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图1 Graphical abstract

该文亮点：

1.提出了一种频带选取指标（synthetic quantitative index），该指标能有效提取微弱故障特征所在的子频带。

2.提出了一种改进的蝗虫优化算法（improved grasshopper optimization algorithm, IGOA），解决了典型GOA优化效果不佳及不稳定的问题。

Highlights:

1. A synthetic quantitative index is proposed, which can effectively extract the sub-band containing weak fault characteristics.

2. An improved grasshopper optimization algorithm (IGOA) is proposed to solve the problems of poor and unstable optimization performances in the typical GOA.

旋转机械产生早期故障时，相关的故障特征频率因其幅值较为微弱，直接用传统的谱分析手段难以对其进行检测。因此，增强旋转机械微弱故障特征的可检测性，以实现旋转机械的早期故障诊断具有实际意义。目前，基于蝗虫优化算法（grasshopper optimization algorithm, GOA）优化随机共振系统（stochastic resonance system, SR system）的方法是增强旋转机械微弱故障特征可检测性的颇有前景的手段之一。然而，由于GOA算法存在对寻优初始点较为敏感和易于陷入局部最优的缺陷，因而无法得到稳定高效的应用。

鉴于此，福州大学张俊教授团队在《国际机械系统动力学学报（英文）》（International Journal of Mechanical System Dynamics, IJMSD）发表题为“A local spectrum enhancement-based method and its application in incipient fault diagnosis of rotating machinery”的研究论文。该文通过网格搜索算法预设较优初始点的方式，构建了一种改进的GOA算法以克服典型GOA算法优化效果稳定性低的问题。具体而言，该文首先通过遍历频带分割方法（traversal frequency band segmentation method）对原始信号的频带进行分割；其次利用所构建的频带选取指标（synthetic quantitative index）选择信息量最丰富的子频带；然后利用改进GOA算法优化的随机共振系统，对所选频带段内的微弱故障特征进行增强；最后利用包络谱分析进行故障特征提取，实现了旋转机械早期故障诊断。

Abstract:Incipient faults of gears and rolling bearings in rotating machineries are very difficult to identify using traditional envelope analysis methods. To address this challenge, this paper proposes an effective local spectrum enhancement-based diagnostic method that can identify weak fault frequencies in the original complicated raw signals. For this identification purpose, a traversal frequency band segmentation technique is first proposed for dividing the raw signal into a series of subfrequency bands. Then, the proposed synthetic quantitative index is constructed for selecting the most informative local frequency band (ILFB) containing fault features from the divided subfrequency bands. Furthermore, an improved grasshopper optimization algorithm-based stochastic resonance (SR) system is developed for enhancing weak fault features contained in the selected most ILFB with less computation cost. Finally, the enhanced weak fault frequencies are extracted from the output of the SR system using a common spectrum analysis. Two experiments on a laboratory planetary gearbox and an open bearing data set are used to verify the effectuality of the proposed method. The diagnostic results demonstrate that the proposed method can identify incipient faults of gears and bearings in an effective and accurate manner. Furthermore, the advantages of the proposed method are highlighted by comparison with other methods.