FPGA-Based Multiple-Channel Vibration Analyzer for Industrial Applications in Induction Motor Failure Detection

Early detection of failures in equipment is one of the most important concerns to industry. Many techniques have been developed for early failure detection in induction motors. There is the necessity of low-cost instrumentation for online multichannel measurement and analysis of vibration in the frequency domain, and this could be fixed to the machine for continuous monitoring to provide a reliable continuous diagnosis without needing trained staff. Field-programmable gate arrays (FPGAs) are distinguished by being very fast and highly reconfigurable devices, allowing the development of scalable parallel architectures for multichannel analysis without changing the internal hardware. The novelty of this work is the development of a low-cost FPGA based on a multichannel vibration analyzer; this is capable of providing an automatic diagnosis of the motor state carrying out online continuous monitoring. To test the functionality of the proposed vibration analyzer, three experiments on 746-W (1-hp) induction motors were carried out. Such experiments are intended to detect motor failures such as broken bars, unbalance, and looseness. The obtained results show the overall system performance.

[1]  Antonio Pietrosanto,et al.  An intelligent FFT-analyzer , 1998, IEEE Trans. Instrum. Meas..

[2]  J. Ilonen,et al.  Diagnosis tool for motor condition monitoring , 2005, IEEE Transactions on Industry Applications.

[3]  René de Jesús Romero-Troncoso,et al.  FPGA based multiple-channel vibration analyzer embedded system for industrial applications in automatic failure detection , 2008, 2008 International Symposium on Industrial Embedded Systems.

[4]  Arturo Garcia-Perez,et al.  Automatic Online Diagnosis Algorithm for Broken-Bar Detection on Induction Motors Based on Discrete Wavelet Transform for FPGA Implementation , 2008, IEEE Transactions on Industrial Electronics.

[5]  Girish Kumar Singh,et al.  Vibration signal analysis using wavelet transform for isolation and identification of electrical faults in induction machine , 2004 .

[6]  Antonello Monti,et al.  Diagnostic of a Faulty Induction Motor Drive via Wavelet Decomposition , 2007, IEEE Transactions on Instrumentation and Measurement.

[7]  Arturo Garcia-Perez,et al.  FPGA Implementation of a Novel Algorithm for on-line Bar Breakage Detection on Induction Motors , 2008, 2008 IEEE Instrumentation and Measurement Technology Conference.

[8]  Antonio Pietrosanto,et al.  A multi-application FFT analyzer based on a DSP architecture , 2001, IEEE Trans. Instrum. Meas..

[9]  G. Betta,et al.  A multi-application FFT analyzer based on a DSP architecture , 1999, IMTC/99. Proceedings of the 16th IEEE Instrumentation and Measurement Technology Conference (Cat. No.99CH36309).

[10]  Michael Neale and Associates A guide to the condition monitoring of machinery , 1979 .

[11]  Mohamed El Hachemi Benbouzid A review of induction motors signature analysis as a medium for faults detection , 2000, IEEE Trans. Ind. Electron..

[12]  Arturo Garcia-Perez,et al.  FPGA based embedded system for induction motor failure monitoring at the start-up transient vibrations with wavelets , 2008, 2008 International Symposium on Industrial Embedded Systems.

[13]  O Gol,et al.  Condition Monitoring of Electrical Machines , 1986 .

[14]  Piotr Bilski,et al.  Virtual spectrum analyzer based on data acquisition card , 2002, IEEE Trans. Instrum. Meas..

[15]  Salvatore Nuccio,et al.  A comparison of spectrum estimation techniques for nonstationary signals in induction motor drive measurements , 2005, IEEE Transactions on Instrumentation and Measurement.

[16]  Heinz P. Bloch,et al.  Machinery failure analysis and troubleshooting , 1983 .

[17]  A. Paolillo,et al.  A DSP-based FFT-analyzer for the fault diagnosis of rotating machine based on vibration analysis , 2001, IMTC 2001. Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference. Rediscovering Measurement in the Age of Informatics (Cat. No.01CH 37188).

[18]  Girish Kumar Singh,et al.  Experimental investigations on induction machine condition monitoring and fault diagnosis using digital signal processing techniques , 2003 .

[19]  Wieslaw Winiecki,et al.  A Low-Cost Real-Time Virtual Spectrum Analyzer , 2005, 2005 IEEE Instrumentationand Measurement Technology Conference Proceedings.

[20]  R. V. Williams,et al.  A guide to the condition monitoring of machinery , 1980 .

[21]  P. Purkait,et al.  Application of Wavelet and Fourier Transforms for Vibration Analysis of Motor , 2005, 2005 Annual IEEE India Conference - Indicon.

[22]  René de Jesús Romero-Troncoso,et al.  VHDL core for 1024-point radix-4 FFT computation , 2005, 2005 International Conference on Reconfigurable Computing and FPGAs (ReConFig'05).

[23]  B. Ayhan,et al.  Multiple signature processing-based fault detection schemes for broken rotor bar in induction motors , 2005, IEEE Transactions on Energy Conversion.

[24]  John G. Proakis,et al.  Digital Signal Processing: Principles, Algorithms, and Applications , 1992 .

[25]  Saleem A. Ansari,et al.  A PC-based vibration analyzer for condition monitoring of process machinery , 1998, IEEE Trans. Instrum. Meas..