A review of model based and data driven methods targeting hardware systems diagnostics
1
Cranfield University
Submission date: 2018-08-29
Acceptance date: 2018-11-15
Online publication date: 2018-11-22
Publication date: 2018-11-22
Corresponding author
Diagnostyka 2019;20(1):3-21
KEYWORDS
diagnosticssystem diagnosticscondition based maintenancemodel based diagnosticsdata driven diagnostics
TOPICS
ABSTRACT
System health diagnosis serves as an underpinning enabler for enhanced safety and optimized maintenance tasks in complex assets. In the past four decades, a wide-range of diagnostic methods have been proposed, focusing either on system or component level. Currently, one of the most quickly emerging concepts within the diagnostic community is system level diagnostics. This approach targets in accurately detecting faults and suggesting to the maintainers a component to be replaced in order to restore the system to a healthy state. System level diagnostics is of great value to complex systems whose downtime due to faults is expensive. This paper aims to provide a comprehensive review of the most recent diagnostics approaches applied to hardware systems. The main objective of this paper is to introduce the concept of system level diagnostics and review and evaluate the collated approaches. In order to achieve this, a comprehensive review of the most recent diagnostic methods implemented for hardware systems or components is conducted, highlighting merits and shortfalls.
REFERENCES (114)
1.
Jennions IK. editor. Integrated Vehicle Health Management - Perspectives on an Emerging Field. SAE International; 2011.
3.
Evan C P. Industrial Internet: Pushing the boundries of Minds and Machines 2012. Available from: http://www.ge.com/docs/chapter....
4.
Esperon-Miguez M, John P, Jennions IK. A review of Integrated Vehicle Health Management tools for legacy platforms: Challenges and opportunities. Prog Aerosp Sci. 2013;56:19–34. http://dx.doi.org/10.1016/j.pa....
5.
Pelham JG, Fan I-S, Jennions I, McFeat J. Application of an AIS to the problem of through life health management of remotely piloted aircraft. AIAA Infotech Aerosp. 2015;(January):5–9.
6.
Piel Eric, Gonzalez-Sanchez A, Gross H, Gemund AJC Van. Online fault localization and health monitoring for software systems. in: situation awareness with systems of systems. New York: Springer Science+Business Media; 2013. 229–45. http://link.springer.com/10.10....
7.
Ogaji SOT, Sampath S, Singh R, Probert SD. Parameter selection for diagnosing a gas-turbine’s performance-deterioration. Appl Energy. 2002;73(1): 25–46.
8.
Staszewski WJ, Boller WJ, Tomlinson GR. Health monitoring of aerospace structures. 2004. 266. http://dx.doi.org/10.1002/0470....
9.
Duffuaa SO, Ben-Daya M. Handbook of Maintenance Management and Engineering. Handbook of Maintenance Management and Engineering. 2009. 223-235: http://www.scopus.com/inward/r....
10.
Mehdi G, Naderi D, Ceschini G, Roshchin M. Model-based reasoning approach for automated failure analysis: An Industrial Gas Turbine Application. 2011;1–10.
11.
Saha B, Vachtsevanos G. A Model-based reasoning approach to system fault diagnosis. Proc 10th WSEAS Int Conf Syst. 2006;2006:64–71. http://dl.acm.org/citation.cfm....
12.
Bartlett LM, Hurdle EE, Kelly EM. Integrated system fault diagnostics utilising digraph and fault tree-based approaches. Reliab Eng Syst Saf. 2009;94(6):1107–15.
13.
Papadopoulos Y. Model-based system monitoring and diagnosis of failures using statecharts and fault trees. Reliab Eng Syst Saf. 2003;81(3):325–41.
14.
Singh S, Holland SW, Bandyopadhyay P. Trends in the development of system-level fault dependency matrices. IEEE Aerosp Conf Proc. 2010;.
15.
Durkin J. Expert systems: Design and development. New York: Macmillan Publishing Company; 1994.
18.
Shi N-Z, Tao J. Statistical hypothesis testing. Toh Tuck Link, Singapore: World Scientific Publishing Co. Pte. Ltd.; 2008.
20.
Mohanty SR, Pradhan AK, Routray A. A cumulative sum-based fault detector for power system relaying application. IEEE Trans Power Deliv. 2008;23(1):79–86.
