| Popis: |
Risk estimation holds significant importance in the selection of risk reduction measures and ensuring machinery safety. However, subjective influences of assessors lead to an inconsistent understanding of risk among relevant stakeholders, hindering the achievement of safety. As similarities exists in product updates or applications in engineering practice, the historical risk information of similar products or applications has essential application value. A novel deep learning approach was established to estimate risks based on historical risk information. To address the issue of overfitting caused by a limited dataset, a data augmentation technique was employed. Our experiment was conducted on the raw, 2×, and 6× hazard event dataset of an industrial robot, demonstrating a substantial improvement in both accuracy and stability. On the validation dataset, there was an increase in median accuracy from 55.56% to 96.92%, with a decrease in standard deviation from 0.118 to 0.015. On the new dataset, the trained network also showed near-perfect performance on similar hazard events and trustworthiness on completely different ones. In cases of risk deviations, approximately 80% of them were small deviations (|RIdeviation| ≤ 2) without a noticeable bias (RIdis is close to 1). The LSTM-based deep learning network makes risk estimation “black-boxed” and “digitized”. Assessors just need to focus on hazard identification with risk being determined by the trained network, mitigating the impact of individual factors. Moreover, the historical risk estimation information can be transformed into a trained network, facilitating the development of a standardized benchmark within project teams, enterprises, and relevant stakeholders to promote coordinated safety measures. |
