https://github.com/sushant1827/machine-learning-for-predictive-maintenance
Demonstrate the application of machine learning on a real-world predictive maintenance dataset, using measurements from actual industrial equipment.
https://github.com/sushant1827/machine-learning-for-predictive-maintenance
binary-classification classification-report confusion-matrix data-imbalance data-visualization decision-tree-classifier exploratory-data-analysis feature-engineering feature-importance feature-selection gradient-boosting-classifier imbalanced-data multi-class-classification roc-auc-curve
Last synced: about 1 year ago
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Demonstrate the application of machine learning on a real-world predictive maintenance dataset, using measurements from actual industrial equipment.
- Host: GitHub
- URL: https://github.com/sushant1827/machine-learning-for-predictive-maintenance
- Owner: sushant1827
- Created: 2025-01-01T17:33:13.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-01T17:35:08.000Z (over 1 year ago)
- Last Synced: 2025-04-08T13:51:39.226Z (about 1 year ago)
- Topics: binary-classification, classification-report, confusion-matrix, data-imbalance, data-visualization, decision-tree-classifier, exploratory-data-analysis, feature-engineering, feature-importance, feature-selection, gradient-boosting-classifier, imbalanced-data, multi-class-classification, roc-auc-curve
- Language: Jupyter Notebook
- Homepage:
- Size: 1.67 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
---
### **Machine Learning for Predictive Maintenance**
---
#### **Brief Description**
This dataset reflects real predictive maintenance encountered in the industry with measurements from real equipments. The features description is taken directly from the dataset source.
Dataset Link:
https://archive.ics.uci.edu/dataset/601/ai4i+2020+predictive+maintenance+dataset
---
#### **Feature Variables**
**Air temperature** [K]: Generated using a random walk process later normalized to a standard deviation of 2 K around 300 K
**Process temperature** [K]: Generated using a random walk process normalized to a standard deviation of 1 K, added to the air temperature plus 10 K.
**Rotational speed** [rpm]: Calculated from a power of 2860 W, overlaid with a normally distributed noise
**Torque** [Nm]: Torque values are normally distributed around 40 Nm with a σ = 10 Nm and no negative values.
**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 Variables**
**Machine failure** : Failure or No failure (to perform binary classification)
**Failure Type**: Type of failure (to perform multiclass classification).
---
The machine failure type are as mentioned below
**Tool wear failure** (TWF): The tool will be replaced on failure
**Heat dissipation failure** (HDF): Heat dissipation causes a process failure
**Power failure** (PWF): The product of torque and rotational speed (in rad/s) equals the power required for the process.
**Overstrain failure** (OSF)
**Random failures** (RNF): Each process has a chance of 0,1 % to fail regardless of its process parameters.
----------------------------------------------------------------------
#### **Additional Variables**
**UID**: Unique identifier ranging from 1 to 10000
**Product ID**: Consists of letters L, M, or H for low (50% of all products), medium (30%) and high (20%) as product quality variants and a variant-specific serial number