Application of Non-intrusive Sensors and Edge Computing for Condition-based Monitoring of Biogas Compressors

The reliability of manufacturing equipment is critical for ensuring the productivity and energy efficiency of a manufacturing facility. An unexpected machine breakdown may lead to unexpected downtime, disruption of the process schedule, lower production efficiency, and higher operation and maintenance cost. The recent development in machine learning and artificial intelligence enables data-driven Predictive Maintenance (PdM) by means of perceiving the dynamics of manufacturing systems and abstracting them into learnable features to provide a better interpretation of machine failures or unplanned downtimes. Often, vibration is used as a proxy for an early indicator of impending failure. In this study, tri-axial acceleration data collected from biogas compressors are utilized. PdM-based strategies for machine condition monitoring and smart scheduling of equipment maintenance using an anomaly scoring model are discussed. This study exploresincorporating smart manufacturing concepts into Water Resource Recovery Facilities (WRRFs) by implementing non-intrusive sensors and edge computing.