Abstract
This chapter reviews condition monitoring techniques in mechanical and electrical systems. The condition monitoring domain in which the data is visualized is discussed and in particular the time, modal, frequency, and time-frequency domains. The generalized condition monitoring framework which includes the data acquisition device, data analysis device, feature selection device, and decision making device is also presented. Techniques for using these decision making devices are introduced. These are the finite element models, correlation based methods, and computational intelligence methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abdel-Latief AN, Abdel-Gawad AF, Ishak AA, Mandour ME (2003) Fault type classification in power distribution feeders utilizing statistical functions and neural networks. In: Proceedings of the universities power engineering conference, pp 614–617
Al-Habaibeh A, Zorriassatine F, Gindy N (2002) Comprehensive experimental evaluation of a systematic approach for cost effective and rapid design of condition monitoring systems using Taguchi’s method. J Mater Process Technol 124:372–383
Aliustaoglu C, Ertunc HM, Ocak H (2009) Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system. Mech Syst Signal Process 23:539–546
Allemang RJ, Brown DL (1982) A correlation coefficient for modal vector analysis. In: Proceedings of the 1st international modal analysis conference, pp 1–18
Alvin KF (1996) Finite element model updating via bayesian estimation and minimisation of dynamic residuals. In: Proceedings of the 14th international modal analysis conference, pp 428–431
Andrade FA, Esat I, Badi MNM (2001) A new approach to time-domain vibration condition monitoring: gear tooth fatigue crack detection and identification by the Kolmogorov-Smirnov test. J Sound Vib 240:909–919
Atalla MJ (1996) Model updating using neural networks. PhD thesis, Virginia Polytechnic Institute and State University
Barschdorf D, Femmer U (1995) Signal processing and pattern recognition methods for biomedical sound analysis. In: Proceedings of the acoustical and vibratory surveillance methods and diagnostics techniques, 2nd international symposium, pp 279–290
Bartelmus W, Zimroz R (2009) A new feature for monitoring the condition of gearboxes in non-stationary operating conditions. Mech Syst Signal Process 23:1528–1534
Ben-Haim Y, Prells U (1993) Selective sensitivity in the frequency domain, Part I: Theory. Mech Syst Signal Process 7:461–475
Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford
Booth C, McDonald JR (1998) The use of artificial neural networks for condition monitoring of electrical power transformers. Neurocomputing 23:97–109
Borghetti A, Bosetti M, Nucci CA, Paolone M, Abur A (2010) Integrated use of time-frequency wavelet decompositions for fault location in distribution networks: theory and experimental validation. IEEE Trans Power Deliv 25:3139–3146
Bouhouche S, Yazid LL, Hocine S, Bast J (2010) Evaluation using online support-vector-machines and fuzzy reasoning. Application to condition monitoring of speeds rolling process. Control Eng Pract 18:1060–1068
Bouhouche S, Yahi M, Bast J (2011) Combined use of principal component analysis and self organisation map for condition monitoring in pickling process. Appl Soft Comput J 11:3075–3082
Butler S, Ringwood J (2010) Particle filters for remaining useful life estimation of abatement equipment used in semiconductor manufacturing. In: Proceedings of the conference on control and fault-tolerant systems, pp 436–441
Cawley P, Adams RD (1979) The location of defects from measurements of natural frequencies. J Strain Anal 14:49–57
Chen JC, Garba JA (1980) Analytical model improvement using modal test results. Am Inst Aeronaut Astronaut J 18:684–690
Chen W, Yeh CP, Yang H (2011) ToMFIR-based fault detection approach in frequency domain. J Syst Eng Electron 22:33–37
Cohen L (1989) Time-frequency distributions – a review. In: Proceedings of the IEEE, pp 941–981
D’Ambrogio W, Zobel PB (1994) Damage detection in truss structures using a direct updating technique. In: Proceedings of the 19th international seminar for modal analysis, pp 657–667
Daubechies I (1987) Orthogonal bases of wavelets with finite support connection with discrete filters. In: Proceedings of the international conference on wavelets, pp 38–66
