Context This dataset contains the machine data of a degrading component recorded over the duration of 12 month total. It was initiated in the European research and innovation project [IMPROVE][1]. Content The [Vega shrink-wrapper][2] from [OCME][3] is deployed in large production lines in the food and beverage industry. The machine groups loose bottles or cans into set package sizes, wraps them in plastic film and then heat-shrinks the plastic film to combine them into a package. The plastic film is fed into the machine from large spools and is then cut to the length needed to wrap the film around a pack of goods. The cutting assembly is an important component of the machine to meet the high availability target. Therefore, the blade needs to be set-up and maintained properly. Furthermore, the blade can not be inspected visually during operation due to the blade being enclosed in a metal housing and its fast rotation speed. Monitoring the cutting blades degradation will increase the machines reliability and reduce unexpected downtime caused by failed cuts. For more information see also [this new vs worn blade data][4]. The 519 files in the dataset are of the format MM-DDTHHMMSS_NUM_modeX.csv, where MM is the month ranging from 1-12 (not calendar month), DD is the day of the month, HHMMSS is the start time of day of recording, NUM is the sample number and X is a mode ranging from 1-8. Each file is a ~8 second sample with a time resolution of 4ms that totals 2048 time-samples for every file. Acknowledgements This dataset is publicly available for anyone to use under the [following terms][5]. von Birgelen, Alexander; Buratti, Davide; Mager, Jens; Niggemann, Oliver: Self-Organizing Maps for Anomaly Localization and Predictive Maintenance in Cyber-Physical Production Systems. In: 51st CIRP Conference on Manufacturing Systems (CIRP CMS 2018) CIRP-CMS, May 2018. Paper available open access: https://authors.elsevier.com/sd/article/S221282711830307X IMPROVE has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 678867 Inspiration Show the degradation of the component over the course of the year. Has the component been replaced at some point? If the wear can be predicted accurately, a *remaining useful life* prediction can be made in order to determine maintenance windows (predictive maintenance). There are 8 different modes and several different speeds that the machine can be operated in. Is it possible to infer such modes by time series analysis? [1]: http://www.improve-vfof.eu/ [2]: http://www.ocme.com/en/our-solutions/secondary-packaging/vega [3]: http://www.ocme.com/en [4]: https://www.kaggle.com/inIT-OWL/vega-shrinkwrapper-runtofailure-data/home [5]: https://creativecommons.org/licenses/by-sa/3.0/