| --- |
| pretty_name: "Azure Predictive Maintenance (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: "azure.tsfile" |
| --- |
| |
| # Azure Predictive Maintenance (TsFile) |
|
|
| This dataset is an Apache TsFile conversion of |
| [`odysseywt/Azure`](https://huggingface.co/datasets/odysseywt/Azure). |
|
|
| Modalities: Time-series. |
|
|
| ## Overview |
|
|
| - Industrial machine telemetry for predictive maintenance (Azure demo dataset). |
| - Voltage, rotation, pressure, and vibration readings with machine age and a failure label. |
| - Each machine is a device identified by the `machineID` TAG. |
|
|
| - Converted observations: 876,100 rows across 1 TsFile file(s) |
| - Source format: csv |
|
|
| ## TsFile schema |
|
|
| - **Time** — source `datetime` (datetime), converted to INT64 milliseconds. |
|
|
| | Column | Role | Type | Meaning | |
| |---|---|---|---| |
| | `Time` | TIME | INT64 (ms) | sample timestamp | |
| | `machineID` | TAG | STRING | machine id | |
| | `volt` | FIELD | FLOAT | voltage | |
| | `rotate` | FIELD | FLOAT | rotation | |
| | `pressure` | FIELD | FLOAT | pressure | |
| | `vibration` | FIELD | FLOAT | vibration | |
| | `age` | FIELD | FLOAT | machine age | |
| | `label` | FIELD | FLOAT | failure label | |
|
|
| ## Conversion notes |
|
|
| - `machineID` kept as TAG; telemetry + age + label as FIELDs. |
|
|
| ## Source & license |
|
|
| - Original dataset: https://huggingface.co/datasets/odysseywt/Azure |
| - 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("azure.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()) |
| ``` |
|
|