Doebling SW, Farrar CR, Prime MB, Shevitz DW (1996) Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: a literature review. Los Alamos National Laboratory report LA-13070-MS, Los Alamos
Dunn SA (1998) The use of genetic algorithms and stochastic hill-climbing in dynamic finite-element model identification. Comput Struct 66:489–497
Elangovan M, Ramachandran KI, Sugumaran V (2010) Studies on bayes classifier for condition monitoring of single point carbide tipped tool based on statistical and histogram features. Expert Syst Appl 37:2059–2065
Ewins DJ (1995) Modal testing: theory and practice. Research Studies Press, Letchworth
Farrar CR, Baker WE, Bell TM, Cone KM, Darling TW, Duffey TA, Eklund A, Migliori A (1994) Dynamic characteristics and damage detection in the I-40 bridge over the Rio Grande. Los Alamos National Laboratory report LA-12767-MS, Los Alamos
Fox CHJ (1992) The location of defects in structures: a comparison of the use of natural frequency and mode shape data. In: Proceedings of the 10th international modal analysis conference, pp 522–528
Friswell MI, Mottershead JE (1995) Finite element model updating in structural dynamics. Kluwer Academic Publishers Group, Dordrecht
Garcia-Escudero LA, Duque-Perez O, Morinigo-Sotelo D, Perez-Alonso M (2011) Robust condition monitoring for early detection of broken rotor bars in induction motors. Expert Syst Appl 38:2653–2660
Garvey DR, Baumann J, Lehr J, Hughes B, Hines JW (2009) Pattern recognition-based remaining useful life estimation of bottomhole assembly tools. In: Proceedings of the SPE/IADC drilling conference, pp 82–89
Gawronski W, Sawicki JT (2000) Structural damage detection using modal norms. J Sound Vib 229:194–198
Gedafa DS, Hossain M, Miller R, Van T (2010) Estimation of remaining service life of flexible pavements from surface deflections. J Transp Eng 136:342–352
Gunal S, Ece DG, Gerek ON (2009) Induction machine condition monitoring using notch-filtered motor current. Mech Syst Signal Process 23:2658–2670
Guyan RJ (1965) Reduction of stiffness and mass matrices. Am Inst Aeronaut Astronaut J 3:380
Gysin H (1990) Comparison of expansion methods for FE model localization. In: Proceedings of the 8th international modal analysis conference, pp 195–204
Haroon M, Adams DE (2007) Time and frequency domain nonlinear system characterization for mechanical fault identification. Nonlinear Dyn 50:387–408
Haug EF, Choi KK (1984) Structural design sensitivity with generalized global stiffness and mass matrices. Am Inst Aeronaut Astronaut J 22:1299–1303
He Q, Yan R, Kong F, Du R (2009) Machine condition monitoring using principal component representations. Mech Syst Signal Process 23:446–466
He ZY, Li XP, Wu LY, Xia LL, Qian QQ (2010) A new single ended fault location method using travelling wave natural frequencies – Part 2: Problems concerned in implementation. In: Proceedings of the IEEE PES general meeting, pp 1–6
Hemez FM (1993) Theoretical and experimental correlation between finite element models and modal tests in the context of large flexible structures. PhD thesis, University of Colorado
Huang SF, Huang H, Wang XG, Wang LC (2009) Analysis on natural frequencies of ultra-high voltage transmission lines in short circuit fault. High Volt Eng 35:2059–2065
Hussain S, Gabbar HA (2011) A novel method for real time gear fault detection based on pulse shape analysis. Mech Syst Signal Process 25:1287–1298
Imregun M, Ewins DJ (1993) An investigation into mode shape expansion techniques. In: Proceedings of the 11th international modal analysis conference, pp 168–175
Imregun M, Visser WJ, Ewins DJ (1995) Finite element model updating using frequency response function data – Part I: Theory and initial investigation. Mech Syst Signal Process 9:187–202
Jain A, Thoke AS, Koley E, Patel RN (2009) Fault classification and fault distance location of double circuit transmission lines for phase to phase faults using only one terminal data. In: Proceedings of the international conference on power systems, pp 1–6
Janter T, Sas P (1990) Uniqueness aspects of model-updating procedure. Am Inst Aeronaut Astronaut J 28:538–543
Jayabharata Reddy M, Mohanta DK (2011) A modular approach for classification and location of arcing and non-arcing faults on transmission lines. Int J Energy Technol Policy 7:309–324
