Skip to main content
SpringerLink
Log in

Signal Based Fault Detection and Diagnosis for Rotating Electrical Machines: Issues and Solutions

  • Chapter
  • First Online:
Complex System Modelling and Control Through Intelligent Soft Computations

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 319))

Abstract

Complex systems are found in almost all field of contemporary science and are associated with a wide variety of financial, physical, biological, information and social systems. Complex systems modelling could be addressed by signal based procedures, which are able to learn the complex system dynamics from data provided by sensors, which are installed on the system in order to monitor its physical variables. In this chapter the aim of diagnosis is to detect if the electrical machine is healthy or a change is occurring due to abnormal events and, in addition, the probable causes of the abnormal events. Diagnosis is addressed by developing machine learning procedures in order to classify the probable causes of deviations from system normal events. This chapter presents two Fault Detection and Diagnosis solutions for rotating electrical machines by signal based approaches. The first one uses a current signature analysis technique based on Kernel Density Estimation and Kullback–Liebler divergence. The second one presents a vibration signature analysis technique based on Multi-Scale Principal Component Analysis. Several simulations and experimentations on real electric motors are carried out in order to verify the effectiveness of the proposed solutions. The results show that the proposed signal based diagnosis procedures are able to detect and diagnose different electric motor faults and defects, improving the reliability of electrical machines. Fault Detection and Diagnosis algorithms could be used not only with the fault diagnosis purpose but also in a Quality Control scenario. In fact, they can be integrated in test benches at the end or in the middle of the production line in order to test the machines quality. When the electric motors reach the test benches, the sensors acquire measurements and the Fault Detection and Diagnosis procedures detect if the motor is healthy or faulty, in this last case further inspections can diagnose the fault.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Abdelmadjid, G., Mohamed, B. S., Mohamed, T., Ahmed, S., & Youcef, M. (2013). An improved stator winding fault tolerance architecture for vector control of induction motor: Theory and experiment. Electric Power Systems Research, 104, 129–137.

    Article  Google Scholar 

  • Antonino-Daviu, J., Riera-Guasp, M., Roger-Folch, J., Martnez-Giménez, F., & Peris, A. (2006). Application and optimization of the discrete wavelet transform for the detection of broken rotor bars in induction machines. Applied and Computational Harmonic Analysis, 21(2), 268–279.

    Article  MATH  MathSciNet  Google Scholar 

  • Baccarini, L. M. R., Tavares, J. P. B., de Menezes, B. R., & Caminhas, W. M. (2010). Sliding mode observer for on-line broken rotor bar detection. Electric Power Systems Research, 80(9), 1089–1095.

    Article  Google Scholar 

  • Bakshi, B. R. (1998). Multiscale PCA with application to multivariate statistical process monitoring. AIChE Journal, 44(7), 1596–1610.

    Article  Google Scholar 

  • Bangura, J., Povinelli, R., Demerdash, N. A. O., & Brown, R. (2003). Diagnostics of eccentricities and bar/end-ring connector breakages in polyphase induction motors through a combination of time-series data mining and time-stepping coupled FE-state-space techniques. IEEE Transactions on Industry Applications, 39(4), 1005–1013.

    Article  Google Scholar 

  • Benloucif, M., & Balaska, H. (2006). Robust fault detection for an induction machine. In Automation Congress, 2006. WAC ‘06. World (pp. 1–6). Budapest.

    Google Scholar 

  • Bouzida, A., Touhami, O., Ibtiouen, R., Belouchrani, A., Fadel, M., & Rezzoug, A. (2011). Fault diagnosis in industrial induction machines through discrete wavelet transform. IEEE Transactions on Industrial Electronics, 58(9), 4385–4395.

    Article  Google Scholar 

  • Case Western Reserve University Bearing Data Center. (2014). Bearing Data Center.

    Google Scholar 

  • Cheng, H., Nikus, M., & Jamsa-Jounela, S. L. (2008). Evaluation of PCA methods with improved fault isolation capabilities on a paper machine simulator. Chemometrics and Intelligent Laboratory Systems, 92(2), 186–199.

