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I am working on simulation sensor data for an industrial machine I can choose. For that I am looking for multiple data points from a single process, for example, multiple pressure and temperature curves to use as sort of blueprint.

My simulation does not focus on accurate simulation but instead on believable sensor data output.

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The UCI Machine Learning Repository has a quite large sensor dataset

Gas sensor array under flow modulation

The data set contains 58 time series acquired from 16 chemical sensors under gas flow modulation conditions. The sensors were exposed to different gaseous binary mixtures of acetone and ethanol.

The data set is organized in two 'csv' files, 'rawdata.csv.gz' (4.5 MB) and 'features.csv' (200 kB). The raw data are stored in the first file 'rawdata.csv.gz', where each line represents a single measurement per sensor. Consequently, one needs to read specific 16 consecutive lines to get a single measurement from 16 sensors. The features extracted in (Ziyatdinov et al., 2014) are provided in the second file 'features.csv', where each line represents features extracted from all 16 time-series of the sensors (a single measurement).


Additionally, blog post "Great IoT, Sensor and other Data Sets Repositories" has 10+ sensor data sets.

Many of these modern, sensor-based data sets collected via Internet protocols and various apps and devices, are related to energy, urban planning, healthcare, engineering, weather, and transportation sectors.

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Here you can download a database of sensor readings (mostly thermocouples) from a steelcasting plant.

https://drive.google.com/drive/folders/0B73t8xHUY2T1bGhlc2lEMWU2REk

The largest file inside the 1 GB Zip file in the POC1 folder contains 11 million records in "long" format (key-value store). There is also some documentation.

Edit: This dataset was provided by a company for a long-running hackathon/ kaggle-like datascience-competition. The dataset used to be online for quite while in late 2016/early 2017 but it now seems to be offline. Sorry about that.

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