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.

中文翻译：

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转子系统的高速D-CART在线故障诊断算法

智能制造对转子系统的故障诊断提出挑战，以使其满足以下三个任务：是否存在故障，故障位置和定量诊断。传统方法几乎无法满足在线故障诊断中的所有三个任务。本文提出了一种改进的分类和回归树（CART）算法，称为D-CART算法，它通过减少计算中的迭代时间而又能确保准确性，从而提供了更快的故障分类。本文针对转子系统的故障在线定位，故障类型，不平衡质量定量分析等进行了实验研究，以实现全面的在线故障诊断。与其他4种基于CART的新颖算法相比，实验结果表明D-CART算法的速度提高了23倍。92与最快的改进算法（Adaboost-CART）相比，模型精度高达96.77％。从而证明了D-CART算法在诊断不同故障类型的位置以及确定不平衡故障的负载量方面的速度优势。所提出的方法有可能实现转子系统的高精度在线故障诊断。