21.
Basseville M, Nikiforov I. Fault isolation for diagnosis: nuisance rejection and multiple hypotheses testing. Annu Rev Control. 2002;25(1):83–94. http://linkinghub.elsevier.com....
22.
Patton, R. J, Chen J. Review of parity space approaches to fault diagnosis for aerospace systems. J Guid Control Dyn. 1994;17(2).
23.
Höfling T, Isermann R. Fault detection based on adaptive parity equations and single-parameter tracking. Control Eng Pract. 1996;4(10):1361–9.
24.
Patton RJ, Chen J. Robust fault detection using eigenstructure assignement: a tutortial considerations and some new results. In: Proceedings of the 30th conference on decision and control. Brighton, England; 1991.
25.
White J, Speyer J. Detection filter design: Spectral theory and algorithms. IEEE Trans Automat Contr 1987;32(7):593–603. http://ieeexplore.ieee.org/doc....
26.
Frank PM. Analytical and Qualitative Model-based Fault Diagnosis – A Survey and Some New Results. Vol. 2, European Journal of Control. 1996. p. 6–28.
27.
Hwang I, Kim S, Kim Y, Seah CE. A survey of fault detection, isolation, and reconfiguration methods. IEEE Trans Control Syst Technol. 2010;18(3):636–53.
28.
Patton RJ, Frank PM, Clarke RN. Fault diagnosis in dynamic systems: theory and application. Upper Saddle River, NJ, USA: Prentice-Hall, Inc.; 1989.
29.
Frank PM. Fault diagnosis in dynamic systems using analytical knowledge-based redundancy – A survey and some new results. Automatica. 1990;26(3):459–74.
30.
Isermann R. Fault diagnosis of machines via parameter estimation and knowledge processing-Tutorial paper. Automatica. 1993;29(4):815–35.
31.
Isermann R. Process Fault Detection Based on Modeling and Estimation Methods-A Survey. 1984;4(4).
32.
Volponi AJ. Foundations of Gas Path Analysis I. In: Gas turbine condition monitoring and fault diagnosis - Lecture series 2003-01. von Karman Institute for Fluid Dynamics; 2003.
33.
Volponi AJ. Foundation of Gas Path Analysis II. In: Gas turbine condition monitoring and fault diagnosis - Lecture series 2003-01. von Karman Institute for Fluid Dynamics;.
34.
Doel DL. An Assessment of Weighted-Least-Squares Based Gas Path. In: International gas turbinbe and aeroengine congress and exposition. Cincinati, Ohio: ASME; 1993.
35.
Moseler O, Isermann R. Application of model-based fault detection to a brushless DC motor. IEEE Trans Ind Electron. 2000;47(5):1015–20.
36.
Urban LA. Gas path analysis applied to turbine engine condition monitoring. J Aircr. 1973;10(7):400–6.
37.
Doel DL. Development of baselines, influence coefficients and statistical inputs for gas path analysis. In: Gas turbine condition monitoring and fault diagnosis - Lecture series 2003-01. von Karman Institute for Fluid Dynamics; 2003.
38.
Stamatis AG. Optimum Use of Existing Sensor Information for Gas Turbine Diagnostics. In: Proceedings of the ASME Turbo Expo 2008 http://proceedings.asmedigital....
39.
Volponi AJ. Gas Turbine Engine Health Management: Past, Present, and Future Trends. J Eng Gas Turbines Power. 2014;136(5):051201. http://gasturbinespower.asmedi....
40.
A.Stamatis, K. Mathioudakis, G. Berrios KP. Jet engine fault detection with discrete operating points gas path analysis. J Propuls Power. 1991;7(6).
41.
Doel DL. TEMPER-A Gas-Path Analysis Tool for Commercial Jet Engines. J Eng Gas Turbines Power 1994;116(1):82. http://link.aip.org/link/JETPE....
42.
Davis R. Diagnostic reasoning based on structure and behavior. Artif Intell. 1984;24(1–3):347–410. http://linkinghub.elsevier.com....
43.
Kolcio K, Hanson M, Fesq L. Validation of Autonomous Fault Diagnostic Software. IEEE. 1998;.
44.
Fesq L. Marple: An autonomous diagnostician for isolationg system hardware failures. UCLA; 1993.
45.
