Abstract
In this paper, a new algorithm is proposed for fault detection and identification (FDI) in a class of nonlinear systems by combining the extended Kalman filter (EKF) and neuro-fuzzy networks (NFNs). There is an abundance of the literature on fault diagnosis ranging from model-based methods to data-driven approaches that have advantages and drawbacks. One may employ the advantages of different approaches to develop a high-efficient method for fault diagnosis. Initially, an EKF is designed to estimate the system output and to generate accurate residuals by a mathematical model of the process. Then, an NFN is designed for making decision using the mean value of the residuals. The network assigns a locally linear model to each faulty condition of the system. The validity of the models is determined based on the fuzzy rules. Combining the introduced EKF and the introduced NFN causes the proposed method to be independent of pre-designing a bank of observers in the model-based methods. Moreover, there is no need for extracting the features from the signals without any physical insight as well as computational complexity in the data-driven techniques. The effectiveness of the proposed FDI scheme is verified by applying it to a chemical plant as the case study, namely, continuous stirred tank reactor process. Simulation results show that the proposed methodology is very effective to detect and identify the faults of the system in different faulty modes.
Similar content being viewed by others
References
Harabi RE, Bouamama BO, Gayed MKB, Abdelkrim MN (2010) Pseudo bond graph for fault detection and isolation of an industrial chemical reactor part II: FDI system design. In: 9th international conference on bond graph modeling and simulation (CBGM’10), Orlando, Florida, pp 190–197
Harabi RE, Smaili R, Abdelkrim MN (2015) Fault diagnosis algorithms by combining structural graphs and PCA approaches for chemical processes. In: Chaos modeling and control systems design. Studies in computational intelligence, vol 581, Springer, Switzerland, pp 393–416
Wei X, Verhaegen M, Engelen T (2010) Sensor fault detection and isolation for wind turbines based on subspace identification and Kalman filter techniques. Int J Adapt Control Signal Process 24:687–707
Kodali A, Zhang Y, Sankavaram C et al (2013) Fault diagnosis in the automotive electric power generation and storage system (EPGS). IEEE-ASME Trans Mechatron 18:1809–1818
Isermann R (1997) Trends in the application of model-based fault detection and diagnosis of technical processes. Control Eng Pract 5:709–719
Willsky AS (1976) A survey of design methods for failure detection in dynamic systems. Automatica 12:601–611
Meinguet F, Sandulescu P, Aslan B et al (2012) A signal-based technique for fault detection and isolation of inverter faults in multi-phase drives. In: IEEE international conference power electronics, drives and energy systems (PEDES’12), Bengaluru, India, pp 1–6
Ga’lvez-Carrillo M, Kinnaert M (2010) Sensor fault detection and isolation in three-phase systems using a signal-based approach. IET Control Theory Appl 4:1838–1848
Giantomassi A, Ferracuti F, Iarlori S et al (2015) Signal based fault detection and diagnosis for rotating electrical machines: issues and solutions. In: Complex system modeling and control through intelligent soft computations. Studies in fuzziness and soft computing, vol 319. Springer, Switzerland, pp 275–309
Venkatasubramanian V, Rengaswamy R, Yin K, Kavuri SN (2003) A review of process fault detection and diagnosis part I: quantitative model-based methods. Comput Chem Eng 27:293–311
Venkatasubramanian V, Rengaswamy R, Yin K, Kavuri SN (2003) A review of process fault detection and diagnosis part II: qualitative models and search strategies. Comput Chem Eng 27:313–326
Venkatasubramanian V, Rengaswamy R, Yin K, Kavuri SN (2003) A review of process fault detection and diagnosis part III: process history based methods. Comput Chem Eng 27:327–346
Hwang I, Kim S, Kim Y, Seah CE (2010) A survey of fault detection, isolation, and reconfiguration methods. IEEE Trans Control Syst Technol 18:636–653
Odendaal HM, Jones T (2014) Actuator fault detection and isolation: an optimized parity space approach. Control Eng Pract 26:222–232
Puig V, Blesa J (2013) Limnimeter and rain gauge FDI in sewer networks using an interval parity equation based detection approach and an enhanced isolation scheme. Control Eng Pract 21:146–170
Benkouider AM, Buvat JC, Cosmao JM, Saboni A (2009) Fault detection in semi-batch reactor using the EKF and statistical method. J Loss Prev Process Ind 22:153–161
Villez K, Srinivasan B, Rengaswamy R et al (2011) Kalman-based strategies for Fault Detection and Identification (FDI): extensions and critical evaluation for a buffer tank system. Comput Chem Eng 35:806–816
Hsoumi A, Harabi RE, Ali SBH, Abdelkrim MN (2009) Diagnosis of a continuous stirred tank reactor using Kalman filter. In: International conference on computational intelligence, modeling and simulation (CSSim’09), Brno, pp 153–158