Kazemi S, Fooladi A, Rahai AR (2010) Implementation of the modal flexibility variation to fault identification in thin plates. Acta Astronaut 66:414–426
Kazemi S, Rahai AR, Daneshmand F, Fooladi A (2011) Implementation of modal flexibility variation method and genetically trained ANNs in fault identification. Ocean Eng 38:774–781
Khorashadi-Zadeh H, Li Z (2008) Fault classification and fault location using ANN for medium voltage cables: design and implementation. Intell Autom Soft Comput 14:479–489
Khosravi A, Llobet JA (2007) A hybrid method for fault detection and modelling using modal intervals and ANFIS. In: Proceedings of the American control conference, pp 3003–3008
Kim JH, Jeon HS, Lee SW (1992) Application of modal assurance criteria for detecting and locating structural faults. In: Proceedings of the 10th international modal analysis conference, pp 536–540
Kim S, Kim S, Choi H (2010a) Remaining life estimation of a level luffing crane component by computer simulation. J Korean Inst Met Mater 48:489–497
Kim YS, Lee DH, Kim SK (2010b) Fault classification for rotating machinery using support vector machines with optimal features corresponding to each fault type. Trans Korean Soc Mech Eng 34:1681–1689
Kudva J, Munir N, Tan P (1991) Damage detection in smart structures using neural networks and finite element analysis. In: Proceedings of the ADPA/AIAA/ASME/SPIE conference on active materials and adaptive structures, pp 559–562
Lai JY, Young KF (1995) Dynamics of graphite/epoxy composite under delamination fracture and environmental effects. J Comput Struct 30:25–32
Lam HF, Ko JM, Wong CW (1995) Detection of damage location based on sensitivity analysis. In: Proceedings of the 13th international modal analysis conference, pp 1499–1505
Larson CB, Zimmerman DC (1993) Structural model refinement using a genetic algorithm approach. In: Proceedings of the 11th international modal analysis conference, pp 1095–1101
Lau HCW, Dwight RA (2011) A fuzzy-based decision support model for engineering asset condition monitoring – a case study of examination of water pipelines. Expert Syst Appl 38:13342–13350
Leath WJ, Zimmerman DC (1993) Analysis of neural network supervised training with application to structural damage detection. Damage and control of large structures. In: Proceedings of the 9th Virginia Polytechnic Institute and State University symposium, pp 583–594
Lee BT, Sun CT, Liu D (1987) An assessment of damping measurement in the evaluation of integrity of composite beams. J Reinf Plast Compos 6:114–125
Li H, Zhang Z, Guo Z, Zou S, Wang F (2010) Rolling bearing fault diagnosis using hough transform of time-frequency image. J Vib Meas Diagn 30:634–637
Lieven NAJ, Ewins DJ (1988) Spatial correlation of mode shapes, the co-ordinate modal assurance criterion. In: Proceedings of the 6th international modal analysis conference, pp 690–695
Lifshitz JM, Rotem A (1969) Determination of reinforcement unbonding of composites by a vibration technique. J Compos Mater 3:412–423
Liguo Z, Yutian W, Sheng Z, Guangpu H (2009) The fault diagnosis of machine based on modal analysis. In: Proceedings of the international conference on measuring technology and mechatronics automation, pp 738–741
Lin RM, Lim MK, Du H (1995) Improved inverse eigensensitivity method for structural analytical model updating. J Vib Acoust 117:192–198
Lin S, He Z, Zang T, Qian Q (2010) Novel approach of fault type classification in transmission lines based on rough membership neural networks. Proc Chin Soc Electr Eng 30:72–79
Loutas TH, Roulias D, Pauly E, Kostopoulos V (2011) The combined Use of vibration, acoustic emission and Oil debris on-line monitoring towards a more effective condition monitoring of rotating machinery. Mech Syst Signal Process 25:1339–1352
Luo GY, Osypiw D, Irle M (2000) Real-time condition monitoring by significant and natural frequencies analysis of vibration signal with wavelet filter and autocorrelation enhancement. J Sound Vib 236:413–430
Lyon R (1995) Structural diagnostics using vibration transfer functions. Sound Vib 29:28–31
Ma H, Zhang L, Li H, Xie W (2007a) The application of modal analysis in fault diagnosis of AC motor. In: Proceedings of the international conference on condition monitoring and diagnosis, pp 217–220
