This project is focused on predicting machine failures in a manufacturing setting using classification models trained on sensor data. The dataset includes features such as tool wear, rotational speed, torque, and temperatures, and the goal is to classify the type of failure a machine is likely to experience. By anticipating equipment issues before they occur, this system helps reduce unplanned downtime, improve operational efficiency, and cut maintenance costs.
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Predictive maintenance aims to forecast when manufacturing equipment is likely to fail so that maintenance can be performed just in time to prevent unexpected breakdowns. This approach minimizes downtime, reduces maintenance costs, and improves overall operational efficiency. In this project, we use sensor data from manufacturing machines to predict potential failures before they occur. The goal is to build a reliable classification model that can identify the likelihood of machine failure based on real-time sensor readings.
- Develop a predictive model that accurately classifies machine states into normal operation or failure risk.
- Use historical sensor data to identify patterns and early warning signs of machine degradation.
- Enable proactive maintenance scheduling to reduce unplanned downtime and increase equipment lifespan.
- Provide actionable insights to maintenance teams through model interpretation.
- Handling imbalanced data, since failure events are often rare compared to normal operation.
- Dealing with noisy or missing sensor data that could affect model accuracy.
- Extracting meaningful features from multivariate time-series sensor data.
- Ensuring the model generalizes well to new, unseen machines or operating conditions.
- Integrating predictive maintenance predictions into existing manufacturing workflows.
Dataset Source: Kaggle - Machine Predictive Maintenance Classification
- Data Collection & Understanding: Acquire and explore the dataset, understand feature distributions, missing data, and class imbalance.
- Data Preprocessing: Clean data, handle missing values, normalize sensor readings, and engineer features if necessary.
- Exploratory Data Analysis (EDA): Visualize sensor trends, correlations, and failure patterns.
- Model Building: Train classification models such as Random Forest, XGBoost, or neural networks.
- Model Evaluation: Assess models using metrics like accuracy, precision, recall, F1-score, and ROC-AUC.
- Model Interpretation: Analyze feature importance and validate predictions with domain knowledge.
- Deployment & Monitoring (Optional): Package the model for real-time prediction and monitor its performance over time.
- Achieve a classification accuracy of at least 85% on the test dataset.
- Maintain a recall (sensitivity) above 80% to minimize missed failure predictions.
- Develop interpretable models that provide insights into sensor features contributing to failures.
- Demonstrate robustness of the model through cross-validation and testing on unseen data.
- A trained machine learning model capable of predicting machine failures ahead of time.
- Detailed analysis and visualization of sensor data highlighting failure indicators.
- A documented workflow from data preprocessing to model evaluation.
- Recommendations for integrating predictive maintenance insights into manufacturing operations.
-Kaggele Link : (https://www.kaggle.com/datasets/shivamb/machine-predictive-maintenance-classification)
