Inverse Fuzzy Fault Models for Fault Isolation and Severity Estimation in Industrial Pneumatic Valves
DOI:
https://doi.org/10.31449/inf.v48i3.5101Abstract
Fault detection is crucial in the chemical industry for identifying process problems, and determining the nature of the fault is essential for scheduling maintenance. This study focuses on the application of inverse fuzzy models to reconstruct faults for the purpose of detection, isolation, and classification. By inverting fuzzy models, the fault signal can be reconstructed, enabling identification of the fault source and its characteristics. To address the issue of undetected small abrupt faults, the wavelet transform is employed. This approach allows for the detection of incipient faults, while the classification is achieved by evaluating the response of the fault reconstruction. Fault isolation is accomplished by comparing the reconstructed faults. However, in the case of the pneumatic valve utilized, four out of the 19 simulated faults demonstrated poor isolation due to the similarity of their reconstructions using inverse fuzzy models. A comparison with similar applications in existing literature is also presented.References
M. Mansouri, M. Nounou, H. Nounou, and N. Karim, "Kernel PCA-based GLRT for nonlinear fault detection of chemical processes,'' Journal of Loss Prevention in the Process Industries, vol. 40, pp. 334-347, 2016.
A. Imanaka, T. Murayama, S. Nishikizawa, and A. Nagaoka, "Local governments' response to accidents in chemical factories in Japan: Focus on petroleum industrial complexes special accident prevention areas,'' International Journal of Disaster Risk Reduction, vol. 74, p. 102880, 2022.
C. Chen and G. Reniers, "Chemical industry in China: The current status, safety problems, and pathways for future sustainable development,'' Safety Science, vol. 128, p. 104741, 2020.
J. N. P. Nogueira, P. A. Melo, and M. B. de Souza Jr., "Faulty scenarios in sour water treatment units: Simulation and AI-based diagnosis,'' Process Safety and Environmental Protection, vol. 165, pp. 716-727, 2022.
S. Bansal and J. T. Selvik, "Investigating the implementation of the safety- diagnosability principle to support defence-in-depth in the nuclear
industry: A Fukushima Daiichi accident case study,'' Engineering Failure
Analysis, vol. 123, p. 105315, 2021.
A. S. Yeardley, J. O. Ejeh, L. Allen, S. F. Brown, and J. Cordiner, "Integrating machine learning techniques into optimal maintenance scheduling,'' Computers & Chemical Engineering, vol. 166, p. 107958, 2022.
R. Doraiswami and L. Cheded, Fault Diagnosis and Detection, ch. Fault
detection and isolation, pp. 1-27. IntechOpen, 2017.
C. D. Bocaniala and J. S. da Costa, "Application of a novel fuzzy classifier
to fault detection and isolation of the DAMADICS benchmark problem,'' Control Engineering Practice, vol. 14, pp. 653--669, 2006.
N. Hadroug, A. Hafaifa, and A. Daoudi, "Valve actuator fault classification
based on fuzzy system using the DAMADICS model,'' in International
Conference on Applied Automation and Industrial Diagnostics (U.~of~Djelfa,
ed.), (Algeria), p. 0185, March 2015.
S. Yin, S. X. Ding, A. Haghani, H. Hao, and P. Zhang, "A comparison study of
basic data-driven fault diagnosis and process monitoring methods on the
benchmark Tennessee Eastman process,'' Journal of Process Control,
vol. 22, pp. 1567-1581, 2012.
A. Kowsalya and B. Kannapiran, "Principal component analysis based approach for fault diagnosis in pneumatic valve using DAMADICS benchmark simulator,'' International Journal of Research in Engineering and Technology,
vol. 3(7), pp. 702--707, 2014.
F. J. Uppal, R. J. Pattona, and M. Witczak, "A neuro-fuzzy multiple-model
observer approach to robust fault diagnosis based on the DAMADICS benchmark problem,''Control Engineering Practice, vol. 14, 2006.
S. Xiong, L. Zhou, Y. Dai, and X. Ji, "Attention-based LSTM fully convolutional network for chemical process fault diagnosis,'' Chinese Journal of Chemical Engineering, vol. In press, 2022.
R. Qin and J. Zhao, "Adaptive multiscale convolutional neural network model
for chemical process fault diagnosis,'' Chinese Journal of Chemical Engineering, vol.~50, pp.~398--411, 2022.