Ma H, Li H, Xie W, Chen F (2007b) Vibration research on winding faults of induction motor based on experiment modal analysis method. In: Proceedings of the 8th international power engineering conference, pp 366–370
Ma H, Sun W, Ren Z, Wen B (2009) Feature analysis of oil-film instability fault based on time-frequency methods in rotor systems. In: Proceedings of the 2nd international conference on intelligent computation technology and automation, pp 541–544
Ma H, Li CF, Xuan GJ, Wen BC (2010) Time-frequency feature analysis of oil-film instability fault in a rotor system. J Vib Shock 29:193–195+198
Maia NMM, Silva JMM (1997) Theoretical and experimental modal analysis. Research Studies Press, Letchworth
Maia NMM, Silva JMM, Sampaio RPC (1997) Localization of damage using curvature of the frequency-response-functions. In: Proceedings of the 15th international modal analysis conference, pp 942–946
Maia NMM, Silva JMM, Sampaio RPC (1999) On the use of frequency response functions for damage detection. In: Proceedings of the 2nd international conference on identification in engineering system, pp 460–471
Mares C, Surace C (1996) An application of genetic algorithms to identify damage in elastic structures. J Sound Vib 195:195–215
Marwala T (1997) Multi-criteria method for determining damage on structures. Master’s thesis, University of Pretoria
Marwala T (2001) Fault identification using neural networks and vibration data. PhD thesis, University of Cambridge
Marwala T (2010) Finite element model updating using computational intelligence techniques. Springer, London
Mayes RL (1992) Error localization using mode shapes – an application to a two link robot arm. In: Proceedings of the 10th international modal analysis conference, pp 886–891
Messina A, Jones IA, Williams EJ (1996) Damage detection and localisation using natural frequency changes. In: Proceedings of the 1st international conference on identification in engineering system, pp 67–76
Messina A, Williams EJ, Contursi T (1998) Structural damage detection by a sensitivity and statistical-based method. J Sound Vib 216:791–808
Migliori A, Bell TM, Dixon RD, Strong R (1993) Resonant ultrasound nondestructive inspection. Los Alamos National Laboratory report LS-UR-93-225, Los Alamos
Miya WS, Mpanza LJ, Nelwamondo FV, Marwala T (2008) Condition monitoring of oil-impregnated paper bushings using extension neural network, Gaussian mixture models and hidden Markov models. In: Proceedings of the IEEE international conference on man, systems, and cybernetics, pp 1954–1959
Murthy VS, Mohanta DK, Gupta S (2011) Video surveillance-based insulator condition monitoring analysis for substation monitoring system (SMS). Int J Inf Commun Technol 3:11–31
Nandi S, Ilamparithi T, Lee SB, Hyun D (2009) Pole pair and rotor slot number independent frequency domain based detection of eccentricity faults in induction machines using a semi on-line technique. In: Proceedings of the IEEE international symposium on diagnostics for electric machines, power electronics and drives, pp 1–7
Nelwamondo FV, Marwala T (2007) Techniques for handling missing data: applications to online condition monitoring. Int J Innov Comput Inf Control 4:1507–1526
Newland DE (1993) An introduction to random vibration, spectral and wavelet analysis. Longman/Harlow/Wiley, New York
Norris MA, Meirovitch L (1989) On the problem of modelling for parameter identification in distributed structures. Int J Numer Methods Eng 28:2451–2463
O’Callahan JC, Avitabile P, Riemer R (1989) System equivalent reduction expansion process. In: Proceedings of the 7th international modal analysis conference, pp 17–21
Pandurangaiah D, Reddy CC, Govindan TP, Mandlik M, Ramu TS (2008) Estimation of remaining life of power transformers. In: Proceedings of the IEEE international symposium on electrical insulation, pp 243–246
Park S, Kim JW, Lee C, Park SK (2011) Impedance-based wireless debonding condition monitoring of CFRP laminated concrete structures. NDT E Int 44:232–238
Paz M (1984) Dynamic condensation. Am Inst Aeronaut Astronaut J 22:724–727
Pedregal DJ, Carnero MC (2006) State space models for condition monitoring: a case study. Reliab Eng Syst Saf 91:171–180
Prasannamoorthy V, Devarajan N (2010) Frequency domain technique for fault diagnosis in analog circuits – software and hardware implementation. J Theor Appl Inf Technol 22:107–119
