A review on empirical mode decomposition in fault diagnosis of rotating machinery

Wavelets for fault diagnosis of rotary machines: A review with applications

Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model

A rolling bearing fault diagnosis approach based on LCD and fuzzy entropy

Health diagnosis of the rotating machinery can identify potential failure at its early stage and reduce severe machine damage and costly machine downtime. In recent years, the adaptive decomposition methods have attracted many researchers’ attention, due to less influences of human operators in the practical application. This paper compares two adaptive methods: local mean decomposition (LMD) and empirical mode decomposition (EMD) from four aspects, i.e., local mean, decomposed components, instantaneous frequency, and the waveletlike filtering characteristic through numerical simulation. The comparative results manifest that more accurate instantaneous frequency and more meaningful interpretation of the signals can be acquired by LMD than by EMD. Then LMD and EMD are both exploited in the health diagnosis of two actual industrial rotating machines with rub-impact and steam-excited vibration faults, respectively. The results reveal that LMD seems to be more suitable and have better performance than EMD for the incipient fault detection. LMD is thus proved to have potential to become a powerful tool for the surveillance and diagnosis of rotating machinery.

A Comparative Study on the Local Mean Decomposition and Empirical Mode Decomposition and Their Applications to Rotating Machinery Health Diagnosis

Cosine window-based boundary processing method for EMD and its application in rubbing fault diagnosis

Rotor crack detection based on high-precision modal parameter identification method and wavelet finite element model

Among the vibration-based fault diagnosis methods for rolling element bearing, the shock pulse method (SPM) combined with the demodulation method is a useful quantitative technique for estimating bearing running state. However, direct demodulation often misestimates the shock value of characteristic defect frequency. To overcome this disadvantage, the vibration signal should be decomposed before demodulation. Empirical mode decomposition (EMD) can be an alternative for preprocess bearing fault signals. However, the trouble with this method’s application is that it is time-consuming. Therefore, a novel method that can improve the sifting process’s efficiency is proposed, in which only one time of cubic spline fitting is required in each sifting process. As a consequence, the time for EMD analysis can be evidently shortened and the decomposition results simultaneously maintained at a high precision. Simulations and experiments verify that the improved EMD method, combined with SPM and demodulation analysis, is efficient and accurate and can be effectively applied in engineering practice.

Sifting process of EMD and its application in rolling element bearing fault diagnosis

Vibration based operational modal analysis of rotor systems

This invention relates to a mode parameter recognition method based on experience mode decomposition and Laplace wavelet which comprises the following steps: first, improving the mean solution means in EMD algorithm by present extremum-domain mean pattern decomposition algorithm that is fit to complex impulse response signal and decouples the coupled multi-order modal response signal into multiple single-order signal; then, taking Laplace wavelet dual-step correlation filter to the obtained signal, and identifying the modal parameter rapidly and accurately. This invention has precise result (natural frequency and damping ratio in experiment) and is effective in modal parameter identification.

Keyu Qi

Papers

Cosine window-based boundary processing method for EMD and its application in rubbing fault diagnosis

Rotor crack detection based on high-precision modal parameter identification method and wavelet finite element model

Sifting process of EMD and its application in rolling element bearing fault diagnosis

Vibration based operational modal analysis of rotor systems

Mode parameter recognition method based on experience mode decomposition and Laplace wavelet