A Synthetic Multivariate Refrigerator Time-Series Dataset for Predictive Maintenance

Abstract:We generated synthetic multivariate time series for 27 refrigerators with a simplified physicsinspired simulator at one-minute resolution. The simulator includes ambient-temperature variation, door use, thermostat and compressor operation, heat exchange, defrost, electrical consumption, and six progressive degradation types. Each refrigerator provides 15 to 20 sensor outputs according to its configuration. The dataset contains 7,066,161 rows in 27 time-series files and 27 failure logs. The release also includes the Python generator, refrigerator configurations, and documentation. The data can support failure prediction, degradation analysis, and learning across refrigerators with different sensor-output sets.

Machine Learning (cs.LG)

arXiv:2609.22229 [cs.LG]

(or arXiv:2609.22229v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.22229 Focus to learn more arXiv-issued DOI via DataCite

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