Damage identification based on response-only measurements using cepstrum analysis and artificial neural networks

This article presents a response-only structural health monitoring technique that utilises cepstrum analysis and artificial neural networks for the identification of damage in civil engineering structures. The method begins by applying cepstrum-based operational modal analysis, which separates source and transmission path effects to determine the structure's frequency response functions from response measurements only. Principal component analysis is applied to the obtained frequency response functions to reduce the data size, and structural damage is then detected using a two-stage ensemble of artificial neural networks. The proposed method is verified both experimentally and numerically using a laboratory two-storey framed structure and a finite element representation, both subjected to a single excitation. The laboratory structure is tested on a large-scale shake table generating ambient loading of Gaussian distribution. In the numerical investigation, the same input is applied to the finite model, but the obtained responses are polluted with different levels of white Gaussian noise to better replicate real-life conditions. The damage is simulated in the experimental and numerical investigations by changing the condition of individual joint elements from fixed to pinned. In total, four single joint changes are investigated. The results of the investigation show that the proposed method is effective in identifying joint damage in a multi-storey structure based on response-only measurements in the presence of a single input. Because the technique does not require a precise knowledge of the excitation, it has the potential for use in online structural health monitoring. Recommendations are given as to how the method could be applied to the more general multiple-input case. © The Author(s) 2014

Fault Detection in Rotating Machinery: Vibration analysis and numerical modeling

This thesis investigates vibration based machine condition monitoring and consists of two parts: bearing fault diagnosis and planetary gearbox modeling. In the first part, a new rolling element bearing diagnosis technique is introduced. Envelope analysis is one of the most advantageous methods for rolling element bearing diagnostics but finding the suitable frequency band for demodulation has been a substantial challenge for a long time. Introduction of the Spectral Kurtosis (SK) and Kurtogram mostly solved this problem but in situations where signal to noise ratio is very low or in presence of non-Gaussian noise these methods will fail. This major drawback may noticeably decrease their effectiveness and goal of this thesis is to overcome this problem. Vibration signals from rolling element bearings exhibit high levels of 2nd order cyclostationarity, especially in the presence of localized faults. A second-order cyclostationary signal is one whose autocovariance function is a periodic function of time: the proposed method, named Autogram by the authors, takes advantage of this property to enhance the conventional Kurtogram. The method computes the kurtosis of the unbiased autocorrelation (AC) of the squared envelope of the demodulated and undecimated signal, rather than the kurtosis of the filtered time signal. Moreover, to take advantage of unique features of the lower and upper portions of the AC, two modified forms of kurtosis are introduced and the resulting colormaps are called Upper and Lower Autogram. In addition, a new thresholding method is also proposed to enhance the quality of the frequency spectrum analysis. Finally, the proposed method is tested on experimental data and compared with literature results so to assess its performances in rolling element bearing diagnostics. Moreover, a second novel method for diagnosis of rolling element bearings is developed. This approach is a generalized version of the cepstrum pre-whitening (CPW) which is a simple and effective technique for bearing diagnosis. The superior performance of the proposed method has been shown on two real case data. For the first case, the method successfully extracts bearing characteristic frequencies related to two defected bearings from the acquired signal. Moreover, the defect frequency was highlighted in case two, even in presence of strong electromagnetic interference (EMI). The second part presents a newly developed lumped parameter model (LPM) of a planetary gear. Planets bearings of planetary gear sets exhibit high rate of failure; detection of these faults which may result in catastrophic breakdowns have always been challenging. Another objective of this thesis is to investigate the planetary gears vibration properties in healthy and faulty conditions. To seek this goal a previously proposed lumped parameter model (LPM) of planetary gear trains is integrated with a more comprehensive bearing model. This modified LPM includes time varying gear mesh and bearing stiffness and also nonlinear bearing stiffness due to the assumption of Hertzian contact between the rollers/balls and races. The proposed model is completely general and accepts any inner/outer race bearing defect location and profile in addition to its original capacity of modelling cracks and spalls of gears; therefore, various combinations of gears and bearing defects are also applicable. The model is exploited to attain the dynamic response of the system in order to identify and analyze localized faults signatures for inner and outer races as well as rolling elements of planets bearings. Moreover, bearing defect frequencies of inner/outer race and ball/roller and also their sidebands are discussed thoroughly. Finally, frequency response of the system for different sizes of planets bearing faults are compared and statistical diagnostic algorithms are tested to investigate faults presence and growth

PHM survey: implementation of signal processing methods for monitoring bearings and gearboxes

The reliability and safety of industrial equipments are one of the main objectives of companies to remain competitive in sectors that are more and more exigent in terms of cost and security. Thus, an unexpected shutdown can lead to physical injury as well as economic consequences. This paper aims to show the emergence of the Prognostics and Health Management (PHM) concept in the industry and to describe how it comes to complement the different maintenance strategies. It describes the benefits to be expected by the implementation of signal processing, diagnostic and prognostic methods in health-monitoring. More specifically, this paper provides a state of the art of existing signal processing techniques that can be used in the PHM strategy. This paper allows showing the diversity of possible techniques and choosing among them the one that will define a framework for industrials to monitor sensitive components like bearings and gearboxes

