| --- |
| pretty_name: "HUST Bearing Fault (TsFile)" |
| modality: timeseries |
| authors: "odysseywt" |
| task_categories: |
| - time-series-forecasting |
| size_categories: |
| - 10M<n<100M |
| tags: |
| - tsfile |
| - timeseries |
| - modality:timeseries |
| - format:tsfile |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: "hust_*.tsfile" |
| --- |
| |
| # HUST Bearing Fault (TsFile) |
|
|
| This dataset is an Apache TsFile conversion of |
| [`odysseywt/HUST`](https://huggingface.co/datasets/odysseywt/HUST). |
|
|
| Modalities: Time-series. |
|
|
| ## Overview |
|
|
| - Huazhong University of Science and Technology (HUST) bearing fault dataset. |
| - Vibration signals across fault types, bearing types, and load conditions. |
| - Filename/fault/bearing/load metadata are device TAGs; `vibration_raw` is the measurement. |
|
|
| - Converted observations: 48,883,200 rows across 10 TsFile file(s) |
| - Source format: csv |
|
|
| ## TsFile schema |
|
|
| - **Time** — sample index within each segment, stored as INT64 milliseconds. |
|
|
| | Column | Role | Type | Meaning | |
| |---|---|---|---| |
| | `Time` | TIME | INT64 (ms) | sample timestamp | |
| | `filename` | TAG | STRING | source file | |
| | `fault_type` | TAG | STRING | fault type | |
| | `bearing_type` | TAG | STRING | bearing type | |
| | `load_condition` | TAG | STRING | load | |
| | `segment_idx` | TAG | STRING | — | |
| | `label` | TAG | STRING | — | |
| | `segment_id` | TAG | STRING | segment id | |
| | `vibration_raw` | FIELD | FLOAT | amplitude | |
|
|
| ## Conversion notes |
|
|
| - Metadata columns kept as TAGs; `vibration_raw` as FLOAT FIELD. |
| - Large source split into sharded tsfiles (row-group sharding); source `features` column dropped. |
|
|
| ## Source & license |
|
|
| - Original dataset: https://huggingface.co/datasets/odysseywt/HUST |
| - Author / publisher: odysseywt |
| - License: mit |
|
|
| ## Usage |
|
|
| Install the Apache TsFile Python SDK (`pip install tsfile`) and read a converted file: |
|
|
| ```python |
| from pathlib import Path |
| from tsfile import TsFileReader |
| |
| path = Path("hust_1.tsfile") |
| with TsFileReader(str(path)) as reader: |
| schemas = reader.get_all_table_schemas() |
| print("tables:", list(schemas)) |
| table_name = next(iter(schemas)) |
| table = schemas[table_name] |
| columns = [column.get_column_name() for column in table.get_columns()] |
| print("columns:", columns) |
| field_names = [ |
| column.get_column_name() |
| for column in table.get_columns() |
| if column.get_column_name() not in {"Time", "time"} |
| ] |
| if field_names: |
| with reader.query_table(table_name, field_names[:3], batch_size=1024) as result: |
| batch = result.read_arrow_batch() |
| if batch is not None: |
| print(batch.to_pandas().head()) |
| ``` |
|
|
