Merge pull request #691 from siddhant4ds/predictive-maintenance

Predictive Maintenance of Equipment
abhisheks008 · Jul 6, 2024 · b6c2408 · b6c2408
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diff --git a/Predictive Maintenance of Equipment/Dataset/README.md b/Predictive Maintenance of Equipment/Dataset/README.md
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+**Source**: [Predictive Maintenance Dataset](https://www.kaggle.com/datasets/stephanmatzka/predictive-maintenance-dataset-ai4i-2020/data)
+
+**Description**:
+This synthetic dataset is modeled after an existing milling machine and consists of 10,000 data points stored as rows with 14 columns.
+
+**Features**:
+
+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.
+
+**Target**: 'Machine failure' label that indicates, whether the machine has failed in this particular datapoint.
+
+Machine failure consists of five independent failure modes:
+
+1. tool wear failure (TWF)
+2. heat dissipation failure (HDF)
+3. power failure (PWF)
+4. overstrain failure (OSF)
+5. random failures (RNF)
+
+If at least one of the above failure modes is true, the process fails and the 'machine failure' label is set to 1.
+
+**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.
diff --git a/Predictive Maintenance of Equipment/Dataset/ai4i2020.csv b/Predictive Maintenance of Equipment/Dataset/ai4i2020.csv
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