Contrastive Preference-Guided Active Learning Approach Based on Ranking Correlation for Real-Time Safety Assessment

Real-time safety assessment is a critical process for identifying and analyzing potential safety hazards in industrial applications. Active learning has been widely recognized as an effective technique for addressing such issues by utilizing small labeled samples to achieve high evaluation performance. However, in real-world scenarios, the performance of query strategies can be significantly impacted by the data environment. Therefore, it is crucial to co-design a strategy using different criteria to ensure reliable and stable safety assessment results for system operation. In this paper, we propose a contrastive preference-guided active learning approach to tackle chunk-level real-time safety assessment tasks in non-stationary environments. Firstly, we construct negative ranking lists and random lists. Then, we introduce Jeffrey divergence to measure pairwise ranking correlation. By leveraging contrastive preference relationships, we can effectively obtain the value of samples in data chunks with preference aggregation procedures. To verify the effectiveness of the proposed method, we conduct numerous experiments using realistic data from the JiaoLong deep-sea manned submersible. The results demonstrate that our approach outperforms most existing advanced methods in terms of performance stability and accuracy.
Note to Practitioners
—In situations where a significant number of samples must be processed concurrently to promptly identify and mitigate potential hazards, it is vital to consider the responsibilities of real-time safety assessment (RTSA). The proposed approach offers a resolution to the difficulty of acquiring annotations for all samples due to the continual stream of data. Multiple query criteria can be seamlessly integrated, which enhances the stability and superiority of learning performance simultaneously. The approach’s scalability, flexibility, and effectiveness render it a valuable instrument for guaranteeing a prompt response to emergent procedural threats and safety. It can accommodate any number and type of advanced single query criteria, making it easy to adapt to various scenarios. The proposed CPRC approach signifies a significant breakthrough in the field of real-time safety assessment, with the potential to enhance safety outcomes in industrial settings. Multiple experiments involving the realistic JiaoLong deep-sea manned submersible were conducted, and the outcomes demonstrate the benefits of this approach for practical RTSA applications.