavy Manufacturing Equipment Based on Sensor Degradation Curves

Unplanned downtime in heavy manufacturing environments inflicts severe financial losses,
compromises worker safety, and disrupts production schedules. Traditional preventive
maintenance approaches rely on static, time-based overhaul intervals that fail to account for
operational stress variations, leading to either premature component replacement or
catastrophic in-service failures. This paper establishes an automated, real-time predictive
maintenance scheduling architecture that estimates equipment Remaining Useful Life
(RUL) by modeling non-linear sensor degradation curves via stochastic Wiener and Gamma
degradation processes. Our framework extracts multi-sensor Industrial Internet of Things
(IIoT) telemetry including tri-axial vibration acceleration, thermal dissipation deltas,
acoustic emissions, and lubricant particle counts to parameterize dynamic health indices.
Evaluated on an empirical dataset of 1,250 heavy industrial assets (CNC milling centers,
hydraulic presses, steam turbines, and conveyor drives) tracking 85 million sensor readings
across six manufacturing plants between 2020 and 2026, our stochastic RUL scheduling
model achieved a Mean Absolute Error (MAE) of 4.2 operating days in RUL prediction, an
Area Under the ROC Curve (AUROC) of 0.942 for early failure detection, and reduced
unplanned equipment downtime by 48.5%, generating $22.4M in net annual maintenance
cost savings. We present an exhaustive quantitative synthesis incorporating 10 data tables,
10 mathematical behavior graphs, and 20 distinct chart architectures detailing degradation
paths, opportunistic scheduling yield, and reliability capital optimization.