Vibration-based Fault Diagnostics in Wind Turbine Gearboxes Using Machine Learning

A significantly increased production of wind energy offers a path to achieve the goals of green energy policies in the United States and other countries. However, failures in wind turbines and specifically their gearboxes are higher due to their operation in unpredictable wind conditions that result in downtime and losses. Early detection of faults in wind turbines will greatly increase their reliability and commercial feasibility. Recently, data-driven fault diagnosis techniques based on deep learning have gained significant attention due to their powerful feature learning capabilities. Nonetheless, diagnosing faults in wind turbines operating under varying conditions poses a major challenge. Signal components unrelated to faults and high levels of noise obscure the signature generated by early-stage damage. To address this issue, we propose an innovative fault diagnosis framework that utilizes deep learning and leverages cyclostationary analysis of sensor data. By generating cyclic spectral coherence maps from the sensor data, we can emphasize fault-related signatures. These 2D color map representations are then used to train convolutional neural networks capable of detecting even minor faults and early-stage damages. The proposed method is evaluated using test data obtained from multibody dynamic simulations conducted under various operating conditions. The benchmark test cases, inspired by an NREL study, are successfully detected using our approach. To further enhance the accuracy of the model, subsequent studies employ Convolutional Neural Networks with Local Interpretable Model-Agnostic Explanations (LIME). This approach aids in interpreting classifier predictions and developing an interpretable classifier by focusing on a subset range of cyclic spectral coherence maps that carry the unique fault signatures. This improvement contributes to better accuracy, especially in scenarios involving multiple faults in the gearbox that need to be identified. Moreover, to address the challenge of applying this framework in practical settings, where standard deep learning techniques tend to provide inaccurate predictions for unseen faults or unusual operating conditions, we investigate fault diagnostics using a Bayesian convolutional neural network. This approach incorporates uncertainty bounds into prediction results, reducing overconfident misclassifications. The results demonstrate the effectiveness of the Bayesian approach in fault diagnosis, offering valuable implications for condition monitoring in other rotating machinery applications

CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS

The book collects the short papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS). The meeting has been organized by the Department of Statistics, Computer Science and Applications of the University of Florence, under the auspices of the Italian Statistical Society and the International Federation of Classification Societies (IFCS). CLADAG is a member of the IFCS, a federation of national, regional, and linguistically-based classification societies. It is a non-profit, non-political scientific organization, whose aims are to further classification research

From phenomenological modelling of anomalous diffusion through continuous-time random walks and fractional calculus to correlation analysis of complex systems

This document contains more than one topic, but they are all connected in ei- ther physical analogy, analytic/numerical resemblance or because one is a building block of another. The topics are anomalous diffusion, modelling of stylised facts based on an empirical random walker diffusion model and null-hypothesis tests in time series data-analysis reusing the same diffusion model. Inbetween these topics are interrupted by an introduction of new methods for fast production of random numbers and matrices of certain types. This interruption constitutes the entire chapter on random numbers that is purely algorithmic and was inspired by the need of fast random numbers of special types. The sequence of chapters is chrono- logically meaningful in the sense that fast random numbers are needed in the first topic dealing with continuous-time random walks (CTRWs) and their connection to fractional diffusion. The contents of the last four chapters were indeed produced in this sequence, but with some temporal overlap. While the fast Monte Carlo solution of the time and space fractional diffusion equation is a nice application that sped-up hugely with our new method we were also interested in CTRWs as a model for certain stylised facts. Without knowing economists [80] reinvented what physicists had subconsciously used for decades already. It is the so called stylised fact for which another word can be empirical truth. A simple example: The diffusion equation gives a probability at a certain time to find a certain diffusive particle in some position or indicates concentration of a dye. It is debatable if probability is physical reality. Most importantly, it does not describe the physical system completely. Instead, the equation describes only a certain expectation value of interest, where it does not matter if it is of grains, prices or people which diffuse away. Reality is coded and “averaged” in the diffusion constant. Interpreting a CTRW as an abstract microscopic particle motion model it can solve the time and space fractional diffusion equation. This type of diffusion equation mimics some types of anomalous diffusion, a name usually given to effects that cannot be explained by classic stochastic models. In particular not by the classic diffusion equation. It was recognised only recently, ca. in the mid 1990s, that the random walk model used here is the abstract particle based counterpart for the macroscopic time- and space-fractional diffusion equation, just like the “classic” random walk with regular jumps ±∆x solves the classic diffusion equation. Both equations can be solved in a Monte Carlo fashion with many realisations of walks. Interpreting the CTRW as a time series model it can serve as a possible null- hypothesis scenario in applications with measurements that behave similarly. It may be necessary to simulate many null-hypothesis realisations of the system to give a (probabilistic) answer to what the “outcome” is under the assumption that the particles, stocks, etc. are not correlated. Another topic is (random) correlation matrices. These are partly built on the previously introduced continuous-time random walks and are important in null- hypothesis testing, data analysis and filtering. The main ob jects encountered in dealing with these matrices are eigenvalues and eigenvectors. The latter are car- ried over to the following topic of mode analysis and application in clustering. The presented properties of correlation matrices of correlated measurements seem to be wasted in contemporary methods of clustering with (dis-)similarity measures from time series. Most applications of spectral clustering ignores information and is not able to distinguish between certain cases. The suggested procedure is sup- posed to identify and separate out clusters by using additional information coded in the eigenvectors. In addition, random matrix theory can also serve to analyse microarray data for the extraction of functional genetic groups and it also suggests an error model. Finally, the last topic on synchronisation analysis of electroen- cephalogram (EEG) data resurrects the eigenvalues and eigenvectors as well as the mode analysis, but this time of matrices made of synchronisation coefficients of neurological activity

