Embedded EMD algorithm within an FPGA-based design to classify nonlinear SDOF systems

Jonathan D. Jones; Jin-Song Pei; Joseph P. Wright; Monte P. Tull

doi:10.1117/12.847889

31 March 2010 Embedded EMD algorithm within an FPGA-based design to classify nonlinear SDOF systems

Jonathan D. Jones, Jin-Song Pei, Joseph P. Wright, Monte P. Tull

Proceedings Volume 7647, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2010; 76470E (2010) https://doi.org/10.1117/12.847889
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2010, San Diego, California, United States

Abstract

Compared with traditional microprocessor-based systems, rapidly advancing field-programmable gate array (FPGA) technology offers a more powerful, efficient and flexible hardware platform. An FPGA and microprocessor (i.e., hardware and software) co-design is developed to classify three types of nonlinearities (including linear, hardening and softening) of a single-degree-of-freedom (SDOF) system subjected to free vibration. This significantly advances the team's previous work on using FPGAs for wireless structural health monitoring. The classification is achieved by embedding two important algorithms - empirical mode decomposition (EMD) and backbone curve analysis. Design considerations to embed EMD in FPGA and microprocessor are discussed. In particular, the implementation of cubic spline fitting and the challenges encountered using both hardware and software environments are discussed. The backbone curve technique is fully implemented within the FPGA hardware and used to extract instantaneous characteristics from the uniformly distributed data sets produced by the EMD algorithm as presented in a previous SPIE conference by the team. An off-the-shelf high-level abstraction tool along with the MATLAB/Simulink environment is utilized to manage the overall FPGA and microprocessor co-design. Given the limited computational resources of an embedded system, we strive for a balance between the maximization of computational efficiency and minimization of resource utilization. The value of this study lies well beyond merely programming existing algorithms in hardware and software. Among others, extensive and intensive judgment is exercised involving experiences and insights with these algorithms, which renders processed instantaneous characteristics of the signals that are well-suited for wireless transmission.

Citation Download Citation

Jonathan D. Jones, Jin-Song Pei, Joseph P. Wright, and Monte P. Tull "Embedded EMD algorithm within an FPGA-based design to classify nonlinear SDOF systems", Proc. SPIE 7647, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2010, 76470E (31 March 2010); https://doi.org/10.1117/12.847889

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