Rethinking Model-based Fault Detection: Uncertainties, Risks, and Optimization Based on a Multilevel Converter Case Study

This study is utilized for model-based fault detection uncertainty analysis using Monte Carlo simulation. The dataset consists of 8 uncertainty factors and 15 system variables under four operation scenarios. The 1000 sets of uncertainty factor samples are generated randomly as initial configuration of the system. This study is utilized for model-based fault detection uncertainty analysis using Monte Carlo simulation. The dataset consists of 8 uncertainty factors and 15 system variables under four operation scenarios. The 1000 sets of uncertainty factor samples are generated randomly as initial configuration of the system.

本研究用于开展基于模型的故障检测（model-based fault detection）不确定性分析，采用蒙特卡洛模拟（Monte Carlo simulation）方法。该数据集包含4种运行工况下的8个不确定性因子与15个系统变量。研究随机生成了1000组不确定性因子样本作为系统的初始配置。本研究用于开展基于模型的故障检测不确定性分析，采用蒙特卡洛模拟方法。该数据集包含4种运行工况下的8个不确定性因子与15个系统变量。研究随机生成了1000组不确定性因子样本作为系统的初始配置。