G. C. Silva, E. E. O. Carvalho, and W. M. Caminhas, "An artificial immune
systems approach to case-based reasoning applied to fault detection and
diagnosis,'' Expert Systems with Applications, vol. 140, p. 112906, 2020.
Z. Xie, J. Chen, Y. Feng, and S. He, "Semi-supervised multi-scale attention-aware graph convolution network for intelligent fault diagnosis of machine under extremely-limited labeled samples,'' Journal of Manufacturing Systems, vol. 64, pp. 561-577, 2022.
G. Hong and D. Suh, "Mel spectrogram-based advanced deep temporal clustering model with unsupervised data for fault diagnosis,'' Expert Systems With Applications, vol. 217, p. 119551, 2023.
Y. Xu, X. Zeng, S. Bernard, and Z. He, "Data-driven prediction of neutralizer
pH and valve position towards precise control of chemical dosage in a
waste water treatment plant,'' Journal of Cleaner Production}, vol. 348,
p. 131360, 2022.
M. Bartys, R. Patton, M. Syfert, S. de las Heras, and J. Quevedo, "Introduction to the DAMADICS actuator fdi benchmark study,'' Control Engineering Practice, vol. 14(6), pp. 577-596, 2006.
P. Subbaraj and B. Kannapiran, "Fault detection and diagnosis of pneumatic
valve using adaptive neuro-fuzzy inference system approach,'' Applied Soft Computing, vol. 19, pp. 362-371, 2014.
D. Saravanakumar, B. Mohan, and T. Muthuramalingam, "A review on recent
research trends in servo pneumatic positioning systems,'' Precision Engineering, vol. 49, pp. 481-492, 2017.
S. H. Wang, T. W. Ni, and Z. G. Yang, "Failure analysis on abnormal blockage
of electro-hydraulic servo valve in digital electric hydraulic control system
of 125 MW thermal power plant,'' Engineering Failure Analysis, vol.123, p. 105294, 2021.
K. Zhang, J. Yao, and T. Jiang, "Degradation assessment and life prediction of
electro-hydraulic servo valve under erosion wear,'' Engineering Failure Analysis, vol. 36, pp. 284-300, 2014.
J. Shi, J. Yi, Y. Ren, Y. Li, Q. Zhong, H. Tang, and L. Chen, "Fault diagnosis in a hydraulic directional valve using a two-stage multi-sensor information
fusion,'' Measurement, vol. 179, p. 109460, 2021.
F. Khan, M. T. Amin, V. Cozzani, and G. Reniers, Methods in Chemical Process Safety, ch. Domino effect: Its prediction and prevention - An overview, pp. 1-35. Elsevier, 2021.
U. Pal, G. Mukhopadhyay, and S. Bhattacharya, "Failure analysis of spring of
hydraulic operated valve,'' Engineering Failure Analysis, vol. 95, pp. 191-198, 2019.
G. M. de Almeida and S. W. Park, "Fault detection and diagnosis in the
DAMADICS benchmark actuator system - a hidden Markov model approach,'' IFAC Proceedings Volumes, vol. 41(2), pp. 12419-12424, 2008. 17th IFAC World Congress.
R. B. di Capaci and C. Scali, "Review and comparison of techniques of analysis
of valve stiction: From modeling to smart diagnosis,'' Chemical Engineering Research and Design, vol. 130, pp. 230-265, 2018.
V. Puig, A. Stancu, and J. Quevedo, "Robust fault isolation using non-linear
interval observers: The DAMADICS benchmark case study,'' in 16th Triennial World Congress (Elsevier, ed.), (Prague, Czech Republic), pp. 293-298, 2005.
R.~Babuska, Fuzzy Modeling for Control. Aachen, Germany: International Series in Intelligent Technologies, 1998.
A. Lemos, W. Caminhas, and F. Gomide, "Adaptive fault detection and diagnosis using an evolving fuzzy classifier,'' Information Science, vol. 220,
pp. 64-85, 2013.
Y. Kourd, D. Lefebvre, and N. Guersi, "FDI with neural network models of
faulty behaviours and fault probability evaluation: Application to DAMADICS,'' in Supervision and Safety of Technical Processes (I. F. of Automatic Control, ed.), (Mexico City, Mexico), pp. 744-749, August 2012.