Kolcio K, Breger L, Zetocha P. Model-based fault management for spacecraft autonomy. IEEE Aerosp Conf Proc. 2014;.
46.
Rossi C. Vehicle Health Monitoring Using Stochastic Constraint Suspension. In: AIAA Guidance, Nagivation, and Control Conference. Minneapolis, Minnesota; 2012.
47.
Rossi C, Benson D, Sargent R, Breger L. Model-Based Design for Vehicle Health Monitoring. Infotech@aerosp 2012. 2012;(June):1–23. http://arc.aiaa.org/doi/abs/10....
48.
Kolcio K, Fesq L. Model-based off-nominal state isolation and detection system for autonomous fault management. IEEE Aerosp Conf Proc. 2016;2016–June.
49.
De Kleer J, Kurien J. Fundamentals of model-based diagnosis. IFAC Proc. 2003;6670(June 2003):25. http://books.google.com/books?....
50.
Stern R, Kalech M, Rogov S, Feldman A. How many diagnoses do we need ? Artif Intell. 2017;248:26–45. http://dx.doi.org/10.1016/j.ar....
51.
Feldman A, Provan G, Van Gemund A. Approximate model-based diagnosis using greedy stochastic search. J Artif Intell Res. 2010;38:371–413.
52.
Keren B, Kalech M, Rokach L. Model-based diagnosis with multi-label classification. 22nd Int Work Princ Diagnosis, DX. 2011;.
53.
Kolcio K, Fesq L, Mackey R. Model-based approach to rover health assessment for increased productivity. 2017;.
54.
Mosterman PJ, Kapadia R, Biswas G. Using bond graphs for diagnosis of dynamic physical systems. 1995.
55.
Daigle M, Field M, Foygel M. Model-based Diagnostics for Propellant Loading Systems. 2011;.
56.
Mosterman PJ, Biswas G. Diagnosis of continuous valued systems in transient operating regions. IEEE Trans Syst Man, Cybern Part ASystems Humans. 1999;29(6):554–65.
57.
Narasimhan S, Biswas G. Model-Based Diagnosis of Hybrid Systems. Syst Man Cybern Part A Syst Humans, IEEE Trans. 2007;37(3):348–61. http://ieeexplore.ieee.org/sta....
58.
Chen B, Li J, Wei G, Ma B. A novel localized and second order feature coding network for image recognition. Pattern Recognit. 2018;76:339–48.
59.
Wang S, Li G, Bao Y. A novel improved fuzzy support vector machine based stock price trend forecast model. 2018. http://arxiv.org/abs/1801.0068....
60.
Vapnik V. The nature of statistical learning theory. Springer; 1995. http://www.ncbi.nlm.nih.gov/pu....
61.
Simon Haykin. Neural networks: a comprehensive foundation. The Knowledge Engineering Review. 1999; 13:409–12.
64.
Muhammad MB, Sarwar U, Tahan MR, Karim ZAA. Fault diagnostic model for rotating machinery based on principal component analysis and neural network. ARPN J Eng Appl Sci. 2016;11(24):14327–31.
65.
Kanelopoulos K, Stamatis A, Mathioudakis K. Incorporating Neural networks into gas turbine performance diagnostics. ASME 1997 Int Gas Turbine Aeroengine Congr Exhib. 1997; V004T15A011-V004T15A011.
66.
Sina Tayarani-Bathaie S, Sadough Vanini ZN, Khorasani K. Dynamic neural network-based fault diagnosis of gas turbine engines. Neurocomputing 2014;125:153–65. http://dx.doi.org/10.1016/j.ne....
67.
Pascoal RM, Vianna WOL, Gomes JPP, Galvão RKH. Estimation of APU failure parameters employing linear regression and neural networks. Phm. 2013;1–7.
68.
Volponi AJ, DePold H, Ganguli R, Daguang C. The use of Kalman filter and neural network methodologies in gas turbine performance diagnostics: A Comparative Study. J Eng Gas Turbines Power. 2003;125(4):917–24. http://dx.doi.org/10.1115/1.14....
69.
Zedda M, Singh R. Fault diagnosis of a turbofan engine using neural networks - A quantitative approach. 34th AIAA/ASME/SAE/ASEE Jt Propuls Conf Exhib. 1998;2. http://arc.aiaa.org/doi/10.251....
