FAULT DETECTION IN HEAVY DUTY WHEELS BY ADVANCED VIBRATION PROCESSING TECHNIQUES AND NUMERICAL MODELLING

The research work reported in this thesis aims at developing a methodology and a procedure for the condition monitoring and diagnostics of heavy-duty wheels based on vibration measurements at the end of the production line. The early detection of manufacturing anomalies is necessary to sensibly reduce the time/money lost due to possible problems that can rise up during the operating phases. Heavy-duty wheels are used in applications as automatic vehicles and motor trucks and are mainly composed of a polyurethane tread glued to a cast iron hub. The adhesive application between tread and hub is the most critical assembly phase, since it is completely made by an operator and a contamination of the link area between polyurethane and cast iron may happen. Furthermore the presence of rust on the hub surface can contribute to worsen the adherence interface and to reduce the operating life. As the author is aware, studies by other researchers concerning the fault detection in heavy-duty wheels are not present in literature. In order to develop a detection procedure, several wheels with different types of faults have been manufactured “ad hoc” with anomalies similar to real ones. Such anomalies consist of incorrectly adherence zones between tread and hub as well as localized or distributed rust on the hub surface. Numerous experimental tests have been carried out in order to identify the vibration effects of these defects as a function of fault type and dimensions. The thesis concerns the detection and diagnostic capability of different vibration processing techniques using well-suited indicators and determining pass/fail decision thresholds through the Tukey’s non-statistical method. Contemporary, an accurate dynamic analysis of this mechanical system has been conducted - both experimentally through modal analysis techniques and numerically through finite element method - in order to establish the influence of the dynamic properties of the system components (namely heavy-duty wheel, support, frame of the test set up) on the measured vibratory signal. Based on this dynamic characterization, a multibody model of the system has been developed: the heavy-duty wheel is considered as rigid and the yielding part is focused in the contact patch between wheel and drum. A non-linear elastic contact algorithm is adopted, based on stiffness properties previously extracted from static tests conducted on both material specimens and complete components. The model makes it possible to reproduce the vibration effects of the defects and to simulate signal modifications due to different component materials and design. as Synchronous Average and Cyclostationarity Analysis

PHY-layer Security in Cognitive Radio Networks through Learning Deep Generative Models: an AI-based approach

PhD ThesisRecently, Cognitive Radio (CR) has been intended as an intelligent radio endowed with cognition which can be developed by implementing Artificial Intelligence (AI) techniques. Specifically, data-driven Self-Awareness (SA) functionalities, such as detection of spectrum abnormalities, can be effectively implemented as shown by the proposed research. One important application is PHY-layer security since it is essential to establish secure wireless communications against external jamming attacks. In this framework, signals are non-stationary and features from such kind of dynamic spectrum, with multiple high sampling rate signals, are then extracted through the Stockwell Transform (ST) with dual-resolution which has been proposed and validated in this work as part of spectrum sensing techniques. Afterwards, analysis of the state-of-the-art about learning dynamic models from observed features describes theoretical aspects of Machine Learning (ML). In particular, following the recent advances of ML, learning deep generative models with several layers of non-linear processing has been selected as AI method for the proposed spectrum abnormality detection in CR for a brain-inspired, data-driven SA. In the proposed approach, the features extracted from the ST representation of the wideband spectrum are organized in a high-dimensional generalized state vector and, then, a generative model is learned and employed to detect any deviation from normal situations in the analysed spectrum (abnormal signals or behaviours). Specifically, conditional GAN (C-GAN), auxiliary classifier GAN (AC-GAN), and deep VAE have been considered as deep generative models. A dataset of a dynamic spectrum with multi-OFDM signals has been generated by using the National Instruments mm-Wave Transceiver which operates at 28 GHz (central carrier frequency) with 800 MHz frequency range. Training of the deep generative model is performed on the generalized state vector representing the mmWave spectrum with normality pattern without any malicious activity. Testing is based on new and independent data samples corresponding to abnormality pattern where the moving signal follows a different behaviour which has not been observed during training. An abnormality indicator is measured and used for the binary classification (normality hypothesis otherwise abnormality hypothesis), while the performance of the generative models is evaluated and compared through ROC curves and accuracy metrics

Wind turbine drivetrains:State-of-the-art technologies and future development trends

This paper presents the state-of-the-art technologies and development trends of wind turbine drivetrains – the system that converts kinetic energy of the wind to electrical energy – in different stages of their life cycle: design, manufacturing, installation, operation, lifetime extension, decommissioning and recycling. Offshore development and digitalization are also a focal point in this study. Drivetrain in this context includes the whole power conversion system: main bearing, shafts, gearbox, generator and power converter. The main aim of this article is to review the drivetrain technology development as well as to identify future challenges and research gaps. The main challenges in drivetrain research identified in this paper include drivetrain dynamic responses in large or floating turbines, aerodynamic and farm control effects, use of rare-earth material in generators, improving reliability through prognostics, and use of advances in digitalization. These challenges illustrate the multidisciplinary aspect of wind turbine drivetrains, which emphasizes the need for more interdisciplinary research and collaboration

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

On Random Sampling for Compliance Monitoring in Opportunistic Spectrum Access Networks

In the expanding spectrum marketplace, there has been a long term evolution towards more market€“oriented mechanisms, such as Opportunistic Spectrum Access (OSA), enabled through Cognitive Radio (CR) technology. However, the potential of CR technologies to revolutionize wireless communications, also introduces challenges based upon the potentially non€“deterministic CR behaviour in the Electrospace. While establishing and enforcing compliance to spectrum etiquette rules are essential to realization of successful OSA networks in the future, there has only been recent increased research activity into enforcement. This dissertation presents novel work on the spectrum monitoring aspect, which is crucial to effective enforcement of OSA. An overview of the challenges faced by current compliance monitoring methods is first presented. A framework is then proposed for the use of random spectral sampling techniques to reduce data collection complexity in wideband sensing scenarios. This approach is recommended as an alternative to Compressed Sensing (CS) techniques for wideband spectral occupancy estimation, which may be difficult to utilize in many practical congested scenarios where compliance monitoring is required. Next, a low€“cost computational approach to online randomized temporal sensing deployment is presented for characterization of temporal spectrum occupancy in cognitive radio scenarios. The random sensing approach is demonstrated and its performance is compared to CS€“based approach for occupancy estimation. A novel frame€“based sampling inversion technique is then presented for cases when it is necessary to track the temporal behaviour of individual CRs or CR networks. Parameters from randomly sampled Physical Layer Convergence Protocol (PLCP) data frames are used to reconstruct occupancy statistics, taking account of missed frames due to sampling design, sensor limitations and frame errors. Finally, investigations into the use of distributed and mobile spectrum sensing to collect spatial diversity to improve the above techniques are presented, for several common monitoring tasks in spectrum enforcement. Specifically, focus is upon techniques for achieving consensus in dynamic topologies such as in mobile sensing scenarios