Datasets:

lifelonglab
/

CAD-TCM

pretty_name: CAD-TCM
license: cc-by-4.0
configs:
  - config_name: default
    data_files: data.csv
language:
  - en
task_categories:
  - tabular-classification
task_ids:
  - tabular-multi-class-classification
size_categories:
  - 100K<n<1M
tags:
  - anomaly-detection
  - continual-learning
  - continual-anomaly-detection
  - predictive-maintenance
  - steel-manufacturing
  - tandem-cold-mill
  - tabular
  - synthetic

CAD-TCM

Dataset Summary

CAD-TCM is a single-source continual anomaly detection benchmark scenario for predictive maintenance in steel manufacturing. It is derived from TCM: Benchmark Datasets for Predictive Maintenance in Steel Manufacturing, a synthetic dataset generated from a physics-based mathematical model of a 5-stand tandem cold mill (TCM), and converts the original tabular sensor data into a sequence of concept-grouped tasks.

The dataset contains 108,872 samples, 8 tasks, and has a reported 18.66% anomaly ratio in the test set.

Intended Use

This dataset is intended for research on:

continual anomaly detection;
continual learning for tabular data;
predictive maintenance in industrial/manufacturing processes;
robustness under distribution shift;
task ordering in continual-learning benchmarks;
forgetting and knowledge transfer across related process-monitoring concepts;
benchmarking anomaly detectors under sequential task exposure.

The intended use is defensive/industrial machine learning research, i.e. detecting equipment faults and process anomalies to support predictive maintenance.

Dataset Source

TCM: Benchmark Datasets for Predictive Maintenance in Steel Manufacturing: https://zenodo.org/records/11469702

The source data is entirely synthetic, generated from a physics-based mathematical model of a 5-stand tandem cold mill simulating four fault types: reduction-scheme irregularities, work-roll friction increase, bearing failure (elevated motor torque), and motor efficiency loss.

Dataset Files

The repository contains the following files:

File	Description
`data.csv`	Main tabular dataset file.
`orderings.json`	Predefined task orderings for continual-learning evaluation.
`croissant.json`	Croissant metadata describing the dataset.

Dataset Structure

The main file is:

data.csv

The dataset contains task metadata, binary labels, and numerical rolling-mill process/sensor features.

Core Columns

Column	Type	Description
`task_id`	integer	Numeric identifier of the continual-learning task.
`task_name`	string	Name of the task, e.g. `tcm_0`.
`task_split`	string	Split assignment for the row.
`label`	integer	Binary anomaly label. `0` denotes normal operation and `1` denotes an anomalous/faulty condition.

Task Identifiers

The dataset contains the following task identifiers:

tcm_0, tcm_1, tcm_2, tcm_3, tcm_4, tcm_5, tcm_6, tcm_7

Feature Columns

The remaining columns are numerical process/sensor features from the 5-stand tandem cold mill, including per-stand measurements. Representative examples include:

thickness_entry, thickness_exit
width
ys_entry, ys_exit (yield strength)
work_roll_diam_1 … work_roll_diam_5
work_roll_mileage_1 … work_roll_mileage_5
reduction_1 … reduction_5
tension_0 … tension_5
roll_speed_1 … roll_speed_5
force_1 … force_5
torque_1 … torque_5
gap_1 … gap_5
motor_power_1 … motor_power_5

For the complete schema, see croissant.json.

Task Orderings

The dataset provides six predefined orderings in orderings.json. These orderings define different continual-learning evaluation regimes over the same task set.

Ordering	Task sequence
`curriculum_asc`	`tcm_2` → `tcm_5` → `tcm_7` → `tcm_6` → `tcm_4` → `tcm_0` → `tcm_1` → `tcm_3`
`curriculum_desc`	`tcm_3` → `tcm_1` → `tcm_0` → `tcm_4` → `tcm_6` → `tcm_7` → `tcm_5` → `tcm_2`
`generalization_desc`	`tcm_0` → `tcm_5` → `tcm_2` → `tcm_7` → `tcm_4` → `tcm_6` → `tcm_1` → `tcm_3`
`generalization_asc`	`tcm_3` → `tcm_1` → `tcm_6` → `tcm_4` → `tcm_7` → `tcm_2` → `tcm_5` → `tcm_0`
`smooth_drift`	`tcm_5` → `tcm_7` → `tcm_0` → `tcm_2` → `tcm_1` → `tcm_6` → `tcm_4` → `tcm_3`
`abrupt_drift`	`tcm_0` → `tcm_3` → `tcm_5` → `tcm_1` → `tcm_2` → `tcm_6` → `tcm_7` → `tcm_4`

These orderings are intended to expose complementary continual-learning dynamics, including curriculum-like adaptation, generalization-oriented ordering, smooth drift, and abrupt drift.

Dataset Creation

The details of dataset creation can be found in our paper: link

Citation

When using the dataset, please cite:

@article{faber2026towards,
  title={Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios},
  author={Faber, Kamil and Smendowski, Mateusz and Corizzo, Roberto},
  journal={arXiv preprint arXiv:2607.18289},
  year={2026}
}