Hardware Design and Implementation for Empirical Mode Decomposition

Hilbert-Huang transform (HHT) is an effective method for analyzing nonlinear systems and nonstationary signals. Empirical mode decomposition (EMD) is the core of HHT. In this paper, a flexible, low cost, and high-performance silicon intellectual property (SIP) core for the EMD is proposed to meet the high-speed requirements of various EMD applications. Variables in the proposed EMD SIP are parameterized as much as possible. Using the proposed auxiliary software system, the EMD SIP generator, users can choose various precisions, data lengths, extrema extraction methods, envelope generation methods, stopping criterion methods, and computation units for different EMD applications. All circuits generated using the proposed EMD SIP generator can be operated at 200 MHz using the 90-nm cell library from the Taiwan Semiconductor Manufacturing Company (TSMC), and they can be easily used for various applications and hardware architectures.

[1]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[2]  Luis Romeral,et al.  Short-Circuit Detection by Means of Empirical Mode Decomposition and Wigner–Ville Distribution for PMSM Running Under Dynamic Condition , 2009, IEEE Transactions on Industrial Electronics.

[3]  Jawad Faiz,et al.  EMD-Based Analysis of Industrial Induction Motors With Broken Rotor Bars for Identification of Operating Point at Different Supply Modes , 2014, IEEE Transactions on Industrial Informatics.

[4]  Elmar Wolfgang Lang,et al.  Sliding Empirical Mode Decomposition , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[5]  Liang-Gee Chen,et al.  On-line empirical mode decomposition biomedical microprocessor for Hilbert Huang transform , 2011, 2011 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[6]  Ruoyu Li,et al.  Plastic Bearing Fault Diagnosis Based on a Two-Step Data Mining Approach , 2013, IEEE Transactions on Industrial Electronics.

[7]  Chrysostomos D. Stylios,et al.  Automatic Pattern Identification Based on the Complex Empirical Mode Decomposition of the Startup Current for the Diagnosis of Rotor Asymmetries in Asynchronous Machines , 2014, IEEE Transactions on Industrial Electronics.

[8]  Manuel Duarte Ortigueira,et al.  On the HHT, its problems, and some solutions , 2008 .

[9]  Wen-Chung Shen,et al.  New Ping-Pong Scheduling for Low-Latency EMD Engine Design in Hilbert–Huang Transform , 2013, IEEE Transactions on Circuits and Systems II: Express Briefs.

[10]  S. S. Shen,et al.  A confidence limit for the empirical mode decomposition and Hilbert spectral analysis , 2003, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[11]  Birendra Biswal,et al.  Automatic Classification of Power Quality Events Using Balanced Neural Tree , 2014, IEEE Transactions on Industrial Electronics.

[12]  Po-Lei Lee,et al.  Hardware Implementation of EMD Using DSP and FPGA for Online Signal Processing , 2011, IEEE Transactions on Industrial Electronics.

[13]  Serhat Cagdas,et al.  FPGA implementation of cubic spline interpolation method for empirical mode decomposition , 2012, 2012 20th Signal Processing and Communications Applications Conference (SIU).

[14]  Bhim Singh,et al.  Power Quality Event Classification Under Noisy Conditions Using EMD-Based De-Noising Techniques , 2014, IEEE Transactions on Industrial Informatics.

[15]  Peng Un Mak,et al.  Hardware-accelerated implementation of EMD , 2010, 2010 3rd International Conference on Biomedical Engineering and Informatics.

[16]  Louis Yu Lu Fast Intrinsic Mode Decomposition of Time Series Data , 2008, ArXiv.

[17]  Ying-Yi Hong,et al.  FPGA Implementation for Real-Time Empirical Mode Decomposition , 2012, IEEE Transactions on Instrumentation and Measurement.

[18]  Shihong Miao,et al.  A Modified Empirical Mode Decomposition Filtering-Based Adaptive Phasor Estimation Algorithm for Removal of Exponentially Decaying DC Offset , 2014, IEEE Transactions on Power Delivery.

[19]  Cheng Junsheng,et al.  Research on the intrinsic mode function (IMF) criterion in EMD method , 2006 .