A proposal of a technique for correlating defect dimensions to vibration amplitude in bearing monitoring

The capability of early stage detection of a defect is gaining more and more importance because it can help the maintenance process, the cost reduction and the reliability of the systems. The increment of vibration amplitude is a well-known method for evaluating the damage of a component, but it is sometimes difficult to understand the exact level of damage. In other words, the amplitude of vibration cannot be directly connected to the dimension of the defect. In the present paper, based on a non-Hertzian contact algorithm, the spectrum of the pressure distribution in the contact surface between the race and the rolling element is evaluated. Such spectrum is then compared with the acquired spectrum of a vibration response of a defected bearing. The bearing vibration pattern was previously analyzed with monitoring techniques to extract all the damage information. The correlation between the spectrum of the pressure distribution in the defected contact surface and the analyzed spectrum of the damaged bearing highlights a strict relationship. By using that analysis, a precise correlation between defect aspect and dimension and vibration level can be addressed to estimate the level of damaging

Multi-Component Machine Monitoring and Fault Diagnosis Using Bling Source Separation and Advanced Vibration Analysis

RÉSUMÉ Dans le diagnostic des machines rotatives, l'analyse des vibrations est largement connue pour être l'une des techniques les plus efficaces. Les vibrations sont une caractéristique inhérente des machines rotatives et les différentes composantes de ce type de machines telles que les arbres, les roulements et les engrenages produisent de l'énergie vibratoire avec différentes caractéristiques. N'importe quelle détérioration de l'état de telles composantes peut affecter leurs propriétés vibratoires et se manifester par conséquent dans la signature de vibration. Ceci est valable pour le diagnostic des défauts en analysant la signature des vibrations du système. Pour faire un excellent diagnostic des défauts utilisant les techniques d'analyse de vibration, il faut que les signaux acquis atteignent un certain niveau de propretés de telle sorte que le plus petit changement des attributs du signal dû à un défaut imminent dans n'importe quelle composante peut être détecté. Néanmoins, ce n’est pas le cas dans la pratique, car les signaux de vibration sont souvent encombrés par le bruit. Dans le cas des machines complexes à plusieurs éléments ce problème est aggravé encore plus car les différentes composantes produisent de l'énergie vibratoire. En effet à toutes les fois qu'il est nécessaire de surveiller n'importe quelle composante d'intérêt, les vibrations produites par les autres affectent le signal. Parmi les moyens pour contourner ce problème est de placer des capteurs aussi proches que possible des composantes données. Mais, certaines restrictions telles que la complexité, la politique de garantie du fabricant et l'inaccessibilité empêchent de tel emplacement, de ce fait, dans la majorité des cas les capteurs sont placés sur la surface extérieure de la structure. Par conséquent les capteurs collectent non seulement des signaux de vibrations d'une composante spécifique mais des autres composantes aussi, de ce fait, les signaux de chaque capteur est en effet, la combinaison de l'énergie vibratoire des différentes composantes, plus le bruit. La dissipation de l'énergie des vibrations complique la situation encore plus. Pour surpasser ce problème, principalement deux approches peuvent être adoptées. La première consiste à considérer ces cas comme un problème de séparation aveugle de sources et en tirer profit des méthodes statistiques et mathématiques développées à cet effet, surtout l'analyse en composantes indépendante (ACI), qui sépare les signaux provenant de sources différentes.----------ABSTRACT In diagnosis of rotating machinery, vibration analysis is widely known to be one of the most effective techniques. This stems from the fact that oscillation is an inherent characteristic of rotating machines and different components of these types of machinery such as shafts, bearings and gears produce vibration energy with different characteristics. Any deterioration in the condition of such components can affect their vibratory attributes and manifest itself in the vibration signature. This allows diagnosis of machine faults by analyzing the vibration signature of the system. For improved and authentic fault diagnosis using vibration analysis techniques it is necessary that the acquired vibration signals be ‘clean’ enough that small changes in signal attributes due to an impending fault in any component can be detected. unfortunately, this is not the case in common practice and vibration signals received from operating machinery are almost always cluttered with noise. In complex multi-component machines this problem is aggravated because vibration energy is generated by each individual component. Whenever it is necessary to monitor a specific component, vibration produced by other components affect the signal. One solution for this problem is to mount the vibration sensors as close as possible to the targeted components. Some restrictions such as complexity, manufacturer’s warranty policy and inaccessibility constrain this approach and in a majority of cases sensors are placed on the innermost surface possible (i.e.,casing) of the structure. As a consequence, the sensors collect vibration signals which are not uniquely generated from the targeted component, but also include contributions from many other components. The vibration signals collected by each sensor are in effect the combination of vibration energy produced by different components in addition to the noise. Dissipation of vibration energy through transmission path complicates the situation even further. To tackle this problem, one of two alternative approaches can be adopted. One approach is to regard this case as a blind source separation (cocktail party) problem and take advantage of statistical and mathematical methods developed for this purpose, primarily independent component analysis (ICA), to separate signals coming from different sources

