IoT-Based Predictive Maintenance and Monitoring System for Water Purifiers

Abstract

This work will involve the design of an IoT based, machine learning driven, proactive maintenance and monitoring system for water purifiers. There are various types of sensors: pressure, pH, vibration, temperature, flow, and turbidity will be fitted to the system to continuously check the status of filters, pumps functioning. Cognitive models of Long Short- Term Memory (LSTM) networks and Gradient Boosted Trees (e. g., XGBoost) will classify sensor signals to identify future failures and performance loss anticipation. The methodology uses LSTM for time series data and the XGBoost for fail-safe classification of failure patterns. The system will comprise an application interface that will provide real-time alert and comprehensive notice for maintenance purposes. The expected result is increased service duration of the purifiers, lowered maintenance expenses, and constant availability of filtered water through a solution that is conscious and anticipative of potential problems while keeping the purifiers at maximum efficiency and reliability.