A risk-based early warning method for offshore platform equipment based on multi-source data fusion

Abstract

Risk-based early warning is a critical approach to ensuring the safety of offshore operations. Its effectiveness relies on data and information collected from the field. However, due to the diversity of data sources, the data often vary in format and characteristics, making standardization within a unified framework challenging. Moreover, inconsistencies may arise between data from different sources, and traditional data fusion techniques can yield counterintuitive results when processing conflicting information. To address these challenges, this study proposes a risk-based early warning method based on multi-source data fusion. Utilizing cloud model theory, the method systematically integrates data from three key sources: sensor monitoring, on-site inspections, and expert judgment. These are transformed into a unified basic probability assignment (BPA). An improved evidence theory incorporating the Bray-Curtis distance and information entropy is introduced to dynamically adjust the weights of BPAs from different evidence sources. Dempster's rule is then applied to sequentially fuse the data and determine the final risk warning level. A case study involving an offshore oil and gas production separator demonstrates that the proposed method effectively integrates data from multiple sources, harmonizes qualitative and quantitative information, and significantly enhances the credibility and reliability of risk warnings compared to traditional approaches.