J. Sarkar, Z. H. Prottoy, M. T. Bari, and M. A. A. Faruque, "Comparison of
ANFIS and ANN modeling for predicting the water absorption behavior of
polyurethane treated polyester fabric,'' Heliyon, vol. 7(9), p. e08000, 2021.
V. Gomathi, V. Elakkiya, R. Valarmathi, and K. Ramkumar, "Actuator fault
detection using adaptive neuro fuzzy approach for DAMADICS benchmark,''
Research Journal of Pharmaceutical, Biological and Chemical Sciences, vol. 7(1), pp. 628-635, 2016.
A. Andrade, K. Lopes, B. Lima, and A. Maitelli, "Development of a methodology
using artificial neural network in the detection and diagnosis of faults for
pneumatic control valves,'' Sensors, vol. 21, p. 853, 2021.
A. R. C. Oliveira and J. M. G. S. da Costa, "Hierarchic fault diagnosis by pattern - recognition approaches applied to DAMADICS benchmark,'' in 18th World Congress The International Federation of Automatic Control (I. F. of Automatic Control, ed.), (Milano, Italy), pp. 7737-7742, August 2011.
A. Katunin, M. Amarowicz, and P. Chrzanowski, "Faults diagnosis using
self-organizing maps: A case study on the DAMADICS benchmark problem,'' in
Federated Conference on Computer Science and Information Systems,
pp. 1673-1681, 2015.
T. Chopra and J. Vajpai, "Classification of faults in DAMADICS benchmark
process control system using self organizing maps,'' International Journal of Soft Computing and Engineering}, vol. 1(3), pp. 85-90, 2011.
V. Palade, C. D. Bocaniala, and L. Jain, Computational Intelligence in Fault Diagnosis. Advanced Information and Knowledge Processing, United States of
America: Springer-Verlag London, 2006.
C. D. Bocaniala and V. Palade, Computational intelligence methodologies in
fault diagnosis: Review and state of the art, Computational intelligence in
fault diagnosis}, ch. Computational intelligence methodologies in fault
diagnosis: Review and state of the art, pp. 1-36. United States of America: Springer-Verlag London, 2006.
P. Supavatanakul, J. Lunze, V. Puig, and J. Quevedo, "Diagnosis of timed automata: Theory and application to the DAMADICS actuator benchmark
problem,'' Control Engineering Practice , vol. 14, pp. 609-619, 2006.
C. Gomes-Bezerra, B. S. Jales-Costa, L. A. Guedes, and P. P. Angelov, "An
evolving approach to unsupervised and real-time fault detection in industrial
processes,'' Expert Systems With Applications, vol. 63, pp. 134-144, 2016.
X. Li, X. Yang, Y. Yang, I. Bennett, and D. Mba, "A novel diagnostic and
prognostic framework for incipient fault detection and remaining service life
prediction with application to industrial rotating machines,'' Applied Soft Computing, vol. 82, p. 105564, 2019.
Y. Langeron, A. Grall, and A. Barros, "Actuator lifetime management in industrial automation,'' IFAC Proceedings Volumes, vol. 45(20), pp. 642-647, 2012.
R. Kosturkov, V. Nachev, and T. Titova, "Diagnosis of pneumatic systems on
basis of time series and generalized feature for comparison with standards
for normal working condition,'' TEM Journal, vol. 10(1), pp. 183-191, 2021.
J. Yang and C. Delpha, "An incipient fault diagnosis methodology using local
Mahalanobis distance: Fault isolation and fault severity estimation,'' Signal Processing, vol. 200, p. 108657, 2022.
M. A. Márquez-Vera, L. E. Ramos-Velasco, O. López-Ortega, N. S. Zúñiga-Peña, J. C. Ramos-Fernáandez, and R. M. Ortega-Mendoza, "Inverse fuzzy fault model for fault detection and isolation with least angle regression for variable selection,'' Computers & Industrial Engineering, vol. 159, p. 107499, 2021.
J. Vosloo, K. R. Uren, G. van Schoor, L. Auret, and H. Marais, "Exergy-based
fault detection on the Tennessee Eastman process,'' IFAC-PapersOnLine,
vol. 53(2), pp. 13713--13720, 2020.
L. Zhang and K. Li, "Forward and backward least angle regression for nonlinear system identification,'' Automatica , vol. 53, pp. 94-102, 2015.
