Rotating machineries are widely used in manufacturing, petroleum, chemical, aircraft, and other industries. To accurately identify the operating conditions of such rotating machineries, this paper aims to propose a fault diagnosis method based on sensitive dimensionless parameters and particle swarm optimization (PSO)–support vector machine (SVM) for reducing the unexpected downtime and economic losses.,A relatively new hybrid intelligent fault classification approach is proposed by integrating multiple dimensionless parameters, the Fisher criterion and PSO–SVM. In terms of data pre-processing, a method based on wavelet packet decomposition (WPD), empirical mode decomposition (EMD) and dimensionless parameters is proposed for the extraction of the vibration signal features. The Fisher criterion is applied to reduce the redundant dimensionless parameters and search for the sensitive dimensionless parameters. Then, PSO is adapted to optimize the penalty parameter and kernel parameter for SVM. Finally, the sensitive dimensionless parameters are classified with the optimized model.,As two different time–frequency analysis methods, a method based on a combination of WPD and EMD used to extract multiple dimensionless parameters is presented. More vital diagnosis information can be obtained from the vibration signals than by only using a single time–frequency analysis method. Besides, a fault classification approach combining the sensitive dimensionless parameters and PSO-SVM classifier is proposed. The comparative experiment results show that the proposed method has a high classification accuracy and efficiency.,To the best of the authors’ knowledge, very few efforts have been performed for fault classification using multiple dimensionless parameters. In this paper, eighty dimensionless parameters have been studied intensively, which provides a new strategy in fault diagnosis field.

中文翻译：

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敏感无量纲参数和 PSO-SVM 在旋转机械故障分类中的应用

旋转机械广泛应用于制造业、石油、化工、飞机等行业。为了准确识别此类旋转机械的运行状况，本文旨在提出一种基于敏感无量纲参数和粒子群优化（PSO）-支持向量机（SVM）的故障诊断方法，以减少意外停机时间和经济损失。,A通过集成多个无量纲参数、Fisher 准则和 PSO-SVM，提出了相对较新的混合智能故障分类方法。在数据预处理方面，提出了一种基于小波包分解(WPD)、经验模态分解(EMD)和无量纲参数的方法来提取振动信号特征。Fisher准则用于减少冗余无量纲参数并搜索敏感的无量纲参数。然后，采用 PSO 来优化 SVM 的惩罚参数和核参数。最后,利用优化后的模型对敏感的无量纲参数进行分类。作为两种不同的时频分析方法,提出了一种基于WPD和EMD结合提取多个无量纲参数的方法。与仅使用单一时频分析方法相比，可以从振动信号中获得更重要的诊断信息。此外，提出了一种结合敏感无量纲参数和PSO-SVM分类器的故障分类方法。对比实验结果表明，该方法具有较高的分类准确率和效率。据作者所知，使用多个无量纲参数进行故障分类的工作很少。本文对80个无量纲参数进行了深入研究，为故障诊断领域提供了一种新的策略。