Shibao Pang

Abstract Deficiencies in existing research on hot work risk assessment were identified, as work number and time factors were not considered. A risk assessment method integrating these two factors was proposed. First, a time-segmented adjacent mean method was designed to handle outliers. A time-segmented Lagrange interpolation method was constructed to process missing values, so as to improve data quality.

Abstract With the development of information technology, fault diagnosis models based on data-driven technology are playing an increasingly important role in the chemical industry. However, the lack of interpretability in the decision-making process has severely restricted the application of data-driven fault diagnosis models. To address these issues, this paper proposes an interpretable Takagi-Sugeno-Kang(TSK) fuzzy system based on information granules.