    Article  Google Scholar 

  • Chiang, L. H., Russell, E. L., & Braatz, R. D. (2000). Fault diagnosis in chemical processes using Fischer discriminant analysis, discriminant partial least squares, and principal component analysis. Chemometrics and Intelligent Laboratory Systems, 50(2), 243–252.

    Article  Google Scholar 

  • Chilengue, Z., Dente, J., & Branco, P. C. (2011). An artificial immune system approach for fault detection in the stator and rotor circuits of induction machines. Electric Power Systems Research, 81(1), 158–169.

    Article  Google Scholar 

  • Ciabattoni, L., Corradini, M., Grisostomi, M., Ippoliti, G., Longhi, S., & Orlando, G. (2011a). Adaptive extended Kalman filter for robust sensorless control of PMSM drives. In Proceedings of the IEEE Conference on Decision and Control (pp. 934–939).

    Google Scholar 

  • Ciabattoni, L., Corradini, M., Grisostomi, M., Ippoliti, G., Longhi, S., & Orlando, G. (2014). A discrete-time vs controller based on RBF neural networks for PMSM drives. Asian Journal of Control, 16(2), 396–408.

    Article  MATH  MathSciNet  Google Scholar 

  • Ciabattoni, L., Grisostomi, M., Ippoliti, G., & Longhi, S. (2011b). Estimation of rotor position and speed for sensorless DSP-based PMSM drives. In 2011 19th Mediterranean Conference on Control and Automation, MED 2011 (pp. 1421–1426).

    Google Scholar 

  • Ciandrini, C., Gallieri, M., Giantomassi, A., Ippoliti, G., & Longhi, S. (2010). Fault detection and prognosis methods for a monitoring system of rotating electrical machines. In Industrial Electronics (ISIE), 2010 IEEE International Symposium on (pp. 2085–2090).

    Google Scholar 

  • Comaniciu, D. (2003). An algorithm for data-driven bandwidth selection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(2), 281–288.

    Article  Google Scholar 

  • Daubechies, I. (1988). Orthonormal bases of compactly supported wavelets. Communications on Pure and Applied Mathematics, 41(7), 909–996.

    Article  MATH  MathSciNet  Google Scholar 

  • Dunia, R., & Joe Qin, S. (1998). Joint diagnosis of process and sensor faults using principal component analysis. Control Engineering Practice, 6(4), 457–469.

    Article  Google Scholar 

  • Fan, Y., & Zheng, G. (2007). Research of high-resolution vibration signal detection technique and application to mechanical fault diagnosis. Mechanical Systems and Signal Processing, 21(2), 678–687.

    Article  Google Scholar 

  • Feki, N., Clerc, G., & Velex, P. (2013). Gear and motor fault modeling and detection based on motor current analysis. Electric Power Systems Research, 95, 28–37.

    Article  Google Scholar 

  • Ferracuti, F., Giantomassi, A., Iarlori, S., Ippoliti, G., & Longhi, S. (2013a). Induction motor fault detection and diagnosis using KDE and Kullback–Leibler divergence. In Industrial Electronics Society, IECON 2013—39th Annual Conference of the IEEE (pp. 2923–2928).

    Google Scholar 

  • Ferracuti, F., Giantomassi, A., Ippoliti, G., & Longhi, S. (2010). Multi-scale PCA based fault diagnosis for rotating electrical machines. In European workshop on advanced control and diagnosis, 8th ACD (pp. 296–301). Ferrara, Italy.

    Google Scholar 

  • Ferracuti, F., Giantomassi, A., & Longhi, S. (2013b). MSPCA with KDE thresholding to support QC in electrical motors production line. In Manufacturing Modelling, Management, and Control, 7, 1542–1547.