Cyclosparsity: A New Concept for Sparse Deconvolution

Periodic random impulse signals are appropriate tools for several situations of interest and are a natural way for modeling highly localized events occuring randomly at given times. Nevertheless, the impulses are generally hidden and swallowed up in noise because of unwanted convolution. Thus, the resulting signal is not legible and may lead to erroneaous analysis, and hence, the need of deconvolution to restore the random periodic impulses. The main purpose of this study is to introduce the concept of cyclic sparsity or cyclosparsity in deconvolution framework for signals that are jointly sparse and cyclostationary like periodic random impulses. Indeed, all related works in this area exploit only one property, either sparsity or cyclostationarity and never both properties together. Although, the key feature of the cyclosparsity concept is that it gathers both properties to better characterize this kind of signals. We show that deconvolution based on cyclic sparsity hypothesis increases the performances and reduces significantly the computation cost as well. Finally, we use computer simulations to investigate the behavior in deconvolution framework of the algorithms Matching Pursuit (MP) [13], Orthogonal Matching Pursuit (OMP) [14], Orthogonal Least Square (OLS) [15], Single Best Replacement (SBR), [19, 20, 21] and the proposed extensions to cyclic sparsity context: Cyclo-MP, Cyclo-OMP, Cyclo-OLS and Cyclo-SBR

Surveillance vibratoire des machines tournantes en régime non-stationnaires

In the last decades, vibration-based condition monitoring of rotating machine has gained special interest providing an efficient aid for maintenance in the industry. Nowadays, many efficient techniques are well-established, rooted on powerful tools offered in particular by the theory of cyclostationary processes. However, all these techniques rely on the assump-tion of constant— or possibly fluctuating but stationary— operating regime (i.e. speed and/or load). Unfortunately, most monitored machines used in the industry operate under nonstationary regimes in order to fulfill the task for which they have been designed. In this case, these techniques fail in analyzing the produced vibration signals. This issue, therefore, has occupied the scientific committee in the last decade and some sophisticated signal processing techniques have been conceived to deal with regime variability. But these works remain limited, dispersed and generally not supported by theoretical frameworks. The principal goal of this thesis is to partially fill in this gap on the basis of a theoretical formalization of the subject and a systematic development of new dedicated signal processing tools. In this work, the nonstationarity of the regime is confined to that of the speed— i.e. variable speed and constant load, assumed to be known a priori. In order to reach this goal, the adopted methodology consists in extending the cyclostationary framework together with its dedicated tools. We have elaborated this strategy by distinguishing two types of signatures. The first type includes deterministic waveforms known as first-order cyclostationary. The proposed solution consists in generalizing the first-order cyclostationary class to the more general first-order cyclo-non-stationary class which enfolds speed-varying deterministic signals. The second type includes random periodically-correlated waveforms known as second-order cyclostationary. Three different but complementary visions have been proposed to deal with the changes induced by the nonstationarity of the operating speed. The first one adopts an angle\time cyclostationary approach, the second one adopts an envelope-based solution and the third one adopts a (second-order) cyclo-non-stationary approach. Many tools have been conceived whose performances have been successfully tested on simulated and real vibration signals.Dans les dernières décennies, la surveillance vibratoire des machines tournantes a acquis un intérêt particulier fournissant une aide efficace pour la maintenance dans l'industrie. Aujourd'hui, de nombreuses techniques efficaces sont bien établies, ancrées sur des outils puissants offerts notamment par la théorie des processus cyclostationnaires. Cependant, toutes ces techniques reposent sur l'hypothèse d’un régime de fonctionnement (c.à.d. vitesse et/ou charge) constant ou éventuellement fluctuant d’une façon stationnaire. Malheureusement, la plupart des machines surveillées dans l'industrie opèrent sous des régimes non stationnaires afin de remplir les tâches pour lesquelles elles ont été conçues. Dans ce cas, ces techniques ne parviennent pas à analyser les signaux vibratoires produits. Ce problème a occupé la communauté scientifique dans la dernière décennie et des techniques sophistiquées de traitement du signal ont été conçues pour faire face à la variabilité du régime. Mais ces tentatives restent limitées, dispersées et généralement peu soutenues par un cadre théorique. Le principal objectif de cette thèse est de combler partiellement cette lacune sur la base d'une formalisation théorique du sujet et d’un développement systématique de nouveaux outils de traitement du signal. Dans ce travail, la non-stationnarité du régime est limitée à celle de la vitesse— c.à.d. vitesse variable et charge constante— supposée connue a priori. Afin d'atteindre cet objectif, la méthodologie adoptée consiste à étendre le cadre cyclostationnaire avec ses outils dédiés. Nous avons élaboré cette stratégie en distinguant deux types de signatures. Le premier type comprend des signaux déterministes connus comme cyclostationnaires au premier ordre. La solution proposée consiste à généraliser la classe cyclostationnaire au premier ordre à la classe cyclo-non-stationnaire au premier ordre qui comprend des signaux déterministes en vitesse variable. Le second type comprend des signaux aléatoires périodiquement corrélés connus comme cyclostationnaires au deuxième ordre. Trois visions différentes mais complémentaires ont été proposées pour traiter les variations induites par la non-stationnarité de la vitesse de fonctionnement. La première adopte une approche cyclostationnaire angle\temps, la seconde une solution basée sur l'enveloppe et la troisième une approche cyclo-non-stationnaire (au second ordre). De nombreux outils ont été conçus dont les performances ont été testées avec succès sur des signaux vibratoires réels et simulés

An informative frequency band identification framework for gearbox fault diagnosis under time-varying operating conditions

Please read abstract in the article.http://www.elsevier.com/locate/ymssphj2022Mechanical and Aeronautical Engineerin

Advanced techniques for aircraft bearing diagnostics

The task is the creation of a method able to diagnose and monitor bearings healthy, mainly in case of varying external conditions. The ability of the technique is verified through data acquisition on a laboratory test rig, where various operating conditions could be checked (load, speed, temperature). Signal processing techniques and data mining techniques are applied to analyse the data