Yuan Tangxiao, Xu Weilin, Adjallah Kondo Hloindo, Wang Huifen, Liu Linyan, Xu Junshan
School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
LCOMS, University of Lorraine, 57070 Metz, France.
Sensors (Basel). 2024 Feb 28;24(5):1550. doi: 10.3390/s24051550.
Sensor degradation and failure often undermine users' confidence in adopting a new data-driven decision-making model, especially in risk-sensitive scenarios. A risk assessment framework tailored to classification algorithms is introduced to evaluate the decision-making risks arising from sensor degradation and failures in such scenarios. The framework encompasses various steps, including on-site fault-free data collection, sensor failure data collection, fault data generation, simulated data-driven decision-making, risk identification, quantitative risk assessment, and risk prediction. Leveraging this risk assessment framework, users can evaluate the potential risks of decision errors under the current data collection status. Before model adoption, ranking risk sensitivity to sensor data provides a basis for optimizing data collection. During the use of decision algorithms, considering the expected lifespan of sensors enables the prediction of potential risks the system might face, offering comprehensive information for sensor maintenance. This method has been validated through a case study involving an access control.
传感器退化和故障常常削弱用户采用新的数据驱动决策模型的信心，尤其是在风险敏感场景中。引入了一个针对分类算法量身定制的风险评估框架，以评估在此类场景中由传感器退化和故障引发的决策风险。该框架涵盖多个步骤，包括现场无故障数据收集、传感器故障数据收集、故障数据生成、模拟数据驱动决策、风险识别、定量风险评估和风险预测。利用这个风险评估框架，用户可以在当前数据收集状态下评估决策错误的潜在风险。在采用模型之前，对传感器数据的风险敏感性进行排名可为优化数据收集提供依据。在使用决策算法期间，考虑传感器的预期寿命能够预测系统可能面临的潜在风险，为传感器维护提供全面信息。该方法已通过一个涉及门禁控制的案例研究得到验证。