    Google Scholar 

  • Ferracuti, F., Giantomassi, A., Longhi, S., & Bergantino, N. (2011). Multi-scale PCA based fault diagnosis on a paper mill plant. In Emerging Technologies Factory Automation (ETFA), 2011 IEEE 16th Conference on (pp. 1–8).

    Google Scholar 

  • Filbert, D., & Metzger, L. (1982). Quality test of systems by parameter estimation. In Proceedings of 9th IMEKO congress. Berlin, Germany.

    Google Scholar 

  • Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188.

    Article  Google Scholar 

  • Gani, A., & Salami, M. (2002). Vibration faults simulation system (VFSS): A system for teaching and training on fault detection and diagnosis. In Research and Development, 2002. SCOReD 2002. Student Conference on (pp. 15–18).

    Google Scholar 

  • Giantomassi, A. (2012). Modeling estimation and identification of complex system dynamics. LAP Lambert Academic Publishing.

    Google Scholar 

  • Giantomassi, A., Ferracuti, F., Benini, A., Longhi, S., Ippoliti, G., & Petrucci, A. (2011). Hidden Markov model for health estimation and prognosis of turbofan engines. In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (pp. 1–6).

    Google Scholar 

  • Hua, L., Zhanfeng, L., & Zhaowei. (2009). Time frequency distribution for vibration signal analysis with application to turbo-generator fault diagnosis. In Control and Decision Conference, 2009. CCDC ‘09. Chinese (pp. 5492–5495). Guilin.

    Google Scholar 

  • Immovilli, F., Bellini, A., Rubini, R., & Tassoni, C. (2010). Diagnosis of bearing faults in induction machines by vibration or current signals: A critical comparison. IEEE Transactions on Industry Applications, 46(4), 1350–1359.

    Article  Google Scholar 

  • Isermann, R. (1984). Process fault detection on modeling and estimation methods? A survey. Automatica, 20(4), 387–404.

    Article  MATH  Google Scholar 

  • Isermann, R. (2006). Fault-diagnosis dystems. Berlin: Springer.

    Book  Google Scholar 

  • Isermann, R., & Balle, P. (1997). Trends in the application of model-based fault detection and diagnosis of technical processes. Control Engineering Practice, 5(5), 709–719.

    Article  Google Scholar 

  • Jackson, J. E. (2003). A user’s guide to principal components (Vol. 587). New York: Wiley-Interscience.

    Google Scholar 

  • Jackson, J. E., & Mudholkar, G. S. (1979). Control procedures for residuals associated with principal component analysis. Technometrics, 21(3), 341–349.

    Article  MATH  Google Scholar 

  • Jolliffe, I. T. (2002). Principal component analysis. Berlin: Springer.

    MATH  Google Scholar 

  • Keogh, E. (2013). UCR time series data mining archive.

    Google Scholar 

  • Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. Annals of Mathematical Statistics, 22(1), 79–86.

    Article  MATH  MathSciNet  Google Scholar 

  • Lachouri, A., Baiche, K., Djeghader, R., Doghmane, N., & Ouhtati, S. (2008). Analyze and fault diagnosis by Multi-scale PCA. In Information and communication technologies: From theory to applications, 2008. ICTTA 2008. 3rd International Conference on (pp. 1–6).

    Google Scholar 

  • Li, W., Yue, H. H., Valle-Cervantes, S., & Qin, S. J. (2000). Recursive PCA for adaptive process monitoring. Journal of Process Control, 10(5), 471–486.

    Article  Google Scholar 

  • Li, X., Dong, S., & Yuan, Z. (1999). Discrete wavelet transform for tool breakage monitoring. International Journal of Machine Tools and Manufacture, 99(12), 1944–1955.

    Google Scholar 

  • Mallat, S. G. (1989). A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674–693.

    Article  MATH  Google Scholar 

  • Misra, M., Yue, H., Qin, S., & Ling, C. (2002). Multivariate process monitoring and fault diagnosis by multi-scale PCA. Computers and Chemical Engineering, 26(9), 1281–1293.

