Bosch CNC Machining Dataset


Donated on 11/10/2022

Manufacturing processes have undergone tremendous technological progress in recent decades. To meet the agile philosophy in industry, data-driven algorithms need to handle growing complexity, particularly in Computer Numerical Control machining. To enhance the scalability of machine learning in real-world applications, this paper presents a benchmark dataset for process monitoring of brownfield milling machines based on acceleration data. The data is collected from a real-world production plant using a smart data collection system over a two-years period. In this work, the edge-to-cloud setup is presented followed by an extensive description of the different normal and abnormal processes. An analysis of the dataset highlights the challenges of machine learning in industry caused by the environmental and industrial factors. The new dataset is published with this paper and available at:

Dataset Characteristics

Multivariate, Time-Series

Subject Area


Associated Tasks

Classification, Clustering

Attribute Type


# Instances


# Attributes



What do the instances in this dataset represent?

time-series data of a high-frequency accelerometer, mounted on a large CNC machining center.

Are there recommended data splits?

See Paper Figure 10. Split over process OP | per machine | per time-frame

Additional Information

The dataset created for the research located in the directory data are licensed under a Creative Commons Attribution 4.0 International License (CC-BY-4.0).

Citation Requests/Acknowledgements

Please cite this paper if using the dataset and direct any questions regarding the dataset to Tnani Mohamed-Ali.

Introduction Paper


Home Page
1 citations


unequal lengthmultivariatecnc machining2kHzaccelerometeriotfault detectiondata drift


Michael Feil

Technical University Munich


See linked dataset for licensing information.

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