CLC number: TP277
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2019-02-15
Cited: 0
Clicked: 6941
Santiago Ruzi-arenas, Zoltán Rusák, Imre Horváth, Ricardo Mejí-gutierrez. Systematic exploration of signal-based indicators for failure diagnosis in the context of cyber-physical systems[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(2): 152-175.
@article{title="Systematic exploration of signal-based indicators for failure diagnosis in the context of cyber-physical systems",
author="Santiago Ruzi-arenas, Zoltán Rusák, Imre Horváth, Ricardo Mejí-gutierrez",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="20",
number="2",
pages="152-175",
year="2019",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1700277"
}
%0 Journal Article
%T Systematic exploration of signal-based indicators for failure diagnosis in the context of cyber-physical systems
%A Santiago Ruzi-arenas
%A Zoltán Rusák
%A Imre Horváth
%A Ricardo Mejí-gutierrez
%J Frontiers of Information Technology & Electronic Engineering
%V 20
%N 2
%P 152-175
%@ 2095-9184
%D 2019
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1700277
TY - JOUR
T1 - Systematic exploration of signal-based indicators for failure diagnosis in the context of cyber-physical systems
A1 - Santiago Ruzi-arenas
A1 - Zoltán Rusák
A1 - Imre Horváth
A1 - Ricardo Mejí-gutierrez
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
IS - 2
SP - 152
EP - 175
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1700277
Abstract: Malfunction or breakdown of certain mission critical systems (MCSs) may cause losses of life, damage the environments, and/or lead to high costs. Therefore, recognition of emerging failures and preventive maintenance are essential for reliable operation of MCSs. There is a practical approach for identifying and forecasting failures based on the indicators obtained from real life processes. We aim to develop means for performing active failure diagnosis and forecasting based on monitoring statistical changes of generic signal features in the specific operation modes of the system. In this paper, we present a new approach for identifying emerging failures based on their manifestations in system signals. Our approach benefits from the dynamic management of the system operation modes and from simultaneous processing and characterization of multiple heterogeneous signal sources. It improves the reliability of failure diagnosis and forecasting by investigating system performance in various operation modes, includes reasoning about failures and forming of failures using a failure indicator matrix which is composed of statistical deviation of signal characteristics between normal and failed operations, and implements a failure indicator concept that can be used as a plug and play failure diagnosis and failure forecasting feature of cyber-physical systems. We demonstrate that our method can automate failure diagnosis in the MCSs and lend the MCSs to the development of decision support systems for preventive maintenance.
[1]Abdolsamadi A, Wang PF, Tamilselvan P, 2015. A generic fusion platform of failure diagnostics for resilient engineering system design. Proc ASME Int Design Engineering Technical Conf and Computers and Information in Engineering Conf, p.1-10.
[2]Abe S, 2010. Multiclass support vector machines. In: Abe S (Ed.), Support Vector Machines for Pattern Classification. Springer, London.
[3]Albertos P, Mareels I, 2010. Signal analysis. In: Albertos P, Mareels I (Eds.), Feedback and Control for Everyone. Springer Berlin Heidelberg.
[4]Alencar MS, da Rocha VCJr, 2005. Signal analysis. In: Alencar MS, da Rocha VCJr (Eds.), Communication Systems. Springer, Boston.
[5]Alzghoul A, Backe B, Löfstrand M, et al., 2014. Comparing a knowledge-based and a data-driven method in querying data streams for system fault detection: a hydraulic drive system application. Comput Ind, 65(8):1126-1135.
[6]Bellido I, Fernández G, 1991. Backpropagation growing networks: towards local minima elimination. Int Workshop on Artificial Neural Networks, p.130-135.
[7]Berk RA, 2008. Support vector machines. In: Berk RA (Ed.), Statistical Learning from a Regression Perspective. Springer, New York.
[8]Bocaniala CD, Palade V, 2006. Computational intelligence methodologies in fault diagnosis: review and state of the art. In: Palade V, Jain L, Bocaniala CD (Eds.), Computational Intelligence in Fault Diagnosis. Springer, London.
