Enhanced Predictive Maintenance of Industrial Gas Analyzers via Dynamic Bayesian Calibration & Multi-Modal Anomaly Detection

**Abstract:** This paper introduces a novel framework for enhancing predictive maintenance of industrial gas analyzers, addressing the limitations of existing condition monitoring strategies. We leverage a Dynamic Bayesian Calibration (DBC) technique applied to data streams from multiple analyzer sensors (pressure, temperature, flow rate, and gas concentrations) alongside a Multi-Modal Anomaly Detection (MMAD) system employing deep autoencoders and statistical process control. This combined approach allows for early detection of subtle degradation patterns, leading to improved accuracy in predicting analyzer failures and minimizing downtime. The system is readily implementable using existing sensor technology and standard industrial data acquisition systems, demonstrating immediate commercial viability.