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

The empirical mode decomposition (EMD) is reviewed and some questions related to its effective performance are discussed. Its interpretation in terms of AM/FM modulation is done. Solutions for its drawbacks are proposed. Numerical simulations are carried out to empirically evaluate the proposed modified EMD.

/pdf/on-the-hht-its-problems-and-some-solutions-377tmeptp1.pdf

On the HHT, its problems, and some solutions

This paper presents a novel signal processing scheme, bandwidth empirical mode decomposition, and adaptive multiscale morphological analysis (BEMD-AMMA) for early fault diagnosis of rolling bearings. In this scheme, we propose a bandwidth based method to select the best envelope interpolation method. First, multiple envelope algorithms are defined and separately subtracted from the original data to obtain the preintrinsic mode functions (PIMFs). Second, an IMF with the smallest frequency bandwidth is selected to be the optimal IMF (OIMF). Third, this OIMF is subtracted from the original signal, and then repeat the sifting process until the residual is a constant or monotonic. Since the OIMF has the smallest frequency bandwidth, the mode mixing phenomenon can be significantly weakened. After that the OIMFs with clear fault information are used to construct the main component of the original signal. Then, the AMMA is introduced to demodulate the constructed main component. Simulation and experimental vibration signals are employed to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms EMD-AMMA, ensemble empirical mode decomposition-AMMA, and generalized empirical mode decomposition-empirical envelope demodulation in detecting early inner race fault.

Application of Bandwidth EMD and Adaptive Multiscale Morphology Analysis for Incipient Fault Diagnosis of Rolling Bearings

A rotating machinery fault diagnosis method based on local mean decomposition

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

A new envelope algorithm of Hilbert-Huang Transform

Unit step signal or function u(t) appears in many fields, which plays an important role in the research and solution of many problems. But for a long time, this signal has a clear definition when t ≠ 0, but there is no definite definition at t=0 - this paper calls it u (0) uncertainty. Although u (0) uncertainty will not affect the application and related theoretical research of u (t) in many cases, we find that it will do in some cases. We also find that the definite definition of u (0) has been implied in the existing basic theory of signals and systems, that is, u (0) =1/2. A physical meaning of u(0) =1/2 is also given by the double switch circuit. This paper makes the understanding of u(t) and its related theory more profound, comprehensive and accurate, and lays a more solid foundation for establishing a more perfect theoretical system of information, signal, system and so on.

Y.M. Zhong

Papers

A new envelope algorithm of Hilbert-Huang Transform

Value of unit step signal u(t) at t=0