Add TsFile (converted from odysseywt/CWRU)

.gitattributes

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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+cwru.tsfile filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+pretty_name: "CWRU Bearing Fault (TsFile)"
+modality: timeseries
+authors: "odysseywt"
+task_categories:
+- time-series-forecasting
+size_categories:
+- 100K<n<1M
+tags:
+- tsfile
+- timeseries
+- modality:timeseries
+- format:tsfile
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: "cwru.tsfile"
+---
+
+# CWRU Bearing Fault (TsFile)
+
+This dataset is an Apache TsFile conversion of
+[`odysseywt/CWRU`](https://huggingface.co/datasets/odysseywt/CWRU).
+
+Modalities: Time-series.
+
+## Overview
+
+- Case Western Reserve University (CWRU) bearing fault benchmark.
+- Each segment stores 17 extracted features (`all_features`).
+- Fault size, label, and segment are device TAGs.
+
+- Converted observations: 370,362 rows across 1 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 |
+| `fault_size` | TAG | STRING | fault size |
+| `label` | TAG | STRING | condition label |
+| `segment_idx` | TAG | STRING | — |
+| `segment_id` | TAG | STRING | segment id |
+| `all_features` | FIELD | FLOAT | feature value |
+
+## Conversion notes
+
+- Extracted features flattened to scalar samples; fault_size/label/segment_id are TAGs.
+
+## Source & license
+
+- Original dataset: https://huggingface.co/datasets/odysseywt/CWRU
+- 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("cwru.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())
+```

cwru.tsfile

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+oid sha256:dfb7ff7bc27eaf52373be454601eb017230e57f7b7d13ee4671c64ef4541320a
+size 6565082