Abstract
Ultra-low temperature (ULT) freezers are used to store perishable biological contents and are among the most energy-intensive equipment in laboratory buildings, biobanks, and similar settings. To ensure reliable and efficient operation, it is essential to implement data-driven fault detection and diagnostic algorithms, along with energy optimization techniques. This study presents labelled and long-term ULT-freezer performance dataset, the first of its kind, derived from 53 ULT freezers featuring two different control strategies. The dataset comprises high-resolution historical operation data spanning up to 10 years. More than 10 attributes are recorded from the freezing chamber and critical locations in the refrigeration systems. The dataset is labelled with regular events, such as door openings, as well as fault events obtained from 46 service reports. A scalable data pipeline, consisting of extraction, transformation, and loading processes, is developed to convert the raw data into a format ready for analysis. The dataset can be utilized to support the development of data-driven models and algorithms that advance the intelligent digital operation of ULT freezers.