Prime MB, Shevitz DW (1996) Damage detection of street frame by modal testing. In: Proceedings of the 11th international modal analysis conference, pp 1437–1443
Purbolaksono J, Khinani A, Ali AA, Rashid AZ, Nordin NF (2009) Iterative technique and finite element simulation for supplemental condition monitoring of water-tube boiler. Simul Model Pract Theory 17:897–910
Qian S, Jiao W, Hu H (2010) Time-frequency analysis and fault diagnosis of air-lift compressor for an offshore oil and gas platform. In: Proceedings of the 29th Chinese control conference, pp 2977–2980
Riml S, Piontke A, Larcher L, Kompatscher P (2010) Quantification of faults resulting from disregard of standardised facial photography. J Plast Reconstr Aesthet Surg 64:898–901
Rishvanth KP, Rai S, Kumar S, Sudheer SK, Raina JP (2009) Design and simulation of optical frequency domain reflectometer for short distance fault detection in optical fibers and integrated optical devices using Ptolemy-II. In: Proceedings of the international conference on ultra modern telecommunications and workshops, pp 1–3
Rolo-Naranjo A, Montesino-Otero ME (2005) A method for the correlation dimension estimation for on-line condition monitoring of large rotating machinery. Mech Syst Signal Process 19:939–954
Salawu OS (1995) Non-destructive assessment of structures using integrity index method applied to a concrete highway bridge. Insight 37:875–878
Salawu OS, Williams C (1994) Damage location using vibration mode shapes. In: Proceedings of the 11th international modal analysis conference, pp 933–939
Schultz MJ, Warwick DN (1971) Vibration response: a non-destructive test for fatigue crack damage in filament-reinforced composites. J Compos Mater 5:394–404
Schultz MJ, Pai PF, Abdelnaser AS (1996) Frequency response function assignment technique for structural damage identification. In: Proceedings of the 14th international modal analysis conference, pp 105–111
Sestieri A, D’Ambrogio W (1989) Why be modal: how to avoid the use of modes in the modification of vibrating systems. In: Proceedings of the 7th international modal analysis conference, pp 25–30
Sinha JK (2009) Recent trends in fault quantification in rotating machines. Adv Vib Eng 8:79–85
Surace C, Ruotolo R (1994) Crack detection of a beam using the wavelet transform. In: Proceedings of the 12th international modal analysis conference, pp 1141–1148
Tao B, Zhu L, Ding H, Xiong Y (2007) An alternative time-domain index for condition monitoring of rolling element bearings – a comparison study. Reliab Eng Syst Saf 92:660–670
Thai N, Yuan L (2011) Transmission line fault type classification based on novel features and neuro-fuzzy system. Electr Power Compon Syst 38:695–709
Tian Z, Jin T, Wu B, Ding F (2011) Condition based maintenance optimization for wind power generation systems under continuous monitoring. Renew Energy 36:1502–1509
Treetrong J (2011a) Fault prediction of induction motor based on time-frequency analysis. Appl Mech Mater 52–54:115–120
Treetrong J (2011b) The use of higher-order spectrum for fault quantification of industrial electric motors. Lect Notes Electr Eng 70:59–68
Vilakazi CB, Marwala T (2007) Incremental learning and its application to bushing condition monitoring. Lect Notes Comput Sci 4491:1241–1250
Vilakazi CB, Marwala T (2009) Computational intelligence approach to Bushing condition monitoring: incremental learning and its application. In: Intelligent engineering systems and computational cybernetics, pp 161–171
Vilakazi CB, Mautla RP, Moloto EM, Marwala T (2005) On-line Bushing condition monitoring. In: Proceedings of the 5th WSEAS/IASME international conference on electric power system, pp 406–411
Ville J (1948) Théorie et Applications de la Notion de Signal Analytique. Cables et Transm 2A:61–74
Wei L, Hua W, Pu H (2009) Neural network modeling of aircraft power plant and fault diagnosis method using time frequency analysis. In: Proceedings of the Chinese control and decision conference, pp 353–356
Weidl G, Madsen AL, Israelson S (2005) Applications of object-oriented Bayesian networks for condition monitoring, root cause analysis and decision support on operation of complex continuous processes. Comput Chem Eng 29:1996–2009
