Elevator Maintenance Predictor

Predictive maintenance model for elevators.

Overview

This project aims to predict elevator maintenance needs using machine learning techniques. By analyzing historical data and sensor readings, we can anticipate when an elevator might require maintenance, allowing for timely repairs and minimizing downtime.

Table of Contents

• Introduction
• Dataset
• Installation
• Usage
• Model Details
• Contributing

Introduction

Elevators play a crucial role in modern buildings, and unexpected breakdowns can cause inconvenience and safety risks. This project leverages predictive maintenance to identify potential issues before they escalate.

Dataset

We used the Predictive Maintenance of an Elevator System dataset for training and evaluation. The dataset contains sensor readings, maintenance logs, and elevator usage information.

Installation

1. Clone this repository: git clone https://github.com/Karthiga-220701119/elevator-maintenance-predictor.git cd elevator-maintenance-predictor

2. Install the required dependencies

3. Run the model: python elevator_maintenance.py

Usage

1. Collect sensor data from elevators.
2. Preprocess the data (cleaning, feature engineering, etc.).
3. Train the predictive maintenance model.
4. Monitor the model's performance and adjust as needed.

Model Details

We used a deep learning model (e.g., LSTM or CNN) to predict maintenance needs based on sensor readings. Hyperparameter tuning and cross-validation were performed to optimize the model's performance.

Contributing

Contributions are welcome! If you find any issues or have suggestions, feel free to open an issue or submit a pull request.