[9]Cholewa W, Korbicz J, Kościelny JM, et al., 2010. Diagnostic methods. In: Korbicz J, Kościelny JM (Eds.), Modeling, Diagnostics and Process Control: Implementation in the DiaSter System. Springer Berlin Heidelberg.
[10]Daum FE, 2015. Kalman filters. In: Baillieul J, Samad T (Eds.), Encyclopedia of Systems and Control. Springer, London.
[11]Ding SX, 2008a. Introduction. In: Ding SX (Ed.), Model-Based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools. Springer Berlin Heidelberg.
[12]Ding SX, 2008b. Norm based residual evaluation and threshold computation. In: Ding SX (Ed.), Model-Based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools. Springer Berlin Heidelberg.
[13]Fernando H, Surgenor B, 2017. An unsupervised artificial neural network versus a rule-based approach for fault detection and identification in an automated assembly machine. Rob Comput Integr Manuf, 43:79-88.
[14]Fortuna L, Graziani S, Rizzo A, et al., 2007. Fault detection, sensor validation and diagnosis. In: Fortuna L, Graziani S, Rizzo A, et al. (Eds.), Soft Sensors for Monitoring and Control of Industrial Processes. Springer, London.
[15]Fujimaki R, Yairi T, Machida K, 2005. Adaptive limit-checking for spacecraft using relevance vector autoregressive model. Proc 8th Int Symp on Artificial Intelligence, Robotics and Automation in Space, p.1-7.
[16]Gao ZW, Cecati C, Ding SX, 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(6):3757-3767.
[17]Gertler J, Singer D, 1990. A new structural framework for parity equation-based failure detection and isolation. Automatica, 26(2):381-388.
[18]Ghanbari T, 2015. Kalman filter based incipient fault detection method for underground cables. IET Gener Transm Distrib, 9(14):1988-1997.
[19]Hang J, Zhang JZ, Cheng M, 2016. Application of multi-class fuzzy support vector machine classifier for fault diagnosis of wind turbine. Fuzzy Sets Syst, 297:128-140.
[20]He P, Liu G, Tan C, et al., 2016. Nonlinear fault detection threshold optimization method for RAIM algorithm using a heuristic approach. GPS Sol, 20(4):863-875.
[21]Hood CS, Ji C, 1997. Proactive network-fault detection [telecommunications]. IEEE Trans Reliab, 46(3):333-341.
[22]Hwang W, Han K, Huh K, 2012. Fault detection and diagnosis of the electromechanical brake based on observer and parity space. Int J Autom Technol, 13(5):845-851.
[23]Irita T, Namerikawa T, 2015. Decentralized fault detection of multiple cyber attacks in power network via Kalman filter. Proc European Control Conf, p.3180-3185.
[24]Isermann R, 2006a. Fault diagnosis with classification methods. In: Isermann R (Ed.), Fault-Diagnosis Systems: an Introduction from Fault Detection to Fault Tolerance. Springer Berlin Heidelberg.
[25]Isermann R, 2006b. Supervision and fault management of processes—tasks and terminology. In: Isermann R (Ed.), Fault-Diagnosis Systems: an Introduction from Fault Detection to Fault Tolerance. Springer Berlin Heidelberg.
[26]Johnson DM, 1996. A review of fault management techniques used in safety-critical avionic systems. Prog Aerosp Sci, 32(5):415-431.
[27]Kang M, Ramaswami GK, Hodkiewicz M, et al., 2016. A sequential k-nearest neighbor classification approach for data-driven fault diagnosis using distance- and density-based affinity measures. Proc 1st Int Conf on Data Mining and Big Data, p.253-261.
[28]Kishore B, Satyanarayana MRS, Sujatha K, 2016. Efficient fault detection using support vector machine based hybrid expert system. Int J Syst Assur Eng Manag, 7(S1): 34-40.
[29]Krishnamachari B, Iyengar S, 2004. Distributed Bayesian algorithms for fault-tolerant event region detection in wireless sensor networks. IEEE Trans Comput, 53(3):241-250.
[30]Lee C, Lee D, Koo J, et al., 2009. Proactive fault detection schema for enterprise information system using statistical process control. LNCS, 5617:113-122.
