Energy Analytics in Action: Heating Tunnel Project

This repository contains the implementation of the Heating Tunnel Project, a comprehensive application of unsupervised machine learning techniques for industrial process optimization. The project identifies operational inefficiencies and potential sources of quality defects in a manufacturing heating tunnel system using clustering analysis.

Project Overview

The goal of this project was to analyze operational energy data from a heating tunnel system, detect patterns, and uncover anomalies leading to quality defects and increased energy consumption. Using the CRISP-DM methodology, the project encompasses all phases from business understanding to deployment.

Key Objectives

• Analyze archived operational data to identify inefficiencies in energy consumption.
• Implement unsupervised clustering algorithms to group operational patterns.
• Draw actionable insights for improving manufacturing consistency and efficiency.

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Features

• Data Preprocessing: Data cleaning, standardization, and preparation for analysis.
• Machine Learning: Implementation of the Time Series KMeans algorithm for clustering.
• Evaluation Metrics: Use of Silhouette and Calinski-Harabasz scores for cluster validation.
• Visualization: Graphical representation of energy consumption patterns and clustering results.

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Technologies Used

• Programming Language: Python
• Libraries:
  • tslearn for time series clustering
  • numpy and pandas for data manipulation
  • matplotlib for data visualization
  • scikit-learn for evaluation metrics
• Tools: Jupyter Notebook / Google Colab

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Project Structure

Heating_Tunnel_Project/
│
├── data/                       # Raw and processed data files
├── notebooks/                  # Jupyter notebooks for each phase of the project
│   ├── Phase_1_Business_Understanding.ipynb
│   ├── Phase_2_Data_Understanding.ipynb
│   ├── Phase_3_Data_Preprocessing.ipynb
│   ├── Phase_4_Model_Implementation.ipynb
│   └── Phase_5_Evaluation_and_Deployment.ipynb
├── outputs/                    # Visualizations and clustering results
├── requirements.txt            # Required Python libraries
└── README.md                   # Project documentation

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How to Use

1. Clone this repository:

   git clone https://github.com/aksh-ay06/Operational-Efficiency-Analysis-Machine-Learning-for-Heating-Tunnel-Optimization.git

2. Navigate to the project directory:

   cd Operational-Efficiency-Analysis-Machine-Learning-for-Heating-Tunnel-Optimization

3. Install the required dependencies:

   pip install -r requirements.txt

4. Open the Jupyter Notebooks in the directory to follow each project phase:

   jupyter Project_3(Smart_manufacturing).ipynb

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Results

The project successfully identified two distinct operational patterns and highlighted inefficiencies related to:

• Operator-dependent parameter variations.
• Maintenance inconsistencies in pneumatic systems.
• Process control limitations due to the absence of automation.

Cluster Analysis Metrics

• Silhouette Score: 0.65–0.66
• Calinski-Harabasz Score: 63.03–82.04

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Contributors

• [Akshay Patel]
  Graduate Student in Industrial Engineering
  Email: [akshaypatelnitb6@gmail.com]