Predictive Maintenance using Dash Framework

Predictive Maintenance using Dash Framework

Author Name : Malathesh K, S R Biradar, Pushpalatha S. Nikkam , Jagadeesh D. Pujari

ABSTRACT Predictive maintenance is a crucial approach to enhance the reliability and efficiency of industrial systems by leveraging advanced analytics and data-driven methodologies. This abstract outlines the application of a predictive maintenance system using Dash, a Python web framework for building interactive data visualization dashboards. The proposed system aims to proactively identify potential equipment failures and minimize unplanned downtime, leading to reduced maintenance costs and improved overall productivity. The predictive maintenance system follows a multi-step process, starting with data collection from various sensors and monitoring devices installed in the industrial machinery. These sensors continuously gather real-time data on key performance indicators, and other relevant parameters. The collected data is then pre-processed and stored in a centralized database or data warehouse. Dash, being a powerful tool for building interactive and responsive web applications, serves as the frontend for the predictive maintenance system. The system administrators and maintenance personnel can access the Dash-based dashboard through their web browsers. The dashboard provides an intuitive interface to visualize and explore the collected data, enabling users to monitor the health of the equipment and identify potential anomalies or deviations from normal behavior