PHM Survey : Implementation of Diagnostic Methods for Monitoring Industrial Systems



Published Jun 1, 2019
Abdenour Soualhi Bilal Elyousfi Yasmine Hawwari Kamal Medjaher Guy Clerc Razik Hubert François Guillet


The modernization of industrial sectors involves the use of complex industrial systems and therefore requires condition based maintenance. This one aims at increasing the operational availability and reducing the life-cycle while increasing the reliability and life expectancy of industrial systems. This maintenance also called predictive maintenance is a part of an emerging philosophy called PHM ‘Prognostics and Health Management’. In this paper, the PHM will be emphasized on the existing diagnostic methods used for fault isolation and identification. This depicts an important part of the PHM as it exploits the data given by the signal-processing step and its output is treated by the prognostic part. The diagnostic is mainly classified in three categories that will be highlighted in this paper.

Abstract 468 | PDF Downloads 301



diagnosis, prognosis, fault-tolerant control, reconfigurable control, PHM

Alhelou, H.H. (2019). Fault Detection and Isolation in Power Systems Using Unknown Input Observer. Adv. Cond. Monit. Fault Diagn. Electr. Mach. 38–58.
Alrowaie, F., Kwok, K.E., and Gopaluni, R.B. (2014). Fault Isolation based on General Observer Scheme in Stochastic Non-linear State-Space Models Using Particle Filters.
Angeli, C., and Chatzinikolaou, A. (2004). On-line Fault Detection Techniques for Technical Systems: A survey. Int. J. Comput. Sci. Appl. Vol 1, 12–30.
Ariola, M., Mattei, M., Notaro, I., Corraro, F., and Sollazzo, A. (2015). An SFDI Observer–Based Scheme for a General Aviation Aircraft. Int. J. Appl. Math. Comput. Sci. 25, 149–158.
Atamuradov, V., Medjaher, K., Dersin, P., Zerhouni, N., and Camci, F. (2018). A new adaptive prognostics approach based on hybrid feature selection with application to point machine monitoring. pp. 1–9.
Atoui, M.A., Verron, S., and Kobi, A. (2016). A Bayesian network dealing with measurements and residuals for system monitoring. Trans. Inst. Meas. Control 38.
Baraldi, P., Cannarile, F., Di Maio, F., and Zio, E. (2016). Hierarchical k-nearest neighbours classification and binary differential evolution for fault diagnostics of automotive bearings operating under variable conditions. Eng. Appl. Artif. Intell. 56, 1–13.
Ben Ali, J., Fnaiech, N., Saidi, L., Chebel-Morello, B., and Fnaiech, F. (2015). Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Appl. Acoust. 89, 16–27.
Benkouider, A.M., Kessas, R., Yahiaoui, A., Buvat, J.C., and Guella, S. (2012). A hybrid approach to faults detection and diagnosis in batch and semi-batch reactors by using EKF and neural network classifier. J. Loss Prev. Process Ind. 25, 694–702.
Blesa, J., Puig, V., Saludes, J., and Fernández-Cantí, R.M. (2016). Set-membership parity space approach for fault detection in linear uncertain dynamic systems. Int. J. Adapt. Control Signal Process. 30, 186–205.
Broomhead, D.S. (1988). Radial Basis Functions, Multivariable Functional Interpolation and Adaptive Networks (Royals Signals & Radar Establishment).
Capolino, G.-A., Antonino-Daviu, J.A., and Riera-Guasp, M. (2015). Modern Diagnostics Techniques for Electrical Machines, Power Electronics, and Drives. IEEE Trans. Ind. Electron. 62, 1738–1745.
Casimir, R., Boutleux, E., Clerc, G., and Chappuis, F. (2003a). Broken bars detection in an induction motor by pattern recognition. (IEEE), pp. 313–319.
Casimir, R., Boutleux, E., and Clerc, G. (2003b). Fault diagnosis in an induction motor by pattern recognition methods. (IEEE), pp. 294–299.
