Datasets:

THULab
/

hust

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.

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:

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())