Comparative Analysis of Student Learning: Technical, Methodological and Result Assessing of PISA-OECD and INVALSI-Italian Systems .

PISA is the most extensive international survey promoted by the OECD in the field of education, which measures the skills of fifteen-year-old students from more than 80 participating countries every three years. INVALSI are written tests carried out every year by all Italian students in some key moments of the school cycle, to evaluate the levels of some fundamental skills in Italian, Mathematics and English. Our comparison is made up to 2018, the last year of the PISA-OECD survey, even if INVALSI was carried out for the last edition in 2022. Our analysis focuses attention on the common part of the reference populations, which are the 15-year-old students of the 2nd class of secondary schools of II degree, where both sources give a similar picture of the students

Perspectives on European Earthquake Engineering and Seismology: Volume 2

Geotechnical Engineering & Applied Earth Science

Theory of nonlinear polarization spectroscopy in the frequency domain (NLPF) with applications to photosynthetic antennae

In der vorliegenden Arbeit wird eine einheitliche und allumfassende Theorie der Nicht-linearen Polarisationsspektroskopie in der Frequenzdomäne (NLPF) aufgestellt. Dies Methode basiert auf der in einer isotropen Farbstofflösung durch ein polarisiertes, monochromatisches Laserfeldes (pump) erzeugten Anisotropie, die mittels eines weiteren monochromatischen Laserfeldes (probe), mit einer um 45° gegenüber dem Pumpfeld gedrehten Polarisationsrichtung geprobt wird. Ausgehend von den grundlegenden Gleichungen für den nichtlinearen Respons molekularer Systeme auf elektromagnetische Felder wird das zweidimensional NLPF-spektrum hergeleitet, und zwar sowohl in der niedrigsten Ordnung Störungstheorie als auch unter Verwendung eines selbstkonsistenten Ansatzes für beliebige Pumpfeldstärken. In der niedrigsten Ordnung Störungstheorie können drei in ihrer Frequenzabhängigkeit sich unterscheidende Arten von Ausdrücke explizit angegeben werden. Diese sind drei Areten von Peaks im NLPF-spektrum zuzuordnen: Den T2-peaks, dem T1-peaks und den Zweiphotonen-peaks. Letztere sind unter Normalbedingungen im allgemeinen nicht beobachtbar und wurden daher nicht weiter behandelt. Die in dieser Arbeit erstmals gelungene, allgemeine und einheitliche theoretische Beschreibung der T1- und T2-peaks in NLPF-spektren von Mehrniveausystemen stellt einen Durchbruch hin zu einer allumfassenden Subbandenanalyse mittels NLPF dar. Durch Einbeziehung der teilweise bereits bekannten Auswirkungen homogener und inhomogener Linienverbreiterung und spektraler Diffusion auf NLPF-spektren, sowie deren Verallgemeinerung im Ramen der Theorie nichtmarkowscher Dissipationsprozesse, konnte eine Methodik entwickelt werden, die es erlaubt, NLPF-spektren molekularer und supramolekularer Systeme in Bezug auf das ihnen zugrundeliegende Termschema mit Übergangsfrequenzen und -dipolen, die homogenen und inhomogenen Linienbreiten, sowie dem zugeordneten Energierelaxations- und -transferpfad mitsamt zugehörigen Raten zu analysieren. Die in dieser Arbeit vorgestellte und über frühere rudimentäre Ansätze weit hinausgehende Theorie der NLPF bei starken Pumpfeldern, die auf einem selbstkonsistenten Ansatz für den Fourier-transformierten statistischen Operator beruhen, eröffnet ein komplett neues Feld von Anwendungen der NLPF. Für Zweiniveausysteme konnten die selbstkonsistenten Gleichung vollständig analytisch gelöst werden. Dabei konnten die Querverbindungen zur nichtlinearen Absorption und zum optischen Starkeffekt aufgezeigt werden. Aus der resultierenden Sättigungskurve für das NLPF-signal kann die Sättigungsintensität mit hoher Genauigkeit bestimmt werden. Diese kann unter Heranziehen der aus Analyse des T1-peaks bei niedrigen Intensitäten gewonnen Energierelaxationsrate und der analog aus T2-peakanalyse erhaltenen