Chen, W., and Saif, M. (2007). Observer-based strategies for actuator fault detection, isolation and estimation for certain class of uncertain nonlinear systems. IET Control Theory Amp Appl. 1, 1672–1680.
Cottrell, M., Gaubert, P., Eloy, C., François, D., Hallaux, G., Lacaille, J., and Verleysen, M. (2009). Fault Prediction in Aircraft Engines Using Self-Organizing Maps. In Advances in Self-Organizing Maps, (Springer, Berlin, Heidelberg), pp. 37–44.
Dash, S., Maurya, M.R., Venkatasubramanian, V., and Rengaswamy, R. (2004). A novel interval-halving framework for automated identification of process trends. AIChE J. 50, 149–162.
Dey, S., Pisu, P., and Ayalew, B. (2015). A Comparative Study of Three Fault Diagnosis Schemes for Wind Turbines. IEEE Trans. Control Syst. Technol. 23, 1853–1868.
Ding, F., Wang, X., Chen, Q., and Xiao, Y. (2016). Recursive Least Squares Parameter Estimation for a Class of Output Nonlinear Systems Based on the Model Decomposition. Circuits Syst. Signal Process. 35, 3323–3338.
Ding, S., Zhang, P., Ding, E., Naik, A., Deng, P., and Gui, W. (2010). On the application of PCA technique to fault diagnosis. Tsinghua Sci. Technol. 15, 138–144.
Ding, S.X., Zhang, P., Jeinsch, T., Ding, E.L., Engel, P., and Gui, W. (2011). A survey of the application of basic data-driven and model-based methods in process monitoring and fault diagnosis. IFAC Proc. Vol. 44, 12380–12388.
Duda, R.O., Hart, P.E., and Stork, D.G. (2012). Pattern Classification (John Wiley & Sons).
Eissa, M.A., Darwish, R.R., Bassiuny, A.M., Eissa, M.A., Darwish, R.R., and Bassiuny, A.M. (2019). New Model-Based Fault Detection Approach using Black Box Observer. J. Mechatron. Robot. 3, 42–51.
Elghazel, W., Bahi, J., Guyeux, C., Hakem, M., Medjaher, K., and Zerhouni, N. (2015). Dependability of wireless sensor networks for industrial prognostics and health management. Comput. Ind. 68, 1–15.
Fontes, C.H., and Pereira, O. (2016). Pattern recognition in multivariate time series – A case study applied to fault detection in a gas turbine. Eng. Appl. Artif. Intell. 49, 10–18.
Gao, D., Wu, C., Zhang, B., and Ma, X. (2010). Signed Directed Graph and Qualitative Trend Analysis Based Fault Diagnosis in Chemical Industry. Chin. J. Chem. Eng. 18, 265–276.
Gao, Z., Cecati, C., and Ding, S.X. (2015). A survey of fault diagnosis and fault-tolerant techniques- Part I: fault diagnosis With model-based and signal-based approaches. IEEE Trans. Ind. Electron. 62, 3757–3767.
Gertler, J. (2017). Fault Detection and Diagnosis in Engineering Systems (CRC Press).
Ghimire, R., Sankavaram, C., Ghahari, A., Pattipati, K., Ghoneim, Y., Howell, M., and Salman, M. (2011). Integrated model-based and data-driven fault detection and diagnosis approach for an automotive electric power steering system. (IEEE), pp. 70–77.
Ghosh, K., Ng, Y.S., and Srinivasan, R. (2011). Evaluation of decision fusion strategies for effective collaboration among heterogeneous fault diagnostic methods. Scopus.
Glowacz, A., and Glowacz, Z. (2017). Diagnosis of stator faults of the single-phase induction motor using acoustic signals. Appl. Acoust. 117, 20–27.
Guo, Y., Wang, J., Chen, H., Li, G., Huang, R., Yuan, Y., Ahmad, T., and Sun, S. (2019). An expert rule-based fault diagnosis strategy for variable refrigerant flow air conditioning systems. Appl. Therm. Eng. 149, 1223–1235.
