LAUSR

Intelligent manufacturing poses a challenge for fault diagnosis of rotor systems to meet the three tasks: whether exists faults, faults location and quantitative diagnosis. Traditional methods hardly meet all the three tasks in online fault diagnosis. This paper proposes a modified classification and regression tree (CART) algorithm named D-CART algorithm to provide much faster fault classification by reducing the iteration times in computation while still ensuring accuracy. Experiments are carried on to achieve a comprehensive online fault diagnosis for rotor systems such as faults location, faults types and quantitative analysis of unbalanced mass in this paper. In comparison with the other 4 novel CART-based algorithms, the experimental results indicate that the speed of D-CART algorithm is improved by a factor of 23.92 compared to the fastest improved algorithm (Adaboost-CART) and a model accuracy of up to 96.77%. Thus demonstrating the speed superiority of D-CART algorithm in both diagnosing locations of different faults types and determining the loading masses of unbalanced faults. The proposed method has the potential to realize high-accuracy online fault diagnosis for rotor systems.