[31]Lei Y, 2017. Signal processing and feature extraction. In: Lei Y (Ed.), Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery. Elsevier, Amsterdam.
[32]Leonhardt S, Ayoubi M, 1997. Methods of fault diagnosis. Contr Eng Pract, 5(5):683-692.
[33]Li WL, Monti A, Ponci F, 2014. Fault detection and classification in medium voltage DC shipboard power systems with wavelets and artificial neural networks. IEEE Trans Instrum Meas, 63(11):2651-2665.
[34]Liu YM, Ye LB, Zheng PY, et al., 2010. Multiscale classification and its application to process monitoring. J Zhejiang Univ-Sci C (Comput & Electron), 11(6):425-434.
[35]Luh GC, Wu CY, Cheng WC, 2004. Artificial immune regulation (AIR) for model-based fault diagnosis. Proc 3rd Int Conf on Artificial Immune Systems, p.28-41.
[36]Mathur A, Foody GM, 2008. Multiclass and binary SVM classification: implications for training and classification users. IEEE Geosci Remot Sens Lett, 5(2):241-245.
[37]Mehranbod N, Soroush M, Panjapornpon C, 2005. A method of sensor fault detection and identification. J Process Contr, 15(3):321-339.
[38]Miclea L, Sanislav T, 2011. About dependability in cyber-physical systems. Proc 9th East-West Design & Test Symp, p.17-21.
[39]Montaño JC, Bravo JC, Borrás MD, 2007. Joint time-frequency analysis of the electrical signal. In: Moreno-Mu noz A (Ed.), Power Quality: Mitigation Technologies in a Distributed Environment. Springer, London.
[40]Muralidharan V, Sugumaran V, Indira V, 2014. Fault diagnosis of monoblock centrifugal pump using SVM. Eng Sci Technol Int J, 17(3):152-157.
[41]Patan K, 2008. Decision making in fault detection. In: Patan K (Ed.), Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes. Springer Berlin Heidelberg.
[42]Ponsard C, Massonet P, Rifaut A, et al., 2005. Early verification and validation of mission critical systems. Electron Note Theor Comput Sci, 133:237-254.
[43]Puig V, Escobet T, Sarrate R, et al., 2015. Fault diagnosis and fault tolerant control in critical infrastructure systems. In: Kyriakides E, Polycarpou M (Eds.), Intelligent Monitoring, Control, and Security of Critical Infrastructure Systems. Springer Berlin Heidelberg.
[44]Qi JT, Zhao XG, Jiang Z, et al., 2007. An adaptive threshold neural-network scheme for rotorcraft UAV sensor failure diagnosis. Proc 4th Int Symp on Advances in Neural Networks, p.589-596.
[45]Ramoni M, Sebastiani P, 2001. Robust Bayes classifiers. Artif Intell, 125(1-2):209-226.
[46]Ramos PM, Martins RC, Rapuano S, et al., 2009. Frequency and time-frequency domain analysis tools in measurement. In: Pavese F, Forbes AB (Eds.), Data Modeling for Metrology and Testing in Measurement Science. Birkhäuser, Boston.
[47]Rezazadeh AS, Koofigar HR, Hosseinnia S, 2014. Robust leakage detection for electro hydraulic actuators using an adaptive nonlinear observer. Int J Prec Eng Manuf, 15(3):391-397.
[48]Rudin K, Ducard GJJ, Siegwart RY, 2014. A sensor fault detection for aircraft using a single Kalman filter and hidden Markov models. Proc IEEE Conf on Control Applications, p.991-996.
[49]Sharifi R, Langari R, 2013. Sensor fault diagnosis with a probabilistic decision process. Mech Syst Signal Process, 34(1-2):146-155.
[50]Shui AS, Chen WM, Zhang P, et al., 2009. Review of fault diagnosis in control systems. Proc Chinese Control and Decision Conf, p.5324-5329.
[51]Shukla A, Tiwari R, Kala R, 2010. Artificial neural networks. In: Shukla A, Tiwari R, Kala R (Eds.), Towards Hybrid and Adaptive Computing: a Perspective. Springer Berlin Heidelberg.
