The accuracy of structural state evaluation may be affected by the damaged piezoelectric sensors. Therefore, it is necessary to identify the sensor fault during monitoring. This paper proposes a method based on classification and regression tree (CART) and particle swarm optimization (PSO) to improve the efficiency of potential feature sets selection for sensor fault classification and build an identification model with the best performance. Firstly, the signal features of three structural changes and four sensor faults were extracted with five indexes. Then the decision trees (DT) for sensor fault classification were built based on different index combinations whose performances were then evaluated by the designed fitness function. Finally, PSO was used to optimize the searching for the best index combination. The results show that compared with the exhaustive method, adopting PSO for DT optimization can greatly simplify the search process. When the particle population is 5 and 10, the fitness converges to the optimal solution after only 6 and 4 iterations respectively. Although the DT with the best fitness is trained with only two indexes, its accuracy is higher than those trained with more indexes and the classification accuracy of 64 samples reaches 98.4% which shows the feasibility and practicability of the method.

损坏的压电传感器可能会影响结构状态评估的准确性。因此，有必要在监测过程中识别传感器故障。本文提出了一种基于分类回归树（CART）和粒子群优化（PSO）的方法，以提高传感器故障分类潜在特征集选择的效率，并构建性能最佳的识别模型。首先用五个指标提取了三个结构变化和四个传感器故障的信号特征。然后基于不同的指标组合构建传感器故障分类的决策树（DT），然后通过设计的适应度函数评估其性能。最后，使用 PSO 优化搜索最佳索引组合。结果表明，与穷举法相比，采用PSO进行DT优化可以大大简化搜索过程。当粒子群为5和10时，适应度分别在6次和4次迭代后收敛到最优解。虽然仅用两个指标训练了适应度最好的 DT，但其准确率高于采用更多指标训练的 DT，64 个样本的分类准确率达到 98.4%，说明了该方法的可行性和实用性。