Hafaifa, A., Guemana, M., and Daoudi, A. (2015). Vibrations supervision in gas turbine based on parity space approach to increasing efficiency. J. Vib. Control 21, 1622–1632.
Harmouche, J. (2014). Statistical Incipient Fault Detection and Diagnosis with Kullback-Leibler Divergence : from Theory to Applications (Supélec).
He, H., Liu, Z., and Hua, Y. (2015). Adaptive Extended Kalman Filter Based Fault Detection and Isolation for a Lithium-Ion Battery Pack. Energy Procedia 75, 1950–1955.
Hua, J., Lu, L., Ouyang, M., Li, J., and Xu, L. (2011). Proton exchange membrane fuel cell system diagnosis based on the signed directed graph method. J. Power Sources 196, 5881–5888.
Hwang, I., KIM, S., KIM, Y., and SEAH, C.E. (2010). A Survey of Fault Detection, Isolation, and Reconfiguration Methods. Surv. Fault Detect. Isol. Reconfiguration Methods 18, 636–653.
Idrissi, I., El bachtiri, R., and Chafouk, H. (2017). A Bank of Kalman Filters for Current Sensors Faults Detection and Isolation of DFIG for Wind Turbine. (IEEE), pp. 1–6.
Jardine, A.K.S., Lin, D., and Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Signal Process. 20, 1483–1510.
Jia, F., Lei, Y., Lu, N., and Xing, S. (2018). Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization. Mech. Syst. Signal Process. 110, 349–367.
Jiang, T., Khorasani, K., and Tafazoli, S. (2008). Parameter Estimation-Based Fault Detection, Isolation and Recovery for Nonlinear Satellite Models. IEEE Trans. Control Syst. Technol. 16, 799–808.
Jiang, Y., An, B., Huo, M., and Yin, S. (2018). Design Approach to MIMO Diagnostic Observer and its Application to Fault Detection. (IEEE), pp. 5377–5382.
Jing, C., and Hou, J. (2015). SVM and PCA based fault classification approaches for complicated industrial process. Neurocomputing 167, 636–642.
Jlassi, I., Estima, J.O., El Khil, S.K., Bellaaj, N.M., and Cardoso, A.J.M. (2017). A Robust Observer-Based Method for IGBTs and Current Sensors Fault Diagnosis in Voltage Source Inverters of PMSM Drives. IEEE Trans. Ind. Appl. 53, 2894–2905.
Kim, Y.-H., Youn, Y.-W., Hwang, D.-H., Sun, J.-H., and Kang, D.-S. (2013). High-Resolution Parameter Estimation Method to Identify Broken Rotor Bar Faults in Induction Motors. IEEE Trans. Ind. Electron. 60, 4103–4117.
Kohonen, T. (2001). Self-Organizing Maps 3rd edition. Kurban, T., and Beşdok, E. (2009). A Comparison of RBF Neural Network Training Algorithms for Inertial Sensor Based Terrain Classification. Sensors 9, 6312–6329.
Laamami, S., Ben, H.M., and Sbita, L. (2015). Fault detection for nonlinear systems: Parity space approach. (IEEE), pp. 1–5.
Leski, J.M. (2016). Fuzzy c-ordered-means clustering. Fuzzy Sets Syst. 286, 114–133.
Lin, D., Wiseman, M., Banjevic, D., Jardine, A.K.S., Lin, D., Wiseman, M., Banjevic, D., and Jardine, A.K.S. (2004). An approach to signal processing and conditionbased maintenance for gearboxes subject to tooth failure. Mech. Syst. Signal Process. 18, 993.
Liu, R., Yang, B., Zio, E., and Chen, X. (2018). Artificial intelligence for fault diagnosis of rotating machinery: A review. Mech. Syst. Signal Process. 108, 33–47.