Saravanakumar R, Manimozhi M, Kothari DP, Tejenosh M (2014) Simulation of sensor fault diagnosis for wind turbine generators DFIG and PMSM using Kalman filter. Energy Procedia 54:494–505
Foo GHB, Zhang X, Vilathgamuwa DM (2013) A sensor fault detection and isolation method in interior permanent-magnet synchronous motor drives based on an extended Kalman filter. IEEE Trans Ind Electron 60:3485–3495
Yin S, Wang G, Karimi HR (2014) Data-driven design of robust fault detection system for wind turbines. Mechatron 24:298–306
Dai X, Gao Z (2013) From model, signal to knowledge: a data driven perspective of fault detection and diagnosis. IEEE Trans Ind Inform 9:2226–2238
Angeli C, Chatzinikolaou A (2004) On-line fault detection techniques for technical systems: a survey. Int J Comput Sci Appl 1:12–30
White CJ, Lakany H (2008) A fuzzy inference system for fault detection and isolation: application to a fluid system. Expert Syst Appl 35:1021–1033
Talebi HA, Khorasani K (2013) A neural network-based multiplicative actuator fault detection and isolation of nonlinear systems. IEEE Trans Control Syst Technol 21:842–851
Mendonca LF, Sousa JMC, Sa da Costa JMG (2009) An architecture for fault detection and isolation based on fuzzy methods. Expert Syst Appl 36:1092–1104
Bathaie SST, Vanini ZNS, Khorasani K (2014) Dynamic neural network-based fault diagnosis of gas turbine engines. Neurocomputing 125:153–165
Cho HC, Knowles J, Fadali MS, Lee KS (2010) Fault detection and isolation of induction motors using recurrent neural networks and dynamic bayesian modeling. IEEE Trans Control Syst Technol 18:430–437
Simani S, Farsoni S, Castaldi P (2015) Wind turbine simulator fault diagnosis via fuzzy modeling and identification techniques. Sustain Energy Grid Netw 1(45):52
Garcia RF (2007) On fault isolation by neural-networks-based parameter estimation techniques. Expert Syst 24:47–63
Benkouider AM, Kessas R, Yahiaoui A et al (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
Korbicz J, Kowal M (2007) Neuro-fuzzy networks and their application to fault detection of dynamical systems. Eng Appl Artif Intell 20:609–617
Mok HT, Chan CW (2008) Online fault detection and isolation of nonlinear systems based on neuro-fuzzy networks. Eng Appl Artif Intell 21:171–181
Wu JD, Kuo JM (2010) Fault conditions classification of automotive generator using an adaptive neuro-fuzzy inference system. Expert Syst Appl 37:7901–7907
Viharos ZJ, Kis KB (2015) Survey on Neuro-Fuzzy systems and their applications in technical diagnostics and measurement. Measurement 67:126–136
Khireddine MS, Chaf K, Slimane N, Boutarfa A (2014) Fault diagnosis in robotic manipulators using Artificial Neural Networks and Fuzzy logic. World congress on computer applications and information systems (WCCAIS’14), Hammamet, pp 1–6
Razavi-Far R, Davilu H, Palade V, Lucas C (2009) Model-based fault detection and isolation of a steam generator using neuro-fuzzy networks. Neurocomputing 72:2939–2951
Jang J-SR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybernet 23:665–685
Banu US, Uma G (2011) ANFIS based sensor fault detection for continuous stirred tank reactor. Appl Soft Comput 11:2618–2624
Subbaraj P, Kannapiran B (2014) Fault detection and diagnosis of pneumatic valve using adaptive neuro-fuzzy inference system approach. Appl Soft Comput 19:362–371
Vanini ZNS, Khorasani K, Meskin N (2014) Fault detection and isolation of a dual spool gas turbine engine using dynamic neural networks and multiple model approach. Inf Sci 259:234–251
Tong C, El-Farra NH, Palazoglu A (2014) Fault detection and isolation in hybrid process systems using a combined data-driven and observer-design methodology. In: American control conference (ACC), Portland, Oregon, pp 1969–1974
Jazwinski AH (2007) Stochastic processes and filtering theory. Courier Corporation, Mineola
Singh A, Izadian A, Anwar S (2013) Fault diagnosis of Li-Ion batteries using multiple-model adaptive estimation. In: Industrial Electronics Society, IECON’13. 39th Annual Conference of the IEEE, Vienna, pp 3524–3529
Manimozhi M, Kumar RS (2014) Sensor and actuator fault detection and isolation in nonlinear system using multi-model adaptive linear Kalman filter. Res J Appl Sci Eng Technol 7:3491–3498
Luyben WL (2007) Chemical reactor design and control. Wiley, Hoboken
Zhou Y, Hahn J, Mannan MS (2003) Fault detection and classification in chemical processes based on neural networks with feature extraction. ISA Trans 42:651–664
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Gholizadeh, M., Yazdizadeh, A. & Mohammad-Bagherpour, H. Fault detection and identification using combination of EKF and neuro-fuzzy network applied to a chemical process (CSTR). Pattern Anal Applic 22, 359–373 (2019). https://doi.org/10.1007/s10044-017-0634-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10044-017-0634-7