analiza aiqbench
This release provides a physics-informed synthetic dataset designed specifically to train machine learning models for predicting appliance failures and analyzing degradation patterns. By simulating complex sensor dynamics and failure modes, it offers researchers a scalable resource to improve predictive maintenance algorithms without relying on scarce real-world fault data.
A Synthetic Multivariate Refrigerator Time-Series Dataset for Predictive Maintenance
arXiv:2609.22229v1 Announce Type: new 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.
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
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 182 cuvinte
Subiecte conexe
Introducing Timestral
Mistral AI · 15 oct. 2026
SurvCF(t): Counterfactual Explanations for Survival Analysis in Predictive Maintenance Multivariate Time Series Data
arXiv · 21 iul. 2026
PRISM: Powerful Time Series to Image (TS2I) Representations for Multivariate Anomaly Detection
arXiv · 5 aug. 2026
Time-Series Foundation Model Embeddings for Remaining Useful Life Estimation
arXiv · 17 iun. 2026