Longitudinal Multi-Source Monitoring Dataset for Power Transformer Health Index and RUL Prediction 机器收录·待核验

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The current dataset presents longitudinal monitoring data and engineered features for 166 oil immersed power transformers over a period of 10 years. The dataset is divided into two separate CSV files that serve to support predictive maintenance modeling, condition assessment and asset management analysis as follows:

File 1: Basic Features. This file consists of historical field data at a component level that include multi-source diagnostic parameters. These consist of Dissolved Gas Analysis (DGA), oil quality parameters, paper insulation degradation parameters and operational temperature parameters, thus providing a versatile baseline of physical state representation.

File 2: Engineered Feature Dataset. This file contains the baseline real world data alongwith engineered features created through this research. This includes health index derivative features, multi-variable thermal stress interaction features and filtration based historical maintenance variables.

The dataset serves to train algorithms, test robustness in data sparse conditions and LCC optimization applications.

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落地页

https://zenodo.org/records/20970479

许可证

CC-BY-4.0 (置信:verified_official)

发布年份

2026

发布方

Zenodo

分发点