Liu, Y.-J., Meng, Q.-H., Zeng, M., and Ma, S.-G. (2016). Fault diagnosis method based on probability extended SDG and fault index. In 2016 12th World Congress on Intelligent Control and Automation (WCICA), (Guilin, China: IEEE), pp. 2868–2873.
Lu, D., and Qiao, W. (2013). Adaptive feature extraction and SVM classification for real-time fault diagnosis of drivetrain gearboxes. (IEEE), pp. 3934–3940.
Luo, J., Namburu, M., Pattipati, K.R., Qiao, L., and Chigusa, S. (2010). Integrated Model-Based and Data-Driven Diagnosis of Automotive Antilock Braking Systems. IEEE Trans. Syst. Man Cybern. - Part Syst. Hum. 40, 321–336.
Maurya, M.R., Rengaswamy, R., and Venkatasubramanian, V. (2007). A Signed Directed Graph and Qualitative Trend Analysis-Based Framework for Incipient Fault Diagnosis. Chem. Eng. Res. Des. 85, 1407–1422.
Moshou, D., Kateris, D., Sawalhi, N., Loutridis, S., and Gravalos, I. (2010). Fault Severity Estimation in Rotating Mechanical Systems Using Feature Based Fusion and Self-Organizing Maps. In Artificial Neural Networks – ICANN 2010, (Springer, Berlin, Heidelberg), pp. 410–413.
msaaf, mohammed, and Belmajdoub, F. (2015). L’application des réseaux de neurone de type “ feedforward ” dans le diagnostic statique. In Xème Conférence Internationale: Conception et Production Intégrées, (Tanger, Morocco).
Nouri.Gharahasanlou, A., Mokhtarei, A., Khodayarei, A., and Ataei, M. (2014). Fault tree analysis of failure cause of crushing plant and mixing bed hall at Khoy cement factory in Iran. Case Stud. Eng. Fail. Anal. 2, 33–38.
Ondel, O., Boutleux, E., and Clerc, G. (2006). A method to detect broken bars in induction machine using pattern recognition techniques. IEEE Trans. Ind. Appl. 42, 916–923.
Palak, J. (2017). A Luenberger Observer-Based Fault Detection and Identification Scheme for Photovoltaic DCDC Converters.
Pontoppidan, N.H., and Larsen, J. (2003). Unsupervised condition change detection in large diesel engines. (IEEE), pp. 565–574.
Purba, J.H. (2014). A fuzzy-based reliability approach to evaluate basic events of fault tree analysis for nuclear power plant probabilistic safety assessment. Ann. Nucl. Energy 70, 21–29.
Rosenblatt, F. (1958). The Perceptron: A Probabilistic Model for Information Storage and Organization in The Brain. Psychol. Rev. 65–386.
Ruijters, E., and Stoelinga, M. (2015). Fault tree analysis: A survey of the state-of-the-art in modeling, analysis and tools. Comput. Sci. Rev. 15–16, 29–62.
Saludes, S., Corrales, A., de Miguel, L.J., and Perán, J.R. (2003). A SOM and Expert System Based Scheme for Fault Detection and Isolation in a Hydroelectric Power Station. IFAC Proc. Vol. 36, 999–1004.
Saravanakumar, R., Manimozhi, M., Kothari, D.P., and Tejenosh, M. (2014). Simulation of Sensor Fault Diagnosis for Wind Turbine Generators DFIG and PMSM Using Kalman Filter. Energy Procedia 54, 494–505.
Siswantoro, J., Prabuwono, A.S., Abdullah, A., and Idrus, B. (2016). A linear model based on Kalman filter for improving neural network classification performance. Expert Syst. Appl. 49, 112–122.
Skliros, C., Esperon Miguez, M., Fakhre, A., and Jennions, I.K. (2019). A review of model based and data driven methods targeting hardware systems diagnostics. Diagnostyka Vol. 20, No. 1.
Sliskovic, D., Grbic, R., and Hocenski, Ž. (2012). Multivariate statistical process monitoring.