R. Fazai, M. Mansouri, K. Abodayeh, H. Nounou, and M. Nounou, "Online reduced kernel PLS combined with GLRT for fault detection in chemical systems,'' Process Safety and Environmental Protection, vol. 128, pp. 228-243, 2019.
F. Cannarile, M. Compare, P. Baraldi, G. Diodati, V. Quaranta, and E. Zio,
"Elastic net multinomial logistic regression for fault diagnostics of on-board aeronautical systems,'' Aerospace Science and Technology, vol. 94, p. 105392, 2019.
H. Lee, C. Kim, S. Lim, and J. M. Lee, "Data-driven fault diagnosis for chemical processes using transfer entropy and graphical LASSO,'' Computers & Chemical Engineering, vol. 142, p. 107064, 2020.
T. Ross, Fuzzy Logic with Engineering Applications. West Sussex: John Wiley & Sons, Ltd., 2008.
M. A. Márquez-Vera, J. C. Ramos-Fernández, L. F. Cerecero-Natale, F. Lafont, J. F. Balmat, and J. I. Esparza-Villanueva, "Temperature control in a MISO greenhouse by inverting its fuzzy model,'' Computers and Electronics in Agriculture, vol. 124, pp. 168-174, 2016.
M. Bartys and M. Syfert, Using Damadics Actuator Benchmark Library. Warsaw University if Technology, 00-661 Warsaw, Poland, 1.22 ed., April 2002.Simulink Library Help File.
A. R. Várkonyi-Kóczy, A. Álmos, and T. Kovácsházy, "Genetic algorithms in fuzzy model inversion,'' in International Fuzzy Systems Conference Proceedings (IEEE, ed.), (Seul, Korea), pp. 1421-1426, August 1999.
X. M. Zhang and Q. L. Han, "Output feedback stabilization of networked control systems with a logic zero-order-hold,'' Information Sciences, vol. 381,
pp. 78-91, 2020.
H. Dimassi, "A novel fault reconstruction and estimation approach for a class
of systems subject to actuator and sensor faults under relaxed assumptions,''
ISA Transactions , vol. 111, pp. 192-210, 2021.
H. Kazemi and A.~Yazdizadeh, "Fault reconstruction in a class of nonlinear
systems using inversion-based filter,'' Nonlinear Dynamics, vol. 85, pp. 1805-1814, 2016.
M. Shakarami and K. Esfandiari, "Rapid fault reconstruction using a bank of
sliding mode observers,'' Journal of the Franklin Institute, p. Article in Press, 2022.
U. Libal and Z. Hasiewicz, "Wavelet based rule for fault detection,'' IFAC PapersOnLine, vol. 51(24), pp. 255-262, 2018.
M. Jalayer, C. Orsenigo, and C. Vercellis, "Fault detection and diagnosis for
rotating machinery: A model based on convolutional LSTM, fast Fourier and
continuous wavelet transforms,'' Computers in Industry, vol. 125, p. 103378, 2021.
Z. Feng and X. Chen, "Adaptive iterative generalized demodulation for
nonstationary complex signal analysis: Principle and application in rotating
machinery fault diagnosis," Mechanical Systems and Signal Processing,
vol. 110, pp. 1-27, 2018.
V. M. Pukhova, T. V. Kustov, and G. Ferrini, "Time-frequency analysis of non-stationary signals,'' in Conference of Russian Young Researchers in Electrical and Electronic Engineering (IEEE, ed.), (Moscow and St. Petersburg, Russia), pp. 1141-1145, January 2018.
C. Li, Y. Huang, and L. Zhu, "Color texture image retrieval based on Gaussian
copula models of Gabor wavelets,'' Pattern Recognition, vol. 64, pp. 118-129, 2017.
K. Vernekar, H. Kumar, and K. V. Gangadharan, "Gear fault detection using
vibration analysis and continuous wavelet transform,'' Procedia Materials Science, vol. 5, pp. 1846-1852, 2014.
K. Yan and X. Zhou, "Chiller faults detection and diagnosis with sensor network and adaptive 1D CNN,'' Digital Communications and Networks, vol. 8, pp. 531-539, 2022.
W. M. Salilew, Z. A. A. Karim, and T. A. Lemma, "Investigation of fault detection and isolation accuracy of different machine learning techniques with different data processing methods for gas turbine,'' Alexandria Engineering Journal, vol. 61(12), pp. 12635-12651, 2022.
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