71.
Uluyola O, Buczaka A, Nwadiogbub E. Neural networks based sensor validation and recovery methodology for advanced aircraft engines. In: Component and System Diagnostics, Prognosis, and Health Management. 2001: 102–9.
72.
Guo T-H, Saus J, Lin C-F, Ge J-H. Sensor validation for turbofan engines using an autoassociative neural network. Guid Navig Control Conf. 1996; http://arc.aiaa.org/doi/10.251....
73.
Zedda M, Singh R. Neural-network-based sensor validation for gas turbine test bed analysis. 2001;215:47–56.
74.
Sadough Vanini ZN, Meskin N, Khorasani K. Multiple-Model Sensor and Components Fault Diagnosis in Gas Turbine Engines Using Autoassociative Neural Networks. J Eng Gas Turbines Power. 2014;136(9):091603. http://gasturbinespower.asmedi....
75.
Vapnik V. Support Vector Networks. Mach Learn 1995;20(3):273-297. http://link.springer.com/10.10....
76.
Zhao Q, Wang B, Zhou G, Zhang W, Guan X, Feng W, et al. An Improved Fault Diagnosis Approach Based on Support Vector Machine. In: 2016 IEEE International Conference on Prognostics and Health Management (ICPHM). 2016.
77.
Vieira FM, Bizarria CDO. Health monitoring using support vector classification on an auxiliary power unit. In: IEEE Aerospace Conference Proceedings. 2009.
78.
Zhou D, Zhang H, Weng S. A New Gas Path Fault Diagnostic Method of Gas Turbine Based on Support Vector Machine. J Eng Gas Turbines Power 2015;137(10):6 http://gasturbinespower.asmedi....
79.
Hou C, Wang Q. Diagnosis of Aircraft Engine Performance Deterioration Based on Support Vector Machines. 2014;1–5.
80.
Nieto Gonzalez JP. Vehicle fault detection and diagnosis combining an AANN and multiclass SVM. Int J Interact Des Manuf. 2017;1–7.
81.
Frank PM, Köppen-Seliger B. Fuzzy logic and neural network applications to fault diagnosis. Int J Approx Reason. 1997;16(1):67–88.
82.
Ross TJ. Fuzzy logic with engineering applications. West Sussex, England: John Wiley & Sons, Ltd; 2004.
83.
Allen WH, Rubaai A, Chawla R. Fuzzy neural network-based health monitoring for HVAC system variable-air-volume unit. IEEE Trans Ind Appl. 2016;52(3):2513–24.
84.
Hang J, Zhang J, Cheng M. Application of multi-class fuzzy support vector machine classifier for fault diagnosis of wind turbine. Fuzzy Sets Syst. 2016;297:128–40. http://dx.doi.org/10.1016/j.fs....
85.
Wang Z, Zhao N, Wang W, Tang R, Li S. A Fault diagnosis approach for gas turbine exhaust gas temperature based on fuzzy c-means clustering and support vector machine. Math Probl Eng. 2015;2015:1–11. http://www.hindawi.com/journal....
87.
Zedda M, Singh R. Gas turbine engine and sensor fault diagnosis using optimization techniques. J. Propuls Power. 2002;18(5):1019–25. http://www.scopus.com/inward/r....
88.
Kobayashi T, Simon DL. A hybrid neural network - genetic algorithm technique for aircraft engine performance diagnostics. J Propuls Power. 2005;21(4):751–8.
89.
Sampath S, Singh R. An integrated fault diagnostics model using genetic algorithm and neural networks. J Eng Gas Turbines Power. 2006; 128:49.
90.
Ogaji SOT, Sampath S, Marinai L, Singh R, Probert SD. Evolution strategy for gas-turbine fault-diagnoses. Appl Energy. 2005;81(2):222–30.
91.
Kong C, Kang M, Park G. Study on condition monitoring of 2-spool turbofan engine using non-linear gas path analysis method and genetic algorithms. Int J Mater Mech Manuf. 2013;1(2):214–20. http://www.ijmmm.org/index.php....
92.
Johnson RA, Wichern DW. Applied Multivariate Statistical Analysis. Pearson Education International. 2008. 1–773. https://www.pearsonhighered.co....
93.