Soualhi, A., Clerc, G., Razik, H., and Ondel, O. (2011). Detection of induction motor faults by an improved artificial ant clustering. (IEEE), pp. 3446–3451.
Souibgui, F., BenHmida, F., and Chaari, A. (2011). Particle filter approach to fault detection and isolation in nonlinear systems. (IEEE), pp. 1–6.
Srivastava, N.P., Srivastava, R.K., and Vashishtha, P. (2014). Fault Detection and Isolation (Fdi) Via Neural Networks.
Urmos, A., Farkas, M., Kóczy, L., and Nemcsics, A. (2013). Quantum structure classification by Kohonen Self-Organizing Map and by Fuzzy C-Means algorithm. In Hungarian Consortium, pp. 313–318.
Venkatasubramanian, V., Rengaswamy, R., Kavuri, S.N., and Yin, K. (2003). A review of process fault detection and diagnosis: Part III: Process history based methods. Comput. Chem. Eng. 27, 327–346.
Vento, J., Blesa, J., Puig, V., and Sarrate, R. (2015). Setmembership parity space hybrid system diagnosis. Int. J. Syst. Sci. 46, 790–807.
Villez, K. (2014). Qualitative trend analysis for process monitoring and supervision based on likelihood optimization: state-of-the-art and current limitations. IFAC Proc. Vol. 47, 7140–7145.
Wang, L., and Nee, A.Y.C. (2009). Collaborative Design and Planning for Digital Manufacturing (London: Springer-Verlag).
Xu, L., Chen, L., and Xiong, W. (2015). Parameter estimation and controller design for dynamic systems from the step responses based on the Newton iteration. Nonlinear Dyn. 79, 2155–2163.
Yang, F., Shah, S., and Xiao, D. (2012). Signed directed graph based modeling and its validation from process knowledge and process data. Int. J. Appl. Math. Comput. Sci. 22, 41–53.
Yang, Z., Zhong, J., and Wong, S.F. (2011). Machine learning method with compensation distance technique for gear fault detection. In 2011 9th World Congress on Intelligent Control and Automation, pp. 632–637.
Yin, S., Ding, S.X., Xie, X., and Luo, H. (2014). A Review on Basic Data-Driven Approaches for Industrial Process Monitoring. IEEE Trans. Ind. Electron. 61, 6418–6428.
Yin, S., Ding, S.X., and Zhou, D. (2016). Diagnosis and Prognosis for Complicated Industrial Systems—Part I. IEEE Trans. Ind. Electron. 63, 2501–2505.
Younus, A.M.D., and Yang, B.-S. (2012). Intelligent fault diagnosis of rotating machinery using infrared thermal image. Expert Syst. Appl. 39, 2082–2091.
Zappone, A., Di Renzo, M., and Debbah, M. (2019). Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both? ArXiv.Org.
Zhao, Y., Xiao, F., and Wang, S. (2013). An intelligent chiller fault detection and diagnosis methodology using Bayesian belief network. Energy Build. 57, 278–288.
Zhao, Y., Wen, J., and Wang, S. (2015). Diagnostic Bayesian networks for diagnosing air handling units faults – Part II: Faults in coils and sensors. Appl. Therm. Eng. 90, 145–157.
Zhao, Y., Wen, J., Xiao, F., Yang, X., and Wang, S. (2017). Diagnostic Bayesian networks for diagnosing air handling units faults – part I: Faults in dampers, fans, filters and sensors. Appl. Therm. Eng. 111, 1272–1286.
Zhou, B., and Ye, H. (2016). A study of polynomial fitbased methods for qualitative trend analysis. J. Process Control 37, 21–33.
Zogopoulos-Papaliakos, G., and Kyriakopoulos, K.J. (2019). Parity-Based Diagnosis in UAVs: Detectability and Robustness Analyses. (IEEE), pp. 6496–6502.
Zuo, M.J., Lin, J., and Fan, X. (2005). Feature separation using ICA for a one-dimensional time series and its application in fault detection. J. Sound Vib. 287, 614–624.
Technical Papers