AI-Based Predictive Maintenance for Industrial IOT Applications

Abstract

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.