Lei Wang, a master program graduate student at School of Aeronautics & Astronautics, has recently published in Mechanical Systems and Signal Processing a research paper “Time-Frequency Analysis Based on Ensemble Local Mean Decomposition and Fast Kurtogram for Rotating Machinery Fault Diagnosis”. Lei Wang is the first author. The corresponding author is his academic supervisor Zhiwen Liu who is an associate research scientist at SCU School of Aeronautics & Astronautics, which is also the first work unit on this research paper. This research paper was entered into ESI database for its high citation rate in June 2018.
Being published by the Dutch publishing house Elsevier, Mechanical Systems and Signal Processing is one of the top jounals in the field of mechanical fault diagnosis. It reports scientific advancements of the highest quality arising from new techniques in sensing, instrumentation, signal processing, modelling and control of dynamic systems. It has an impact factor of 4.37 in 2018.
This paper proposes a novel time–frequency analysis method based on ELMD and FK. The ELMD is used to decompose non-stationary signal and obtain PF components. The FK is used to detect impact components from the sensitive PF component. The proposed method proves to be highly efficient by experiments. “The proposed method combines the merits of Ensemble LMD and fast kurtogram (FK) to detect the fault for rotating machinery. Primarily, by applying ELMD the raw signal is decomposed into a set of product functions (PFs). Then, the PF which mostly characterizes fault information is selected according to kurtosis index. Finally, the selected PF signal is further filtered by an optimal band-pass filter based on FK to extract impulse signal. Fault identification can be deduced by the appearance of fault characteristic frequencies in the squared envelope spectrum of the filtered signal.”
This research project was funded by Civial Aviation Joint Fund of National Natural Science Foundation of China and China Center University Basic Research Business Expenses Fund.
Table 1 (a)Bevel gear raw signals，(b) FFT spectrum
Table 2 illustrates the results based on ELMD_FK analysis
To read the research paper, please go to the link below：
https://www.sciencedirect.com/science/article/pii/S0888327017305253