[52]Sobhani-Tehrani E, Khorasani K, 2009a. Fault detection and diagnosis. In: Sobhani-Tehrani E, Khorasani K (Eds.), Fault Diagnosis of Nonlinear Systems Using a Hybrid Approach. Springer, Boston.
[53]Sobhani-Tehrani E, Khorasani K, 2009b. Introduction. In: Sobhani-Tehrani E, Khorasani K (Eds.), Fault Diagnosis of Nonlinear Systems Using a Hybrid Approach. Springer, Boston.
[54]Somani AK, Vaidya NH, 1997. Understanding fault tolerance and reliability. Computer, 30(4):45-50.
[55]Soroush M, 1997. Nonlinear state-observer design with application to reactors. Chem Eng Sci, 52(3):387-404.
[56]Stockman M, El Ramli RS, Awad M, et al., 2012. An asymmetrical and quadratic support vector regression loss function for Beirut short term load forecast. Proc IEEE Int Conf on Systems, Man, and Cybernetics, p.651-656.
[57]Sun B, Luh PB, Jia QS, et al., 2014. Building energy doctors: an SPC and Kalman filter-based method for system-level fault detection in HVAC systems. IEEE Trans Autom Sci Eng, 11(1):215-229.
[58]Swetapadma A, Yadav A, 2016. Directional relaying using support vector machine for double circuit transmission lines including cross-country and inter-circuit faults. Int J Electr Power Energy Syst, 81:254-264.
[59]Tornil-Sin S, Ocampo-Martinez C, Puig V, et al., 2014. Robust fault diagnosis of nonlinear systems using interval constraint satisfaction and analytical redundancy relations. IEEE Trans Syst Man Cybern Syst, 44(1):18-29.
[60]Wang DW, Yu M, Low CB, et al., 2013. Health monitoring of engineering systems. In: Wang DW, Yu M, Low CB, et al. (Eds.), Model-Based Health Monitoring of Hybrid Systems. Springer, New York.
[61]Witczak M, 2014. Introduction. In: Witczak M (Ed.), Fault Diagnosis and Fault-Tolerant Control Strategies for Non-linear Systems: Analytical and Soft Computing Approaches. Springer, Cham.
[62]Wu W, Liu M, Liu Q, et al., 2016. A quantum multi-agent based neural network model for failure prediction. J Syst Sci Syst Eng, 25(2):210-228.
[63]Ye N, Zhao BJ, Salvendy G, 1993. Neural-networks-aided fault diagnosis in supervisory control of advanced manufacturing systems. Int J Adv Manuf Technol, 8(4):200-209.
[64]Yin G, Zhang YT, Li ZN, et al., 2014. Online fault diagnosis method based on incremental support vector data description and extreme learning machine with incremental output structure. Neurocomputing, 128:224-231.
[65]Yodo N, Wang PF, 2015. Resilience analysis and allocation for complex systems using Bayesian network. Proc ASME Int Design Engineering Technical Conf and Computers and Information in Engineering Conf, p.1-10.
[66]Zarei J, Tajeddini MA, Karimi HR, 2014. Vibration analysis for bearing fault detection and classification using an intelligent filter. Mechatronics, 24(2):151-157.
[67]Zhang JH, Chen YN, Xiong J, et al., 2014. Sensor fault detection and estimation for heat exchanger using unscented Kalman filter. Proc 9th IEEE Conf on Industrial Electronics and Applications, p.540-545.
[68]Zhang W, 2016. Fault diagnosis method based on artificial immune system. In: Zhang W (Ed.), Failure Characteristics Analysis and Fault Diagnosis for Liquid Rocket Engines. Springer Berlin Heidelberg.
[69]Zhou ZJ, Hu CH, Xu DL, et al., 2011. Bayesian reasoning approach based recursive algorithm for online updating belief rule based expert system of pipeline leak detection. Expert Syst Appl, 38(4):3937-3943.
[70]Zweigle O, Keil B, Wittlinger M, et al., 2013. Recognizing hardware faults on mobile robots using situation analysis techniques. In: Lee S, Cho H, Yoon KJ, et al. (Eds.), Intelligent Autonomous Systems 12. Springer Berlin Heidelberg.
Open peer comments: Debate/Discuss/Question/Opinion
<1>