Predictive Maintenance

This project focuses on developing a predictive maintenance model to estimate the Remaining Useful Life (RUL) of machinery components. By leveraging machine learning techniques, the model aims to predict when a component is likely to fail, enabling proactive maintenance and reducing downtime.

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Table of Contents

• Project Overview
• Dataset
• Methodology
• Results and Findings
• Usage
• Contributing
• License
• Acknowledgments

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Project Overview

The objective of this project is to build a predictive model that estimates the Remaining Useful Life (RUL) of machinery components based on historical sensor data. Accurate RUL predictions allow maintenance teams to perform timely interventions, thereby minimizing unexpected failures and optimizing maintenance schedules.

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Dataset

The dataset used in this project contains sensor readings collected over time from machinery components. Key features include:

• Sensor Measurements: Various sensor readings indicating the operational status of the component.
• Operational Settings: Parameters defining the operating conditions during data collection.
• Time Cycles: The number of operational cycles since the last maintenance.
• Failure Records: Indicators of component failures or end-of-life events.

Prior to analysis, the dataset undergoes preprocessing steps such as handling missing values, normalizing sensor readings, and encoding categorical variables to ensure data quality and integrity.

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Methodology

1. Data Collection and Preparation

• Data Acquisition: Collect sensor data from machinery over multiple operational cycles.
• Data Cleaning: Handle missing values, remove outliers, and ensure data consistency.
• Feature Engineering: Create new features that may enhance the predictive power of the model, such as moving averages or rates of change of sensor readings.

2. Exploratory Data Analysis (EDA)

• Univariate Analysis: Examine the distribution of individual sensor readings and operational settings.
• Bivariate Analysis: Explore relationships between sensor readings and RUL.
• Visualization: Use plots and graphs to identify patterns and trends in the data.

3. Model Selection and Training

• Algorithm Evaluation: Assess various machine learning algorithms, including:
  • Linear Regression
  • Decision Trees
  • Random Forest
  • Gradient Boosting Machines
  • Neural Networks
• Training: Split the data into training and testing sets and train the selected model using the training data.

4. Model Evaluation

• Metrics: Evaluate model performance using metrics such as:
  • Mean Absolute Error (MAE)
  • Root Mean Squared Error (RMSE)
  • R-squared (R²)
• Validation: Perform cross-validation to ensure the model generalizes well to unseen data.

5. Deployment

• Application Development: Develop a user-friendly application that allows users to input new sensor data and receive RUL predictions.
• Integration: Integrate the predictive model into existing maintenance management systems for real-time monitoring and decision-making.

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Results and Findings

The predictive maintenance model demonstrates a high degree of accuracy in estimating the RUL of machinery components. Key findings include:

• Sensor Importance: Certain sensor readings are more indicative of impending failures than others.
• Operational Conditions: The operating environment significantly influences component lifespan.
• Maintenance Scheduling: Implementing predictive maintenance based on model predictions can reduce unexpected downtime and maintenance costs.

These insights can assist maintenance teams in making informed decisions and optimizing maintenance strategies.

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Usage

To utilize the predictive maintenance model:

1. Clone the repository:

   git clone https://github.com/Rishi-Kora/Predictive-Maintenance.git