Predictive Maintenance for Manufacturing

Project Overview

This project applies machine learning and anomaly detection techniques to anticipate machine failures in a manufacturing setting. By leveraging real sensor data, we identify operational patterns, detect anomalies using Isolation Forest, and predict failures using Random Forest Classifier. The outputs are visualized through interactive dashboards and plots to support real-time maintenance decisions.

Objective & Business Context

Manufacturing systems suffer significantly when machine failures occur unexpectedly — leading to downtime, lost productivity, and costly repairs. Predictive maintenance transforms reactive repair cycles into proactive planning by identifying early warning signals of failure.

This project aims to:

• Detect anomalies in machine sensor data

• Predict the probability of machine failure using classification models

• Analyze contributing features and failure patterns

• Visualize failure risks to support maintenance teams

Business Value and Real-World Scope

Predictive Maintenance helps manufacturing firms shift from reactive repairs to data-driven foresight. By identifying early signals of machine stress, businesses can reduce unplanned downtime, optimize maintenance costs, and ensure smoother operations across the factory floor.

Real-World Impact:

• Manufacturing: Minimize line stoppages and increase machine uptime

• Industrial IoT: Enable smart sensor-based failure alerts

• Scalability: Apply the same models across equipment types or plants

This project’s end-to-end pipeline offers a practical blueprint for adopting AI in operational settings — with interpretable results, fast deployment, and tangible ROI.

Implementation Flow

• Source: predictive_maintenance.csv

• Records: Time-stamped machine sensor data

• Key Features:

  • Temperature, Torque, Tool Wear: Continuous operational parameters

  • Machine Type: Categorical (converted using Label Encoding)

  • Failure Modes: Binary flags for TWF, HDF, PWF, OSF, RNF

  • Target: Machine failure column indicating overall breakdown

Dataset Overview

Data Preprocessing Workflow

• Dropped IDs and redundant columns

• Encoded Type variable as integers using LabelEncoder

• Saved cleaned output as processed_data.csv

Anomaly Detection

Failure Prediction

Visual Exploration

Streamlit Dashboard

• Applied IsolationForest on key features

• Marked anomalies (outliers) in the dataset

• Saved outputs as anomaly_results.csv

• Trained a RandomForestClassifier on sensor values

• Evaluated using accuracy score and classification report

• Saved predictions to predictions_with_rf.csv

• Scatter plots for anomaly vs machine parameters

• Feature importance bar plots

• Failure rate by machine type

• 3D anomaly visualization using plotly

• Created dashboard prototype for failure distribution, anomaly plots, and model stats

• Data export button for maintenance teams to download results

Visual Analytics & Interpretations

• Anomaly Detection Plot: Scatterplot of Torque vs RPM with anomalies highlighted (red = outlier, blue = normal)

• Feature Importance Bar Plot: Importance scores from Random Forest model to interpret which features matter most

• Anomalies per Failure Mode (Grouped Bar Chart): Visualizing how different failure types align with anomalies

• Anomaly Detection with Failure Modes (Scatterplot): Overlayed view of Machine Failure + Anomaly status on Torque vs RPM

• Correlation Heatmap: Shows relationships between sensor variables and the target

• Failure Rate by Machine Type (Bar Plot): Identifies which machine types are more failure-prone

• Torque Boxplot by Anomaly Class: Distribution of torque values for anomalous vs normal conditions

• 3D Anomaly Detection Plot (Plotly): Interactive 3D visualization across RPM, Torque, and Air Temperature

✅ preprocess.py, train_model.py, predict.py: Core implementation scripts

✅ anomaly_detection.py: Visualization + feature insights

✅ processed_data.csv, predictions_with_rf.csv, anomaly_results.csv: Output datasets

✅ Visuals: All analysis plots (scatter, bar, heatmap, 3D)

✅ Dashboard: Streamlit prototype for internal review

Key Deliverables

Tools and Libraries Used

• pandas ,numpy - Data processing and transformation

• sklearn - Modeling, scaling, evaluation

• matplotlib, seaborn, plotly - Visualization and enhanced styling

• joblib - Model persistence

• streamlit - Interactive dashboard interface

• Model Generalization: Incorporate time series models or neural nets (LSTM)

• Edge Deployment: Convert model to run on IoT sensors locally

• Failure Mode Classifier: Train model to predict specific failure causes

• Interactive UI: Expand dashboard with real-time monitoring & alerting

Possible Next Steps & Conclusion

Conclusion

This project showcases the practical application of AI in industrial reliability and maintenance. By combining supervised and unsupervised models with strong visual storytelling, it not only predicts potential failures but also uncovers operational patterns that may not be evident otherwise. The integration of interpretability-focused dashboards, anomaly overlays, and failure mode breakdowns adds significant value for stakeholders. This is a solid, real-world example of how AI can move from experimentation to impact in manufacturing.

Dive into the foundational concepts, algorithms, and real-world relevance behind this project. From machine learning principles to business strategy insights, this conceptual study bridges the gap between technical implementation and applied decision-making—helping you understand not just how it works, but why it matters.

Key Concepts

GitHub Repository

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