AI-Based Predictive Maintenance for Industrial IOT Applications

The technologies that are being investigated and used in a wide range of ways across different industry verticals in the twenty-first century include Industrial IoT (Internet of Things-IIoT) and Intelligent Production & Manufacturing. In order to meet the service requirements of advanced manufacturing complex networks and safety-critical infrastructures, this new era demands that, in addition to modernizing industry processes, the strategic importance of sophisticated, intelligent, and effective predictive structural health monitoring and management technologies be increased globally. Real-time machine sensor readings are recorded during the machine monitoring process and shown on a graph with threshold limitations. Natural variation is an essential part of the process, and the measured values that fall within the thresholds show that everything is functioning normally and according to plan. A serious issue that needs to be addressed right away to prevent negative outcomes is indicated if measured values exceed the threshold limits. In the past, manufacturers have used SCADA systems to accomplish PdM, manually hardcoding thresholds and putting in place static alert rules, among other changes. The "Remaining Useful Life (RUL) Estimation" is an additional pillar. It makes predictions about how long a machine will last before requiring repairs or replacement. The automobile, nuclear, pharmaceutical, and aviation industries are just a few of the industries where decision-making may be significantly impacted by an accurate RUL assessment. RUL estimates are frequently calculated using one of two main approaches: data-driven models or model-based models.