AI Based Predictive Maintence

The rapid advancement of Industry 4.0 and the Industrial Internet of Things (IIoT) has increased the demand for intelligent maintenance strategies that minimize unplanned downtime and operational costs.Traditional maintenance approaches, such as reactive and scheduled maintenance, fail to fully exploit the large volumes of sensor data generated by modern industrial machines.This paper presents an AI-Based Predictive Maintenance System that employs deep learning techniques to predict potential machine faults before failure occurs.The proposed system utilizes an ESP32-based microcontroller to acquire multivariate time-series data from an AC/DC motor using temperature, humidity, gas concentration, and vibration sensors.A Stacked Long Short-Term Memory (LSTM) neural network is used to analyze temporal patterns within the sensor data and identify anomalous behavior associated with machine degradation.The system follows an end-to-end architecture encompassing data acquisition, preprocessing, normalization, sequence generation, and fault prediction.The trained model is integrated into a Flask-based web application, allowing users to upload sensor data retrieved from a cloud database and receive predictive insights through an interactive dashboard.Experimental evaluation on an experimentally collected dataset of 100 instances demonstrates reliable performance, with the model accurately classifying 44 normal states and 42 fault states.The proposed system improves maintenance efficiency, enhances equipment reliability, and supports data-driven decision-making, validating its suitability for predictive maintenance in Industrial IoT environments.