Zhou J, Guo A, Celler B, Su S. Fault detection and identification spanning multiple processes by integrating PCA with neural network. Appl Soft Comput J. 2014;14:4–11. http://dx.doi.org/10.1016/j.as....
94.
Misra M, Yue HH, Qin SJ, Ling C. Multivariate process monitoring and fault diagnosis by multi scale PCA. Comput Chem Eng. 2002;26:1281–93.
95.
Li S, Wen J. A model-based fault detection and diagnostic methodology based on PCA method and wavelet transform. Energy Build 2014;68:63–71. http://dx.doi.org/10.1016/j.en....
97.
Sedighi T, Phillips P, Foote PD. Model-based intermittent fault detection. Procedia CIRP [Internet]. 2013;11:68–73. http://dx.doi.org/10.1016/j.pr....
98.
Jun H-B, Kim D. A Bayesian network-based approach for fault analysis. Expert Syst Appl 2017; 81:332–48. http://linkinghub.elsevier.com....
99.
Zhao Y, Wen J, Xiao F, Yang X, Wang S. Diagnostic Bayesian networks for diagnosing air handling units faults – part I: Faults in dampers, fans, filters and sensors. Appl Therm Eng. 2017;111:1272–86. http://dx.doi.org/10.1016/j.ap....
100.
Zhao Y, Wen J, Wang S. Diagnostic Bayesian networks for diagnosing air handling units faults - Part II: Faults in coils and sensors. Appl Therm Eng 2015;90:145–57. http://dx.doi.org/10.1016/j.ap....
102.
Guralnik V, Mylaraswamy D, Voges H. On handling dependent evidence and multiple faults in knowledge fusion for engine health management. Knowl Creat Diffus Util. 2002;.
103.
Lo CH, Chan PT, Wong YK, Rad AB, Cheung KL. Fuzzy-genetic algorithm for automatic fault detection in HVAC systems. Appl Soft Comput J. 2007; 7(2):554–60.
104.
Eker ÖF. A Hybrid Prognostic Methodology and its Application to Well- Controlled Engineering. Vol. 53, Cranfield University. 2013.
105.
Luo J, Namburu M, Pattipati KR, Qiao L, Chigusa S. Integrated model-based and data-driven diagnosis of automotive antilock braking systems. IEEE Trans Syst Man, Cybern Part ASystems Humans. 2010; 40(2):321–36.
106.
Ghimire R, Sankavaram C, Ghahari A, Pattipati K, Ghoneim Y, Howell M, et al. Integrated model-based and data-driven fault detection and diagnosis approach for an automotive electric power steering system. AUTOTESTCON (Proceedings). 2011; 1001445:70–7.
107.
Liang J, Du R. Model-based fault detection and diagnosis of HVAC systems using support vector machine method. Int J Refrig. 2007;30(6):1104–14.
108.
Wang S, Zhou Q, Xiao F. A system-level fault detection and diagnosis strategy for HVAC systems involving sensor faults. Energy Build. 2010;42(4): 477–90. http://dx.doi.org/10.1016/j.en....
109.
Gao D ce, Wang S, Shan K, Yan C. A system-level fault detection and diagnosis method for low delta-T syndrome in the complex HVAC systems. Appl Energy 2016;164:1028–38. http://dx.doi.org/10.1016/j.ap....
110.
Ozkurt C, Camci F, Atamuradov V, Odorry C. Integration of sampling based battery state of health estimation method in electric vehicles. Appl Energy 2016;175:356–67. http://dx.doi.org/10.1016/j.ap....
111.
Wang H, Xu P, Lu X, Yuan D. Methodology of comprehensive building energy performance diagnosis for large commercial buildings at multiple levels. Appl. Energy. 2016;169:14–27. http://dx.doi.org/10.1016/j.ap....
112.
Hare J, Gupta S, Najjar N, Orlando PD, Walthall R. System-level fault diagnosis with application to the environmental control system of an aircraft. 2015;.
113.
Lin Y, Zakwan S, Jennions I. A Bayesian approach to fault identification in the presence of multi-component degradation. Int J Progn Heal Manag. 2017;8.
114.
Verbert K, Babuška R, De Schutter B. Combining knowledge and historical data for system-level fault diagnosis of HVAC systems. Eng Appl Artif Intell. 2017;59:260–73.
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