Predictive Maintenance for Industrial Equipment

📌 Project Overview

This project uses machine learning to focus on Predictive Maintenance for industrial equipment. The goal is to predict potential machine failures before they occur, enabling proactive maintenance and reducing downtime.

📊 Dataset

The dataset contains sensor data from industrial machines, including features such as:

• Air Temperature (K)
• Process Temperature (K)
• Rotational Speed (rpm)
• Torque (Nm)
• Tool Wear (min)
• Machine Failure Indicators (TWF, HDF, PWF, OSF, RNF)
• **Tool wear failure **(TWF): the tool will be replaced of fail at a randomly selected tool wear time between 200 - 240 mins (120 times in our dataset). At this point in time, the tool is replaced 69 times, and fails 51 times (randomly assigned). Heat dissipation failure (HDF): heat dissipation causes a process failure, if the difference between air- and process temperature is below 8.6 K and the tools rotational speed is below 1380 rpm. This is the case for 115 data points. Power failure (PWF): the product of torque and rotational speed (in rad/s) equals the power required for the process. If this power is below 3500 W or above 9000 W, the process fails, which is the case 95 times in our dataset. -**Overstrain failure **(OSF): if the product of tool wear and torque exceeds 11,000 minNm for the L product variant (12,000 M, 13,000 H), the process fails due to overstrain. This is true for 98 datapoints. -Random failures (RNF): each process has a chance of 0,1 % to fail regardless of its process parameters. This is the case for only 5 datapoints, less than could be expected for 10,000 datapoints in our dataset.

🎯 Objective

The goal is to build a machine-learning model that predicts whether a machine will fail based on sensor readings. This helps industries reduce downtime, optimize maintenance costs, and improve operational efficiency.

🛠️ Tech Stack

• Programming Language: Python 🐍
• Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
• Modeling: Machine Learning (Logistic Regression, Random Forest, XGBoost)
• Evaluation Metrics: Accuracy, Precision, Recall, F1-score, Confusion Matrix

📌 Key Steps

1. Exploratory Data Analysis (EDA)

   • Data visualization & insights
   • Handling missing values & outliers

2. Feature Engineering

   • Data transformation & scaling
   • Feature selection

3. Model Development

   • Training multiple machine learning models
   • Hyperparameter tuning

4. Model Evaluation

   • Performance comparison
   • Confusion matrix & classification reports

📈 Results

The best-performing model achieved:

• Accuracy: 97.855%
• Precision: 97.78%
• Recall: 97.85%%
• F1-score: 97.81%

🚀 How to Run the Project

1. Clone the repository:

   git clone https://github.com/BatthulaVinay/Predictive-Maintenance.git

2. Navigate to the project folder:

   cd Predictive-Maintenance

3. Install required dependencies:

   pip install -r requirements.txt

4. Run the Jupyter Notebook:

   Jupiter notebook

📌 Future Work

• Implement Deep Learning models (ANN, LSTM)
• Enhance feature selection & engineering
• Deploy the model as an API for real-time predictions

📬 Connect with Me

• LinkedIn: linkedin.com/in/batthula-vinay
• GitHub: github.com/BatthulaVinay

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