EditorialFault Identification, Diagnosis, and Prognostics Based onComplex Signal Analysis

Minvydas Ragulskis ,1 Chen Lu ,2 Maosen Cao ,3

Gangbing Song ,4 and Rafal Burdzik 5

1Center for Nonlinear Systems, Kaunas University of Technology, Kaunas, Lithuania2School of Reliability and Systems Engineering, Beihang University, Beijing, China3Institute of Vibration and Dynamics, Hohai University, Nanjing, China4Department of Mechanical Engineering, University of Houston, Houston, USA5Faculty of Transport, Silesian University of Technology, Katowice, Poland

Correspondence should be addressed to Minvydas Ragulskis; minvydas.ragulskis@ktu.lt

Received 21 October 2018; Accepted 6 December 2018; Published 17 December 2018

Copyright © 2018 Minvydas Ragulskis et al. �is is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

�is special issue titled “Fault Identification, Diagnosis, andPrognostics Based on Complex Signal Analysis” is a trulyinterdisciplinary issue. Manuscripts published in this specialissue do represent such diverse fields as mechanical engi-neering, aviation engineering and technology, electric andelectronic engineering, statistics, and so on. �e geographyof authors spans over three continents, Asia, Europe, andAmerica. A total of 18 manuscripts were published in thisissue.

�is special issue gathered several studies that focuson fault detection and diagnosis of mechanical products,such as bolts, rolling bearings, gears, and hydraulic servosystems. For example, G. Wu et al. proposed a modifiedtime reversal method and successfully realized bolt looseningdetection and localization in simulated thermal protectionsystem panels. Two studies aim to reduce the backgroundnoise inmonitoring signal of rolling bearings, thus improvingthe effectiveness of fault diagnosis. R. Yuan et al. reported anadaptive high-order local projection denoising method anddemonstrated the characteristic frequencies of simulated sig-nals can be well extracted by the proposed method. D. Zhonget al. proposed a novel fault signal denoising scheme basedon improved sparse regularization via convex optimizationto extract the fault feature of rolling bearing. Experimentsshow that the proposed method has a better performance

than traditional methods. In an interesting study, a methodbased on the multiscale chirplet path pursuit and the linearcanonical transform is proposed by X. Shuiqing et al. andapplied to diagnose the gear fault in the variable speedcondition for the first time. �is method can diagnose thegear faults availably. In another study, Y. Ding et al. presenteda fault diagnosis scheme for hydraulic servo system usingcompressed random subspace based ReliefF (CRSR)method.�eproposedCRSRmethod is able to enhance the robustnessof the feature information against interference while selectingthe feature combination with balanced information express-ing ability. Finally, dealing with sliding mode Fault TolerantControl (FTC) problem for nonlinear system, two adaptiveslidingmode FTC schemes for an uncertain nonlinear systemsubject to multiplicative and process faults were presented byA. Ben Brahim et al. By solving a single-step multiobjectiveLMI optimization problem, the observer and controller gainsare obtained, offering a solution to stabilize the closed-loopnonlinear system despite the occurrence of real fault effects.

�is special issue also attracted several studies fromthe field of aviation engineering and technology. H. Liuet al., for example, proposed a fault diagnosis method forelectromechanical actuators (EMAs) based on variationalmode decomposition (VMD) multifractal detrended fluctu-ation analysis (MFDFA) and probabilistic neural network

HindawiComplexityVolume 2018, Article ID 4020729, 2 pageshttps://doi.org/10.1155/2018/4020729

2 Complexity

(PNN).�ismethod is demonstrated to achieve effective faultdiagnosis for EMAs under different working conditions. Inanother elegant study, a deep learning approach is proposedby J. Ma et al. to predict the RUL of an aircra� engine basedon a stacked sparse autoencoder and logistic regression. �eproposed method also has significance for enhancing thesafety of aircra� engines and prognosticating and managingthe health of aircra� engines to reduce the cost of mainte-nance. In addition, as an interdisciplinary method, Y. Chenget al. introduced visual cognition theory into the field oflithium-ion battery capacity estimation. Inspired by multi-channel andmanifold sensing characteristics of human visualsystem, battery capacity is effectively estimated based on non-subsampled contourlet transform and Laplacian eigenmapmanifold learning method.

Two studies in this special issue are related to electricand electronic engineering. D. Song et al. reported a generalfault detection and diagnosis scheme based on observersand residual error analysis for superheterodyne receivers.�e proposed method may provide a promising approachnot only to the superheterodyne receiver but also to morecomplex signal receiving systems in which the transferfunctions are difficult to obtain. Moreover, P. Orlov et al.presented a unified description of a new approach for con-tactless detection, identification, and diagnostics of electricalconnections and described an idea and principles of usingmodal probing for these tasks.

Another two studies in this special issue investigatedtechniques in statistics. H. Sun reported an investigationof stretched exponential function in quantifying long-termmemory of extreme events based on artificial data followingLevy stable distribution. It turns out that the stretchedexponential distribution provides a reliable way to estimatethe scaling behavior of extreme event intervals. In addition,considering the statistic numerical characteristics are o�enrequired in the probability-based damage identification andsafety assessment of functionally graded material (FGM)structures, Y. Xu et al. presented a stochastic model updating-based inverse computational method to identify the second-order statistics (means and variances) of material propertiesas well as distribution of constituents for damaged FGMstructures with material uncertainties.

�is special issue also gathers several interdisciplinarystudies from other fields. For example, an effective approachis introduced by H. Wei et al. to predict the magnitudeof reservoir-triggered earthquake (RTE), based on supportvector machines (SVM) and fuzzy support vector machines(FSVM) methods. Both the SVM and FSVM models arefound to be effective in the prediction of the magnitude ofRTEwith high accuracy. Z.Wang et al. reviewed conventionalmethods for separating the suspended solids from lubricatingoils and presented ultrasonic separation methods for particleseparation, which is environment-friendly and has a highefficiency. N. F. Alkayem et al. presented a hybrid elitist-guided search combining a multiobjective particle swarmoptimization, Levy flights, and the technique for the orderof preference by similarity to ideal solution investigated. �eproposedmethod shows good performance even under noisyconditions and in the case of incomplete mode shapes. C. Liu

reported a damage detection method for refractory based onprinciple component analysis and Gaussian mixture model.Two types of damage were utilized to verify the effectivenessof the proposedmethod. Finally, C. Li proposed an algorithmwhich is built to obtain the rigorous solution approachingupper and lower bound values simultaneously, satisfying thestatic boundary and the kinematical boundary based on theslip line field, while stress discontinuity line and velocitydiscontinuity line are key points.

All in all, this special issue aims to aggregate the latestresearch efforts contributing to theoretical, methodological,and technological advances in detecting anomalies, forecast-ing potential degradation, and classifying faults from com-plex environments and signals. �ese methods are expectedto address the existing challenges for a real-world PHMsystem.

Conflicts of Interest

�e editors declare that they have no conflicts of interestregarding the publication of this special issue.

Minvydas RagulskisChen Lu

Maosen CaoGangbing SongRafal Burdzik

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