Polymer extrusion filter remaining useful life dataset from contamination experiments.

Predictive Maintenance has become an essential objective in industry as it has the potential to decrease unplanned downtime, reduce inventories of replacement parts and increase operational safety, by identifying and predicting the time of failures. To develop accurate predictive maintenance systems, sufficient historical data to failure must be acquired. While such datasets are publicly available for components (i.e. bearing and motors), their public availability for industrial manufacturing systems is limited. This article presents a time-series dataset for the run-to-failure of single-screw polymer extruder filters. The dataset includes the temperatures along the barrel of the extruder, screw motor voltage and current at 1 Hz. The current intake of the extruder is also provided and acquired at 1 kHz, to support feature engineering works. Data is collected for 60 run-to-failures during the extrusion of low-density polyethylene. Two failure modes were recorded: full filter clogging, preventing the extrusion of the material, and filter failure, where the material accumulates until the filter bursts. The established dataset can be used to develop and verify state-of-the-art predictive maintenance models under industrial conditions, physics-based models, feature engineering and multi-rate data fusion methodologies.