homogenen Linienbreite zur Bestimmung der Dipolstärke des Übergangs ohne Bestimmung der Farbstoffkonzentration verwendet werden. Dies erweist sich insbesondere bei der Analyse molekularer Aggregation als vorteilhaft. Durch Abbildung auf das gelöste Zweiniveauproblem konnte die Methodik auch auf spezielle Mehrniveausysteme übertragen werden. Eine analytische Lösung für allgemeine Mehrniveausysteme scheiterte jedoch an der komplizierten Orientierungsmittelung über die isotrope Verteilung der Übergangsdipole. Beide oben beschriebenen Methoden, Subbandanalyse bei niedrigen und Bestimmung der Übergangsdipolstärke bei hohen Pumpintensitäten, wurden in der vorliegenden Arbeit zur Untersuchung der Natur der angeregten Zustände in photosynthetischen Antennen von Purpurbakterien und höheren Pflanzen eingesetzt. Für die periphere lichtsammelnde Antenne LH2 des Purpurbakteriums Rhodobacter sphaeroides ergab die T2-peakanalyse der B850-absorptionsbande überraschenderweise zwei Subbanden, die im Absorptionsspektrum selbst bei tiefsten Temperaturen nicht aufzufinden gewesen wären. Eine Erklärung für die in Bezug auf die Oszilatorstärke asymmetrische Aufspaltung der B850-bande konnte allerdings nicht gefunden werden. Für den LH2 des sehr ähnliche Purpurbakterium Rhodospirillium molischianum konnte keine Aufspaltung der B850-bande festgestellt werden. Vielmehr liegt eine überwiegend homogen verbreiterte Bande mit einer homogener Linienbreite (FWHM) von 474±10 cm-1 und einem oberen limit für die inhomogene Linienbreite von 120 cm-1 vor. Daher wurde Rhodospirillium molischianum ausgewählt, um Delokalisation der Anregung im B850-aggregat mittels pumpintensitätsabhängiger NLPF zu untersuchen. Die Frage nach der Delokalisationslänge im B850-aggregat gab und gibt teilweise immer noch Anlass zu hitzigen Debatten. Das Ergebnis einer Ausdehnung der Anregung über 3-4 Bakteriochlorophylle des B850-aggregats der vorliegenden Arbeit unterstützt die aus Exciton-Exciton gewonnen Resultate. Weder eine vollständig lokalisierte noch vollständig delokalisierte Beschreibung war mit dem hier präsentierten Ergebnis in Übereinstimmung zu bringen. Auch im Hauptlichtsammelkomplex höherer Pflanzen LHC II konnte mittels pumpintensitätsabhängiger NLPF-spektren Delokalisation der Anregung über mindestens ein Chlì¥ÁIn the work be presented a standard theory of non-linear polarization spectroscopy in the frequency domain (NLPF) will be established. The NLPF technique based on anisotropy induced in a dye-solution, which is isotropic elsewhere, by a polarized monochromatic pump laser field. This is probed by a second laser field, which polarization direction is turned of 45 degree in respect to that of the pump. From the fundamental equations describing the non-linear response of molecular systems on electromagnetic fields, the two-dimensional NLPF spectrum is deduced for arbitrary pump-intensities. At low pump-intensities a subband analysis by NLPF has been established. This allows one to study the term scheme and energy relaxation path of molecular and supra-molecular systems by their NLPF-spectra. This includes the determination of transition-frequencies and -dipole orientations, homogeneous and inhomogeneous linewidths, as well as energy relaxation rates. Furthermore, using a self-connsistent approach, the pump-fieled dependence of the NLPF-spectrum has been deduced for the two-level system in general and also for specific multi-level systems. This method allows one to determine the oscillator strength without knowledge of the concentration, what is quite useful for studying molecular aggregates. Applications are presented to the peripheral light harvesting antenna LH2 of purple bacteria and the light harvesting complexes LHC II and CP 29 of higher plants

Proceedings of the 7th Sound and Music Computing Conference

Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010

WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments