Caesarendra, Wahyu

Kankar, Pavan K

Li, Chuan

Wang, Dong

Widodo, Achmad

Shock and Vibration

English

Research Online

University of Wollongong Research Online Faculty of Engineering and Information Sciences - Papers: Part B Faculty of Engineering and Information Sciences 2016 Fault Diagnosis and Prognosis of Critical Components Dong Wang City University of Hong Kong Chuan Li Politecnica Salesiana University Achmad Widodo Diponegoro University Pavan K. Kankar Indian Institute of Information Technology, Design and Manufacturing, Jabalpur Wahyu Caesarendra University of Wollongong, wc026@uowmail.edu.au Follow this and additional works at: https://ro.uow.edu.au/eispapers1  Part of the Engineering Commons, and the Science and Technology Studies Commons Recommended Citation Wang, Dong; Li, Chuan; Widodo, Achmad; Kankar, Pavan K.; and Caesarendra, Wahyu, "Fault Diagnosis and Prognosis of Critical Components" (2016). Faculty of Engineering and Information Sciences - Papers: Part B. 315. https://ro.uow.edu.au/eispapers1/315 Research Online is the open access institutional repository for the University of Wollongong. For further information contact the UOW Library: research-pubs@uow.edu.au Fault Diagnosis and Prognosis of Critical Components Abstract Editorial Keywords fault, components, prognosis, critical, diagnosis Disciplines Engineering | Science and Technology Studies Publication Details Wang, D., Li, C., Widodo, A., Kankar, P. K. & Caesarendra, W. (2016). Fault Diagnosis and Prognosis of Critical Components. Shock and Vibration: shock and vibration control - crashworthiness - structural dynamics - impact engineering - sound, 2016 9597656-1-9597656-3. This journal article is available at Research Online: https://ro.uow.edu.au/eispapers1/315 EditorialFault Diagnosis and Prognosis of Critical ComponentsDong Wang,1 Chuan Li,2 Achmad Widodo,3Pavan Kumar Kankar,4 and Wahyu Caesarendra51City University of Hong Kong, Kowloon Tong, Hong Kong2Universidad Politécnica Salesiana, 010150 Cuenca, Ecuador3University of Diponegoro, Semarang 50275, Indonesia4PDPM Indian Institute of Information Technology, Jabalpur 482005, India5University of Wollongong, Wollongong, NSW 2522, AustraliaCorrespondence should be addressed to Dong Wang; dongwang4-c@my.cityu.edu.hkReceived 12 November 2015; Accepted 12 November 2015Copyright © 2016 Dong Wang et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Some critical components, such as bearings, gearboxes, andimpellers, are widely used in machines. Their faults mayaccelerate failures of other components and finally result inmachine breakdowns. To prevent any unexpected machinebreakdowns and accidents, early faults of critical componentsshould be detected as soon as possible. Once early faultsof critical components are diagnosed, their performancedegradation assessment and remaining useful life estimationshould be conducted to maximize lifetime of critical com-ponents. This special issue mainly focuses on fault diagnosisand prognosis of critical components and enables researchersand engineers to share their most recent developments andideas so as to improve technologies adopted in the researchcommunity of machine fault diagnosis and prognosis.We have receivedmany submissions from different coun-tries and regions. After rigorous peer review processes, thefollowing manuscripts have been accepted and their contentsare briefly summarized as follows.In the paper entitled “Gearbox Fault Identification andclassification with Convolutional Neural Networks,” Z. Chenet al. proposed a deep learning convolutional neural networkbased intelligent method for gearbox fault diagnosis. Thecomparison with the classic support vector machine baseddiagnostic method demonstrated that their method hashigher fault prediction accuracies.In the paper entitled “Rolling Element Bearing FaultDiagnosis Based on Multiscale General Fractal Features,”W. Wen et al. proposed a multiscale general fractal dimen-sions based method to identify different bearing faultsunder different operating conditions and they experimentallydemonstrated that their method has better performance thanwavelet packet and empirical mode decomposition basedmethods under different operating conditions.In the paper entitled “Condition Monitoring and FaultDiagnosis for an Antifalling Safety Device,” G. Yang andH. Liang proposed a discrete wavelet transform based noisereduction method for condition monitoring and fault diag-nosis of an antifalling safety device used in a constructionelevator. Moreover, they experimentally found that the sym8wavelet basis is the most effective wavelet basis among somewavelet basis candidates.In the paper entitled “Screw Performance DegradationAssessment Based on Quantum Genetic Algorithm andDynamic Fuzzy Neural Network,” X. Zhang et al. usedquantumgenetic algorithm to optimize dynamic fuzzy neuralnetwork so as to propose a screw performance degradationassessment method. The results show that the proposedmethod has higher prediction accuracies than backpropaga-tion neural network and radial basis function neural network.In the paper entitled “A New Transmissibility BasedIndicator of Local Variation in Structure and Its Applicationfor Damage Detection,” X. Z. Li et al. proposed a newtransmissibility based indicator to monitor local variationsof stiffness and damping for structure health conditionmonitoring. The results demonstrated that their proposedindicator is effective in localizing cracks of beam structures.In the paper entitled “Planetary GearboxVibration SignalCharacteristics Analysis and Fault Diagnosis,” Q. Miao andHindawi Publishing CorporationShock and VibrationVolume 2016, Article ID 9597656, 3 pageshttp://dx.doi.org/10.1155/2016/95976562 Shock and VibrationQ. Zhou proposed a planetary vibrationmodel to understandplanetary vibration components. The results showed thattheir proposed model matches vibration data collected froma planetary test rig.In the paper entitled “Multifault Diagnosis of RollingElement Bearings Using a Wavelet Kurtogram and VectorMedian-Based Feature Analysis,” P. H. Nguyen and J.-M.Kim proposed a wavelet kurtogram and vector median-based feature analysis method to diagnose different bearingfaults under different rotational speeds and crack sizes.Compared with principle component analysis, independentcomponent analysis, and linear discriminant analysis, theirproposed analysis method has better performance for featureextraction.In the paper entitled “Fault Diagnosis for a MultistagePlanetary Gear Set Using Model-Based Simulation andExperimental Investigation,” G. Li et al. proposed a coupledlateral and torsional dynamic model to predict modulationsidebands of a two-stage compound planetary gear set.Moreover, fault characteristic frequencies were derived.Theirwork provided a theoretical support for fault diagnosis of amultistate planetary gear set by using modulation sidebandanalysis.In the paper entitled “In Situ Measurement of SeekingSpeed and Seeking InducedHead-Disk Interface Instability inHard Disk Drives,” Y. Wang et al. investigated the instabilityof head-disk interface caused by the voice coil motor endcrashing the crash stop in the process of magnetic headseeking and they proposed amaximum likelihood estimationand extended Kalman filter based method to seek speed. Theresults show that the rolling mode of air bearing will lead toa serious disk damage in the case of high seeking speed.In the paper entitled “Cyclostationary Analysis for Gear-box and Bearing Fault Diagnosis,” Z. Feng and F. Chuderived explicit expressions of cyclic correlation and cyclicspectrum of modulation and frequency modulation signalsand thoroughly investigated their applications to gearbox andbearing fault diagnosis.In the paper entitled “Sparse Signal Representations ofBearing Fault Signals for Exhibiting Bearing Fault Features,”W. Peng et al. proposed an optimal wavelet filtering basedsparse signal presentation method. The results demonstratedthat their proposed method is effective in using a few waveletcoefficients to represent bearing fault features.In the paper entitled “Feature Extraction of Faulty RollingElement Bearing under Variable Rotational Speed and GearInterferences Conditions,” D. Zhao et al. proposed an instan-taneous dominant meshing multiply and empirical modedecomposition based on bearing fault feature extractionmethod under varying operation conditions. Multiple vibra-tion mixtures were used to verify the effectiveness of theirproposed method.In the paper entitled “Two General Architectures forIntelligent Machine Performance Degradation Assessment,”Y. Xu et al. proposed two general architectures for intelligentmachine performance degradation assessment. Their pro-posed architectures have wide applications, such as gearboxperformance degradation and bearing performance degrada-tion.In the paper entitled “Application of Reassigned WaveletScalogram in Wind Turbine Planetary Gearbox Fault Diag-nosis under Nonstationary Conditions,” X. Chen and Z.Feng applied reassigned wavelet scalogram to diagnose windturbine planetary gearboxes. The experimental and in situevaluations were conducted to verify the effectiveness ofreassigned wavelet scalogram for turbine planetary gearboxfault diagnosis.In the paper entitled “Research on the Sparse Rep-resentation for Gearbox Compound Fault Features UsingWavelet Bases,” C. Luo et al. proposed a split augmentedLagrangian shrinkage algorithm based method to extractgearbox compound fault features. Laplace wavelet optimizedby correlation filtering was used to design a dictionary forconstructing redundant basis functions. The results demon-strated the effectiveness of their proposed method andshowed that a series of sparse coefficients are able to showgearbox compound fault features.In the paper entitled “A Hybrid Prognostic Approach forRemaining Useful Life Prediction of Lithium-Ion Batteries,”W.-A. Yang et al. proposed a selective kernel ensemble-based relevance vector machine and particle swarm opti-mization based prognostic method for remaining use-ful life prediction. Their proposed method has potentialapplications to prognostics of gearbox, bearing, and soon.In the paper entitled “Application of Artificial NeuralNetwork for Damage Detection in Planetary Gearbox ofWind Turbine,” M. Straczkiewicz, and T. Barszcz proposedan artificial neural network and linear regression basedmethod to distinguish healthy and damaged planetary gear-box conditions. Their proposed method has great potentialfor conditionmonitoring of various rotatingmachinery, suchas mining excavators and gas turbines.In the paper entitled “Trace Ratio Criterion-Based KernelDiscriminant Analysis for Fault Diagnosis of Rolling ElementBearings Using Binary Immune Genetic Algorithm,” W.-A.Yang et al. proposed a trace ratio criterion-based kernel dis-criminant analysis based method for bearing fault diagnosis.The parameters of their proposed method were optimized bybinary immune genetic algorithm. The results showed thattheir proposedmethod is better than somepopular intelligentmethods for bearing fault diagnosis.In the paper entitled “Gearbox Fault Diagnosis UsingComplementary Ensemble Empirical Mode Decompositionand Permutation Entropy,” L. Zhao et al. proposed a com-plementary ensemble empirical mode decomposition andpermutation entropy based method for gearbox fault diag-nosis. The results showed that their proposed method isable to improve prediction accuracies of support vectormachines.We sincerely hope that this special issue could provideuseful technologies and information for researchers andengineers in the research community of fault diagnosis andprognosis.Shock and Vibration 3AcknowledgmentsWewould like to thank all authors for submitting their worksto this special issue. We also would like to thank all reviewersand editors for their valuable comments on all manuscripts.Dong WangChuan LiAchmad WidodoPavan Kumar KankarWahyu Caesarendra

Fault Diagnosis and Prognosis of Critical Components

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Fault Diagnosis and Prognosis of Critical Components

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