Improving Safety and Reliability in Industrial Operations using Semantic AI
Alexi López-Lorca, Semantic Web Company
Zhi Min, Yokogawa
Simon Rogers, Yokogawa
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New York | 4:00 a.m. – 5:00 a.m. EDT
Vienna | 10:00 a.m. – 11 a.m. CEST
Tokyo | 5:00 p.m. – 6:00 p.m. JEST
Shift team effectiveness is critical to safety in the process industries, where ineffective shift handover is a significant source of incidents. The digitalization of operator instructions, operator logs, shift handover reports, and incident management reports, has contributed to reducing risk. However, even though this information may now be stored in a database, much of it is manually entered by operators using natural language. Obtaining insights from the information such as a list of bad actors, or simply searching the information, is challenging because different operators use different nomenclature and abbreviations.
This webinar describes how semantic web technology can be used to effectively address this challenge using an operations management knowledge graph. A knowledge graph defines a taxonomy of terms related to entities such as assets and equipment, shift teams and operators and hazards and events. And it defines an ontology which models the relationships between the entities including synonyms and abbreviations. There are relevant industry standards which can be used to help build the knowledge graph such as ISA95, ISO15926 and ISO14224. In some cases, the standards are already available in a semantic format.
Once built, the knowledge graph can be used to extract and link entities in the operations management database and reports, to enable semantic or natural language search of the information and to link to other information such as equipment data, standard operating procedures and maintenance data. The knowledge graph also allows natural language processing and data analytics to be used to draw insights from the operations data such as a prioritized list of equipment, alarm, and control loop issues. The operations team can then quickly access relevant engineering information to help diagnose the cause of the problems and fix them. In addition to improving safety, this can reduce maintenance costs and increase the availability of the plant.
The application of the operations knowledge graph can be extended to provide semantic integration of all data in an integrated Manufacturing Operations Management (iMOMS) system providing an extensible data model to support all manufacturing activities including planning, scheduling, engineering and maintenance as well as operations. This provides additional benefits such as increased production, lower specific energy costs and emissions and lower Inventories. Learn how this is possible in our webinar conducted jointly with Semantic Web Company and Yokogawa.