Development of an advanced artificial intelligent reliability analysis tool to enhance ship operations and maintenance activities

No Abstract availableNo Abstract availabl

Unsupervised Methods for Condition-Based Maintenance in Non-Stationary Operating Conditions

Maintenance and operation of modern dynamic engineering systems requires the use of robust maintenance strategies that are reliable under uncertainty. One such strategy is condition-based maintenance (CBM), in which maintenance actions are determined based on the current health of the system. The CBM framework integrates fault detection and forecasting in the form of degradation modeling to provide real-time reliability, as well as valuable insight towards the future health of the system. Coupled with a modern information platform such as Internet-of-Things (IoT), CBM can deliver these critical functionalities at scale. The increasingly complex design and operation of engineering systems has introduced novel problems to CBM. Characteristics of these systems - such as the unavailability of historical data, or highly dynamic operating behaviour - has rendered many existing solutions infeasible. These problems have motivated the development of new and self-sufficient - or in other words - unsupervised CBM solutions. The issue, however, is that many of the necessary methods required by such frameworks have yet to be proposed within the literature. Key gaps pertaining to the lack of suitable unsupervised approaches for the pre-processing of non-stationary vibration signals, parameter estimation for fault detection, and degradation threshold estimation, need to be addressed in order to achieve an effective implementation. The main objective of this thesis is to propose set of three novel approaches to address each of the aforementioned knowledge gaps. A non-parametric pre-processing and spectral analysis approach, termed spectral mean shift clustering (S-MSC) - which applies mean shift clustering (MSC) to the short time Fourier transform (STFT) power spectrum for simultaneous de-noising and extraction of time-varying harmonic components - is proposed for the autonomous analysis of non-stationary vibration signals. A second pre-processing approach, termed Gaussian mixture model operating state decomposition (GMM-OSD) - which uses GMMs to cluster multi-modal vibration signals by their respective, unknown operating states - is proposed to address multi-modal non-stationarity. Applied in conjunction with S-MSC, these two approaches form a robust and unsupervised pre-processing framework tailored to the types of signals found in modern engineering systems. The final approach proposed in this thesis is a degradation detection and fault prediction framework, termed the Bayesian one class support vector machine (B-OCSVM), which tackles the key knowledge gaps pertaining to unsupervised parameter and degradation threshold estimation by re-framing the traditional fault detection and degradation modeling problem as a degradation detection and fault prediction problem. Validation of the three aforementioned approaches is performed across a wide range of machinery vibration data sets and applications, including data obtained from two full-scale field pilots located at Toronto Pearson International Airport. The first of which is located on the gearbox of the LINK Automated People Mover (APM) train at Toronto Pearson International Airport; and, the second which is located on a subset of passenger boarding tunnel pre-conditioned air units (PCA) in Terminal 1 of Pearson airport. Results from validation found that the proposed pre-processing approaches and combined pre-processing framework provides a robust and computationally efficient and robust methodology for the analysis of non-stationary vibration signals in unsupervised CBM. Validation of the B-OCSVM framework showed that the proposed parameter estimation approaches enables the earlier detection of the degradation process compared to existing approaches, and the proposed degradation threshold provides a reasonable estimate of the fault manifestation point. Holistically, the approaches proposed in thesis provide a crucial step forward towards the effective implementation of unsupervised CBM in complex, modern engineering systems

Vibration Monitoring: Gearbox identification and faults detection

L'abstract è presente nell'allegato / the abstract is in the attachmen

Residual life prediction and degradation-based control of multi-component systems

The condition monitoring of multi-component systems utilizes multiple sensors to capture the functional condition of the systems and allows the sensor information to be used to reason about the health information of the systems or components. Chapter 3 considers the situation when sensor signals capture unknown mixtures of component signals and proposes a two-stage vibration-based methodology to identify component degradation signals from mixed sensor signals in order to predict component-level residual lives. Specifically, we are interested in modeling the degradation of systems that consist of two or more identical components operating under similar conditions. Chapter 4 focuses on the interactive relationship between tool wear (component degradation) and product quality degradation (sensor information) that widely exists in multistage manufacturing processes and proposes a high-dimensional stochastic differential equation model to capture the interaction relationship. Then, real-time quality measurements are incorporated to online predict the residual life of the system. Chapter 5 develops a strategy of dynamic workload adjustment for parallel multi-component systems in order to control the degradation processes and failure times of individual components, for the purpose of preventing the overlap of component failures. This chapter opens a new research direction that focuses on the active control of degradation rather than only the modeling part.Ph.D

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

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