West WM (1984) Illustration of the use of modal assurance criterion to detect structural changes in an orbiter test specimen. In: Proceedings of air force conference on aircraft structural integrity, pp 1–6
Wheeler JA, Zurek H (1983) Quantum theory and measurement. Princeton University Press, Princeton
Widodo A, Yang BS (2007) Support vector machine in machine condition monitoring and fault diagnosis. Mech Syst Signal Process 21:2560–2574
Wigner EP (1932) On quantum correction for thermodynamic equilibrium. Phys Rev 40:749–759
Wong WK, Loo CK, Lim WS, Tan PN (2010) Thermal condition monitoring system using log-polar mapping, quaternion correlation and Max-product fuzzy neural network classification. Neurocomputing 74:164–177
Worden KA, Ball A, Tomilinson G (1993) Neural networks for fault location. In: Proceedings of the 11th international modal analysis conference, pp 47–54
Worden K, Manson G, Fieler NRJ (2000) Damage detection using outlier analysis. J Sound Vib 229:647–667
Wu X, Ghaboussi J, Garret JH (1992) Use of neural networks in detection of structural damage. Comput Struct 42:649–659
Xia L, He Z, Li X, Chen S (2010) A fault location method based on natural frequencies and empirical mode decomposition for mixed overhead-cable lines. Autom Electr Power Syst 34:67–73
Xie CL, Liu YK, Xia H (2009) Application of ant colony optimization in NPP classification fault location. Nucl Power Eng 30:108–112
Yadav SK, Tyagi K, Shah B, Kalra PK (2011) Audio signature-based condition monitoring of internal combustion engine using FFT and correlation approach. IEEE Trans Instrum Meas 60:1217–1226
Yanagawa T, Kaneko M, Iida I, Fujiwara R (2010) Development of new remaining life estimation method for main parts of hydro-turbine in hydro electric power station. In: Proceedings of the AIP conference, pp 834–839
Yeh CP, Yang HL, Chen W (2010) A fault detection approach using both control and output error signals in frequency domain. In: Proceedings of the IEEE/ASME international conference on mechatronic and embedded systems and applications, pp 341–344
Yu W, Chao S (2010) Fault diagnosis way based on RELAX algorithms in frequency domain for the squirrel cage induction motors. In: Proceedings of the international conference on computer intelligence and software engineering, pp 1–4
Zhang L, Huang AQ (2011) Model-based fault detection of hybrid fuel cell and photovoltaic direct current power sources. J Power Sour 196:5197–5204
Zhang Y, Suonan J (2010) Time domain fault location method based on UHV transmission line parameter identification using two terminal data. In: Proceedings of the Asia-Pacific power and energy engineering conference, pp 1–5
Zhou JH, Pang CK, Lewis FL, Zhong ZW (2011a) Dominant feature identification for industrial fault detection and isolation applications. Expert Syst Appl 38:10676–10684
Zhou Y, Tao T, Mei X, Jiang G, Sun N (2011b) Feed-axis gearbox condition monitoring using built-in position sensors and EEMD method. Robot Comput Integr Manuf 27:785–793
Zhu K, Wong YS, Hong GS (2009) Wavelet analysis of sensor signals for tool condition monitoring: a review and some new results. Int J Mach Tools Manuf 49:537–553
Zhu Y, Wang W, Tong S (2011) Fault detection and fault-tolerant control for a class of nonlinear system based on fuzzy logic system. ICIC Expr Lett 5:1597–1602
Zi Y, Chen X, He Z, Chen P (2005) Vibration based modal parameters identification and wear fault diagnosis using Laplace wavelet. Key Eng Mater 293–294:83–190
Zimmerman DC, Kaouk M (1992) Eigenstructure assignment approach for structural damage detection. Am Inst Aeronaut Astronaut J 30:1848–1855
Zio E, Peloni G (2011) Particle filtering prognostic estimation of the remaining useful life of nonlinear components. Reliab Eng Syst Saf 96:403–409
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag London Limited
About this chapter
Cite this chapter
Marwala, T. (2012). Introduction to Condition Monitoring in Mechanical and Electrical Systems. In: Condition Monitoring Using Computational Intelligence Methods. Springer, London. https://doi.org/10.1007/978-1-4471-2380-4_1
Download citation
DOI: https://doi.org/10.1007/978-1-4471-2380-4_1
Published:
Publisher Name: Springer, London
Print ISBN: 978-1-4471-2379-8
Online ISBN: 978-1-4471-2380-4
eBook Packages: EngineeringEngineering (R0)