Access to operational data needed to drive effective compliance programs is challenging to capture, to effectively use, and is often hampered by commercial pressures. Furthermore, demonstrating value and business benefits often requires significant and complex pilot projects. Just getting meaningful edge data on the front-end to drive analytics and reporting can be a monumental task itself.
SensiML Analytics Toolkit combined with modern smart sensor IIoT endpoints can enable true insights to be quickly tailored from available physical sensors like worker wearable devices, intelligent machine sensors, and process control inputs where they occur. With edge algorithms, the potential deluge of ensuing data can be effectively managed and only insights of value conveyed for downstream reporting and analysis.
Across nearly every sector, organizations are challenged with maximizing operational uptime and production yields while not skipping a beat. Unscheduled downtime means lost revenue, lost margins, and an idled workforce. Poor product yields translate directly to the bottom line with reduced margins, higher operating overhead, and uncompetitive product costs. Anticipating these adverse production events requires insight on complex processes and machinery.
SensiML’s Analytics Toolkit can transform complex high-frequency sensor data from many points in the process to derive production insight in real-time. With ML teachable algorithms, SensiML enabled IIoT sensors can Identify systems anomalies associated with pending equipment failures and yield losses.
Infinitesimal cracking is one of the most dangerous types of material failure and found where components are put under continuous or cyclical stress. These types of cracks often go unnoticed or detected only with infrequent testing. Failure to identify stress-related component fatigue poses significant safety and financial risks.
SensiML Analytics Toolkit combined with high-bandwidth acoustic emission sensors can provide the pattern recognition and edge sensor processing needed to catch fleeting critical events that portend risk for catastrophic failure. Smart structural sensors can replace expensive and infrequent manual inspections. SensiML AI sensor processing integrated directly into the sensor modules offers real-time responsiveness, network independence, and ubiquitous deployment to greatly improve on existing methods.