In bearing fault diagnosis, ensemble empirical mode decomposition (EEMD) is a reliable technique for treating rolling bearing vibration signals by dividing them into intrinsic mode functions (IMFs). Traditional methods used in EEMD consist of identifying IMFs containing the fault information and reconstructing them. However, an incorrect selection can result in the loss of useful IMFs or the addition of unnecessary ones. To overcome this drawback, this paper presents a novel method called combined modes ensemble empirical mode decomposition (CMEEMD) to directly obtain a combination of useful IMFs containing fault information. This is without needing to pass through the processes of IMF selection and reconstruction, as well as guaranteeing that no defect information is lost. Owing to the small signal-to-noise ratio, this makes it difficult to determine the fault information of a rolling bearing at the early stage. Therefore, improving noise reduction is an essential procedure for detecting defects. The paper introduces a robust process for extracting rolling bearings defect information based on CMEEMD and an enhanced deconvolution technique. Firstly, the proposed CMEEMD extracts all combined modes (CMs) from adjoining IMFs decomposed from the raw fault signal by EEMD. Then, a selection indicator known as kurtosis median absolute deviation (KMAD) is created in this research to identify the combination of the appropriate IMFs. Finally, the enhanced deconvolution process minimizes noise and improves defect identification in the identified CM. Analyzing real and simulated bearing signals demonstrates that the developed method shows excellent performance in extracting defect information. Compared results between selecting the sensitive IMF using kurtosis and selecting the sensitive CM using the proposed KMAD show that the identified CM contains rich fault information in many cases. Furthermore, our comparisons revealed that the enhanced deconvolution approach proposed here outperformed the minimum entropy deconvolution (MED) approach for improving fault pulses and the wavelet de-noising method for noise suppression.

Fault diagnosis in induction machines is an essential phase to maintain good system health. Selecting effective and sensitive Intrinsic Mode Functions (IMFs) is one of the important steps for rolling bearing fault diagnosis. Kurtosis is the most commonly used parameter for the selection of these IMFs. Generally, two selection methods for IMFs based on the kurtosis criterion are used in the fault detection field. The first one is to select the IMFs with high kurtosis, and the second selection method is based on the best IMF with the highest kurtosis value (Max Kurtosis value). This paper pointed out the detailed study of the usual IMF selection methods based on kurtosis value to detect rolling element bearing faults. This study treated the experimental data (vibration signal) using Ensemble Empirical Mode Decomposition (EEMD). In addition, this work used two IMF selection methods carefully to find the best decision about bearing fault. The envelope spectrum of the selected IMFs is well analyzed to define the characteristic frequencies of the bearing fault. The results clearly showed what we could face using the IMF selection methods based on kurtosis criterion.

The detection of faults in induction machines (IMs) is crucial for maintaining their optimal performance and extending their lifespan. Bearing faults, in particular, can have a significant impact on the efficiency and reliability of these machines. Ensemble Empirical Mode Decomposition (EEMD) is an appropriate technique for monitoring bearing health in IMs. This work is to evaluate the effectiveness of EEMD. The aim is to see in which level this technique can enhance the efficiency of bearing fault diagnosis. Our experimental findings indicate that EEMD exhibits greater effectiveness than VMD.