    Article  Google Scholar 

  • Mugdadi, A., & Ahmad, I. A. (2004). A bandwidth selection for kernel density estimation of functions of random variables. Computational Statistics and Data Analysis, 47(1), 49–62.

    Article  MATH  MathSciNet  Google Scholar 

  • Odiowei, P.-E., & Cao, Y. (2010). Nonlinear dynamic process monitoring using canonical variate analysis and kernel density estimations. IEEE Transactions on Industrial Informatics, 6(1), 36–45.

    Article  Google Scholar 

  • Parzen, E. (1962). On estimation of a probability density function and mode. The Annals of Mathematical Statistics, 33(3), 1065–1076.

    Article  MATH  MathSciNet  Google Scholar 

  • Patton, R., Uppal, F., & Lopez-Toribio, C. (2000). Soft computing approaches to fault diagnosis for dynamic systems: a survey. In 4th IFAC Symposium on Fault Detection supervision and Safety for Technical Processes (pp. 298–311). Budapest, Hungary.

    Google Scholar 

  • Pedra, J., Candela, I., & Barrera, A. (2009). Saturation model for squirrel-cage induction motors. Electric Power Systems Research, 79(7), 1054–1061.

    Article  Google Scholar 

  • Ran, L., & Penman, J. (2008). Condition monitoring of rotating electrical machines (Vol. 56). London, United Kingdom: Institution of Engineering and Technology.

    Google Scholar 

  • Rao, C. R. (1948). The utilization of multiple measurements in problems of biological classification. Journal of the Royal Statistical Society—Series B, 10(2), 159–203.

    MATH  Google Scholar 

  • Rodriguez, P., Belahcen, A., & Arkkio, A. (2006). Signatures of electrical faults in the force distribution and vibration pattern of induction motors. Electric Power Applications, IEE Proceedings, 153(4), 523–529.

    Google Scholar 

  • Samsi, R., Ray, A., & Mayer, J. (2009). Early detection of stator voltage imbalance in three-phase induction motors. Electric Power Systems Research, 79(1), 239–245.

    Article  Google Scholar 

  • Sawalhi, N., & Randall, R. (2008a). Simulating gear and bearing interactions in the presence of faults: Part I. the combined gear bearing dynamic model and the simulation of localised bearing faults. Mechanical Systems and Signal Processing, 22(8), 1924–1951.

    Article  Google Scholar 

  • Sawalhi, N., & Randall, R. (2008b). Simulating gear and bearing interactions in the presence of faults: Part II. simulation of the vibrations produced by extended bearing faults. Mechanical Systems and Signal Processing, 22(8), 1952–1966.

    Article  Google Scholar 

  • Sheather, S. J. (2004). Density estimation. Statistical science (pp. 588–597).

    Google Scholar 

  • Shuting, W., Heming, L., & Yonggang, L. (2002). Adaptive radial basis function network and its application in turbine-generator vibration fault diagnosis. In Power system technology, 2002. Proceedings of PowerCon 2002. International Conference on (Vol. 3 pp. 1607–1610).

    Google Scholar 

  • Singh, G., & Ahmed, S. A. K. S. (2004). Vibration signal analysis using wavelet transform for isolation and identification of electrical faults in induction machine. Electric Power Systems Research, 68(2), 119–136.

    Article  Google Scholar 

  • Stuart, M., Mullins, E., & Drew, E. (1995). Statistical quality control and improvement. European Journal of Operational Research, 88(2), 203–214.

    Article  Google Scholar 

  • Taniguchi, S., Akhmetov, D., & Dote, Y. (1999). Fault detection of rotating machine parts using novel fuzzy neural network. In Systems, Man, and Cybernetics, 1999. IEEE SMC’99 Conference Proceedings. 1999 IEEE International Conference on, (Vol. 1, pp. 365–369). Tokyo.

    Google Scholar 

  • Tavner, P. (2008). Review of condition monitoring of rotating electrical machines. IET Electric Power Applications, 2(4), 215–247.

