Automated verification of measurement precision for internet-of-things equipment

Embedded sensors and processors built into assets enable condition monitoring data to be collected and used for asset health management and process optimization. Managing data storage, transmission, and security at all levels of the technology stack is crucial for original equipment manufacturers (OEM) and their customers. The federated nature of the product value chain with software and hardware components coming from external suppliers presents challenges when managing this data. The OEM must have assurance processes to confirm that software upgrades provided by their suppliers do not adversely affect the outputs delivered to customers. The output of sensor measurements have a precision determined by the storage space of data registers in a controller’s electronics. One technical challenge for OEMs is tracking changes and settings in software code that affect measurement precision when these settings are embedded in hardware components from external suppliers. For this we use a semantic asset model based on the Industrial Data Ontology (IDO). Entities that impact the reported precision are captured in the model.