Building a classifier model to predict the type of failure, reduce machine downtime, and ensure the reliability of correct maintenance.

Industrial maintenance’s realm , accurately predicting and preventing equipment failures is a critical aspect that can significantly impact operational efficiency and cost-effectiveness. This project delves into a predictive maintenance project aimed at classifying the types of failures based on sensor data, using the Predictive Maintenance Dataset (AI4I 2020).

• Background and Significance Maintenance strategies play a pivotal role in industries, ensuring optimal performance, minimizing downtime, and extending the lifespan of equipment. Predictive maintenance, leveraging data analytics and machine learning, stands out as a proactive approach to identify potential failures before they occur, thereby enabling timely interventions and cost savings.

• The Challenge of Failure Classification Understanding the underlying causes and patterns of equipment failures is essential for effective maintenance planning. By analyzing sensor data, including parameters such as temperature, pressure, vibration, and electrical signals, we aim to categorize different types of failures accurately. This classification not only aids in preemptive maintenance scheduling but also contributes to overall operational reliability.

• Dataset Overview and Analytical Tools The AI4I 2020 dataset provides a comprehensive collection of sensor data from industrial equipment, offering insights into operational conditions and potential failure indicators. Leveraging Jupyter Notebook, we conduct exploratory data analysis (EDA) and build machine learning models to classify failure types based on the sensor readings.

• Project Methodology Following the Cross-Industry Standard Process for Data Mining (CRISP-DM), our project unfolds in structured phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. We utilize Streamlit for deploying the trained model, providing an interactive interface for real-time predictions and analysis.