Gearbox fault diagnosis is one of the most important parts in any industrial systems. Failure of components inside gearbox can lead to a catastrophic failure, uneven breakdown, and financial losses in industrial organization. In that case intelligent maintenance of the gearbox comes into context. This paper presents an integrated gearbox fault diagnosis approach which can easily deploy in online condition monitoring. This work introduces a nonparametric data preprocessing technique i.e., calculus enhanced energy operator (CEEO) to preserve the characteristics frequencies in the noisy and inferred vibrational signal. A set of time domain and spectral domain features are calculated from the raw and CEEO vibration signal and inputted to the multiclass support vector machine (MCSVM) to diagnose the faults on the system. An effective comparison between raw signal and CEEO signal are presented to show the impact of CEEO in gearbox fault diagnosis. The obtained results of this work look very promising and can be implemented in any type of industrial system due to its nonparametric nature.

该研究论文介绍了一种整合的齿轮箱故障诊断方法，能够方便地部署在在线条件监测中，通过引入一种非参数的数据预处理技术即微积分增强能量算子（CEEO）来保留嘈杂和推断振动信号中的特征频率，从原始和CEEO振动信号计算一组时域和频谱域特征，并输入到多类支持向量机（MCSVM）中诊断系统故障，通过对比原始信号和CEEO信号的效果来展示CEEO在齿轮箱故障诊断中的影响。该研究取得的结果看起来非常有希望，并且可以在任何类型的工业系统中实施，因为它具有非参数性质。

齿轮箱故障诊断综合方法