Abstract
To achieve the goal of automated rolling bearing fault diagnosis, a variational mode decomposition (VMD) based diagnosis scheme was proposed. VMD was firstly used to decompose the vibration signals into a series of band-limited intrinsic mode functions (BLIMFs). Subsequently, the multiscale fractal dimension (MSFD) and multiscale energy (MSEN) of each BLIMF were calculated and combined together as features of the original vibration signals. In an attempt to accelerate the classification speed, one-way analysis of variance (ANOVA) test was adopted to extract significant features from the redundant features. Finally, those significant features were fed into the optimized support vector machine (SVM), which was optimized by the genetic algorithm (GA), for classification. Experimental results on the international public Case Western Reserve University bearing data indicate the effectiveness of the proposed method with a classification accuracy of 99.75 % for seven classes. Moreover, our approach also shows good anti-noise performance in different signal-to-noise ratios (SNRs).
Highlights
Rolling bearing is one of the most widely applied objects and the quick-wear parts in rotating machinery
This section is composed of three subsections: the principle of variational mode decomposition (VMD) is presented in the first subsection; subsequently, the multiscale fractal dimension (MSFD) and multiscale energy (MSEN) are proposed in the second subsection;
The experimental results of extracting band-limited intrinsic mode functions (BLIMFs) using VMD are presented in the first part, and the calculated MSFD and MSEN of each BLIMF are presented in the second part
Summary
Rolling bearing is one of the most widely applied objects and the quick-wear parts in rotating machinery. The operation state of the rolling bearing directly affects the performance of the whole rotating machinery system. 30 % of rotating machinery malfunction was caused by the faults of rolling bearing [1]. When the rolling bearing fails, it may lead to the crash of the entire rotating machinery system and directly influence the work efficiency and lower the reliability of the system. The fault diagnosis of rolling bearing can be a useful tool to avoid the halt of rotating machinery, because it can warn the fault information via the vibration signals, electric current and so forth which comes from the sensors seated in the component position. The fault can be found easier and prevented, directly reduce the failures and improve the reliability of the rotating machinery
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