Establishment and optimization of sensor fault identification model based on classification and regression tree and particle swarm optimization

Abstract

Abstract 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.