Exploring Industrial Text Data for Monitoring Chemical Manufacturing Processes

To address the limitations of traditional sensing instrumentation in industrial processes, this work explores the use of industrial text data. Given that current instrumentation often fails to capture the full scope of process-related information, text data resulting from operation of industrial settings (for example: maintenance, inspection and incident reports) can provide valuable insights. This study focuses on accessing the effectiveness of natural language processing (NLP) techniques in retrieving critical information from industrial text data. To achieve this, the classification of Process Safety and Containment Events (PSCE) was used as case study. Overall, we found NLP methods are effective in information retrieval from industrial text data. However, the integration of the embeddings into machine learning (ML) approaches poses some challenges. The complexity of the information encoded in the embeddings makes them too disparate and unique samples of a larger domain, making challenging the training of a ML model.