A NLP Framework for Automated Error Detection and Severity Classification in Power Equipment Quality Data

The power industry depends on the accurate, dependable quality of the data to be used for operational decision-making, risk assessment, and maintenance. In this paper, the power equipment quality data is designed with a new Automated Error Detection and Severity Classification Framework. This framework deals with two important issues: (1) identifying irregularities and errors in unstructured quality data; and (2) categorizing fault severity levels for prioritization of maintenance tasks. By embedding iterative circuits, the framework ensures continuous improvement of error detection and severity classification accuracy, adapting to evolving data patterns. Leveraging domain-specific Natural Language Processing (NLP) techniques based on the Transformer architecture, namely Bidirectional Encoder Representations from Transformers (BERT). The proposed methodology includes supervised learning that increases the accuracy of error detection and severity classification by pre-training the Transformer model adjusted on the power equipment datasets, as well as data preprocessing procedures to standardize technical terms. In order to ensure the real-time data quality assessment and fault classification for use in large-scale power grid operations, the framework combines these features into a single pipeline. To assess and increase the efficacy of the model in dealing with the complicated and context-dependent language of power equipment data, assessment criteria including accuracy, precision, recall, and F1-score are used.