    Article  Google Scholar 

  • Thomson, W., & Fenger, M. (2001). Current signature analysis to detect induction motor faults. Industry Applications Magazine, IEEE, 7(4), 26–34.

    Article  Google Scholar 

  • Tran, V. T., Yang, B.-S., Oh, M.-S., & Tan, A. C. C. (2009). Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference. Expert Systems with Applications, 36(2, Part 1), 1840–1849.

    Article  Google Scholar 

  • Venkatasubramanian, V., Rengaswamy, R., Yin, K., & Kavuri, S. N. (2000a). A review of process fault detection and diagnosis part I: Quantitative model-based methods. Computers and Chemical Engineering, 27(3), 293–311.

    Article  Google Scholar 

  • Venkatasubramanian, V., Rengaswamy, R., Yin, K., & Kavuri, S. N. (2000b). A review of process fault detection and diagnosis part II: Qualitative models and search strategies. Computers and Chemical Engineering, 27(3), 313–326.

    Article  Google Scholar 

  • Venkatasubramanian, V., Rengaswamy, R., Yin, K., & Kavuri, S. N. (2000c). A review of process fault detection and diagnosis part III: Process history based methods. Computers and Chemical Engineering, 27(3), 327–346.

    Article  Google Scholar 

  • Verucchi, C. J., & Acosta, G. G. (2008). Fault detection and diagnosis techniques in induction electrical machines. IEEE Transactions Latin America, 5(1), 41–49.

    Article  Google Scholar 

  • Wand, M. P., & Jones, M. C. (1994a). Kernel smoothing. United Kingdom: Chapman and Hall CRC.

    Google Scholar 

  • Wand, M. P., & Jones, M. C. (1994b). Multivariate plug-in bandwidth selection. Computational Statistics, 9, 97–116.

    MATH  MathSciNet  Google Scholar 

  • Yang, B.-S., & Kim, K. J. (2006). Application of Dempster-Shafer theory in fault diagnosis of induction motors using vibration and current signals. Mechanical Systems and Signal Processing, 20(2), 403–420.

    Article  MathSciNet  Google Scholar 

  • Yu, J. (2011a). Bearing performance degradation assessment using locality preserving projections. Expert Systems with Applications, 38(6), 7440–7450.

    Article  Google Scholar 

  • Yu, J. (2011b). Bearing performance degradation assessment using locality preserving projections and Gaussian mixture models. Mechanical Systems and Signal Processing, 25(7), 2573–2588.

    Article  Google Scholar 

  • Zhaoxia, W., Fen, L., Shujuan, Y., & Bin, W. (2009). Motor fault diagnosis based on the vibration signal testing and analysis. In Intelligent information technology application, 2009. IITA 2009. Third International Symposium on (Vol. 2, pp. 433–436). Nanchang.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, Via Brecce Bianche, 60131, Ancona, Italy

    Andrea Giantomassi, Francesco Ferracuti, Sabrina Iarlori, Gianluca Ippoliti & Sauro Longhi

Authors
  1. Andrea Giantomassi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Francesco Ferracuti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sabrina Iarlori
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gianluca Ippoliti
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sauro Longhi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrea Giantomassi .

Editor information

Editors and Affiliations

  1. Department of Engineering Design and Mathematics, University of the West of England, Bristol, United Kingdom

    Quanmin Zhu

  2. Faculty of Computers and Information, Benha University, Benha, Egypt

    Ahmad Taher Azar

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Giantomassi, A., Ferracuti, F., Iarlori, S., Ippoliti, G., Longhi, S. (2015). Signal Based Fault Detection and Diagnosis for Rotating Electrical Machines: Issues and Solutions. In: Zhu, Q., Azar, A. (eds) Complex System Modelling and Control Through Intelligent Soft Computations. Studies in Fuzziness and Soft Computing, vol 319. Springer, Cham. https://doi.org/10.1007/978-3-319-12883-2_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-12883-2_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12882-5

  • Online ISBN: 978-3-319-12883-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation