Abstract
Widespread adoption of Prognostics Health Management (PHM) systems can be hampered by hardware cost. One way to reduce cost, and expand the applications of PHM into industry, is to use embedded microprocessors to perform PHM analysis. Embedded PHM is challenging in that micro-controllers have limited processing power and memory. Algorithms commonly used in PHM Analysis are the Time Synchronous Average (TSA), the Fast Fourier transform (FFT), and Bearing Envelope Analysis (BEA). Presented are techniques to facilitate the use of these algorithms in embedded PHM systems. For example the real FFT implemented with a table lookup and Clenshaw's algorithm uses only half of the memory compared to a standard FFT, and no trigonometric functions. This resulted in up to 14X reduction in the processing time against a benchmark FFT. For the envelope analysis (a common bearing vibration analysis), a three step process using heterodyne, filtering and decimation was developed which reduces greatly the memory required while allowing an 8× reduction in processing time. These algorithms are currently running on embedded vibration monitoring systems which incorporate a MEMS accelerometer with a microcontroller for a low er cost PHM system.
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