Method and system for extracting and collating failure knowledge from diverse sources in industrial plant

Abstract

Failure analysis of industrial plants are stored in various types of documents associated with industrial plant. The documents are used by operators of plant to address any deviation that is active in plant. The operator generally has prior knowledge of relevant processes, equipment and sensors described in a deviation scenario in these documents. However, a system that is envisaged to aid operator in real-time does not have this information readily available as this knowledge is spread across documents. Currently available systems manually curate failure knowledge thereby making the process time consuming and prone to human errors. Present disclosure provides method and system for performing extracting and collating failure knowledge from diverse sources in industrial plant. The system automatically extracts failure knowledge present in text documents using trained models and links it with process, equipment, sensor relationships that are present in piping and instrumentation diagram to create failure scenario knowledge repository.