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Predictive Maintenance of Equipment
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**Source**: [Predictive Maintenance Dataset](https://www.kaggle.com/datasets/stephanmatzka/predictive-maintenance-dataset-ai4i-2020/data) | ||
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**Description**: | ||
This synthetic dataset is modeled after an existing milling machine and consists of 10,000 data points stored as rows with 14 columns. | ||
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**Features**: | ||
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1. UID: unique identifier ranging from 1 to 10000 | ||
2. product ID: consisting of a letter L, M, or H for low, medium and high as product quality variants, and a variant-specific serial number | ||
3. type: just the product type L, M or H from column 2 | ||
4. air temperature [K] | ||
5. process temperature [K] | ||
6. rotational speed [rpm] | ||
7. torque [Nm] | ||
8. tool wear [min]: The quality variants H/M/L add 5/3/2 minutes of tool wear to the used tool in the process. | ||
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**Target**: 'Machine failure' label that indicates, whether the machine has failed in this particular datapoint. | ||
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Machine failure consists of five independent failure modes: | ||
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1. tool wear failure (TWF) | ||
2. heat dissipation failure (HDF) | ||
3. power failure (PWF) | ||
4. overstrain failure (OSF) | ||
5. random failures (RNF) | ||
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If at least one of the above failure modes is true, the process fails and the 'machine failure' label is set to 1. | ||
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**Citation**: | ||
S. Matzka, "Explainable Artificial Intelligence for Predictive Maintenance Applications," 2020 Third International Conference on Artificial Intelligence for Industries (AI4I), 2020, pp. 69-74, doi: 10.1109/AI4I49448.2020.00023. |
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Predictive Maintenance of Equipment/Dataset/ai4i2020.csv
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