Algorithms for embedded PHM

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.

[1]  E. Bechhoefer,et al.  Condition monitoring architecture: To reduce total cost of ownership , 2012, 2012 IEEE Conference on Prognostics and Health Management.

[2]  A. Braun,et al.  The Extraction of Periodic Waveforms by Time Domain Averaging , 1975 .

[3]  Johnny,et al.  Emerging Results Using IMD-HUMS in a Black Hawk Assault Battalion , 2005 .

[4]  L. Bertling,et al.  Maintenance Management of Wind Power Systems Using Condition Monitoring Systems—Life Cycle Cost Analysis for Two Case Studies , 2007, IEEE Transactions on Energy Conversion.

[5]  P. D. McFadden,et al.  Detecting Fatigue Cracks in Gears by Amplitude and Phase Demodulation of the Meshing Vibration , 1986 .

[6]  P. D. McFadden,et al.  A revised model for the extraction of periodic waveforms by time domain averaging , 1987 .

[7]  Robert B. Randall,et al.  Vibration-based Condition Monitoring: Industrial, Aerospace and Automotive Applications , 2011 .

[8]  Dennis P. Townsend,et al.  An Analysis of Gear Fault Detection Methods as Applied to Pitting Fatigue Failure Data , 1993 .

[9]  Eric Bechhoefer,et al.  A Review of Time Synchronous Average Algorithms , 2009 .

[10]  P.W. Lehn,et al.  Simulation Model of Wind Turbine 3p Torque Oscillations due to Wind Shear and Tower Shadow , 2006, 2006 IEEE PES Power Systems Conference and Exposition.

[11]  William H. Press,et al.  Numerical recipes in C , 2002 .

[12]  James J. Zakrajsek,et al.  Comparison of Interpolation Methods as Applied to Time Synchronous Averaging , 1999 .

[13]  P.G. McLaren,et al.  Reliability improvement and economic benefits of on-line monitoring systems for large induction machines , 1988, Conference Record of the 1988 IEEE Industry Applications Society Annual Meeting.