Investigating parallel multi-step vibration processing pipelines for planetary stage fault detection in wind turbine drivetrains

This paper proposes a signal processing approach for wind turbine gearbox vibration signals based on employing multiple analysis pipelines. These so-called pipelines consist of combinations of various advanced signal processing methods that have been proven to be effective in literature when applied to wind turbine vibration signals. The performance of the pipelines is examined on vibration data containing different wind turbine gearbox faults in the planetary stages. Condition indicators are extracted from every pipeline to evaluate the fault detection capability for such incipient failures. The results indicate that the multipronged approach with the different pipelines increases the reliability of successfully detecting incipient planetary stage gearbox faults. The type, location, and severity of the fault influences the choice for the appropriate processing method combination. It is therefore often insufficient to only utilize a single processing pipeline for vibration analysis of wind turbine gearbox faults. Besides investigating the performance of the different processing techniques, the main outcome and recommendation of this paper is thus to employ a diversified analysis methodology which is not limited to a sole method combination, to improve the early detection rate of planetary stage gearbox faults

Genomics of an endemic cystic fibrosis Burkholderia multivorans strain reveals low within-patient evolution but high between-patient diversity

In many countries, Burkholderia multivorans is the most prevalent species within the Burkholderia cepacia complex (Bcc) found infecting the lungs of patients with cystic fibrosis (CF). Its positive identification is of immediate concern to the health of the patient as it is notoriously hard to eradicate using antibiotics and can cause necrosis of the lung tissues (cepacia syndrome). Infection control measures reduced the prevalence of B. cenocepacia in CF wards, but patients continue to acquire infections by B. multivorans from environmental sources. In most reported cases, the infecting strains are unique except in rare cases in which cross-infection is observed between patients. We report here an endemic strain of B. multivorans with sequence type ST-742 that has been infecting multiple patients, without evidence for cross-infection. We investigated the epidemiology and genomics of this ST-742 strain and show that it is microdiverse, as isolates between-patients exhibit numerous genomic differences, at scales that have not been observed previously when looking at evolutionary trajectories within-patients. Additionally, we found that the specific genomic background of a given strain may dictate the strategy of adaptation within the CF lung. Burkholderia multivorans is a member of the Burkholderia cepacia complex (Bcc), notorious for its pathogenicity in persons with cystic fibrosis. Epidemiological surveillance suggests that patients predominantly acquire B. multivorans from environmental sources, with rare cases of patient-to-patient transmission. Here we report on the genomic analysis of thirteen isolates from an endemic B. multivorans strain infecting four cystic fibrosis patients treated in different pediatric cystic fibrosis centers in Belgium, with no evidence of cross-infection. All isolates share an identical sequence type (ST-742) but whole genome analysis shows that they exhibit peculiar patterns of genomic diversity between patients. By combining short and long reads sequencing technologies, we highlight key differences in terms of small nucleotide polymorphisms indicative of low rates of adaptive evolution within patient, and well-defined, hundred Kbps-long segments of high enrichment in mutations between patients. In addition, we observed large structural genomic variations amongst the isolates which revealed different plasmid contents, active roles for transposase IS3 and IS5 in the deactivation of genes, and mobile prophage elements. Our study shows limited within-patient B. multivorans evolution and high between-patient strain diversity, indicating that an environmental microdiverse reservoir must be present for this endemic strain, in which active diversification is taking place. Furthermore, our analysis also reveals a set of 30 parallel adaptations across multiple patients, indicating that the specific genomic background of a given strain may dictate the route of adaptation within the cystic fibrosis lung

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

Traitement avancé du signal pour l'identification et le diagnostic de l'état des machines tournantes

This Ph.D. dissertation targets innovative methods for vibration-based condition monitoring of rotating machinery. Substantial benefits can be achieved from an economical and a safety point of view using condition monitoring. One of the most popular methods to gather information about the state of machine parts is through the analysis of machine vibrations. Most of these vibrations are directly linked to periodical behavior of subsystems within the machine like e.g. rotating shafts, gears, rotating electrical fields, etc. This knowledge can be exploited to enable faultdependent processing schemes. This dissertation investigates how to implement and utilize these processing schemes and details the steps in such a procedure. Typically, the first prerequisite for advanced analysis is the availability of the instantaneous rotation speed. This speed needs to be known since most frequency-based analysis techniques assume stationary behavior. Knowledge of the speed thus allows for compensating speed fluctuations, for example through angular resampling of the vibration signal. While there are hardware-based solutions for speed estimation using angle encoders or tachometers, this thesis investigates the potential in vibration signals for speed estimation. After speed estimation and angular resampling, a common next step is to separate the signal into deterministic and stochastic components. The cepstrum editing procedure is examined for its efficacy and applicability. Afterwards, different filtering methods are inspected as to improve the signal-to-noise ratio of the signal content of interest. Existing methods using conventional criteria are investigated together with a novel blind filtering methodology. The final step in the multi-step processing scheme is to search for the potential fault. Statistical indicators can be calculated on the processed time domain signal and tracked over time to check for increases. In many cases, the fault signature exhibits cyclostationary behavior. Therefore this dissertation also examines different cyclostationary analysis techniques. Lastly, the performance of the different processing methods is validated on two experimental vibration data sets of wind turbine gearboxes.Cette thèse porte sur des méthodes innovantes de contrôle de l'état de santé des machines tournantes par l’analyse des signaux vibratoires. En effet, la surveillance de l’état de santé des machines contribue à des améliorations substantielles des points de vue économique et de sureté. Afin d’y aboutir, l’une des manières les plus populaires est de recueillir les vibrations de la machine. La plupart de ces vibrations sont directement liées au comportement périodique des sous-systèmes de la machine tels que les arbres de rotation, engrenages, champs électriques rotationnels, etc. Cette connaissance peut être exploitée afin de concevoir une méthodologie adaptée à chaque type de défaut. Cette thèse s’intéresse aux étapes de la mise en œuvre de cette méthodologie. En règle générale, la première condition préalable à l’analyse avancée de l’information récoltée est la disponibilité de la vitesse instantanée de rotation. Cette vitesse doit être connue car la plupart des techniques du traitement du signal sont adaptées aux conditions de fonctionnement stationnaires. Ainsi, la connaissance de la vitesse permettra de compenser les fluctuations de vitesse, par exemple par le ré-échantillonnage angulaire du signal de vibration. Malgré l’existence d’outils de mesure permettant l’estimation de la vitesse tels que les codeurs et les tachymètres, cette thèse étudie le potentiel d’estimer la vitesse instantanée de rotation à partir des signaux vibratoires. Après l'estimation de la vitesse et le ré-échantillonnage angulaire, une étape suivante courante consiste à séparer le signal en composantes déterministes et stochastiques. Dans ce sens, l’efficacité et l’applicabilité de la procédure d'édition du cepstre sont analysées. Ensuite, différentes méthodes de filtrage sont appliquées au signal résiduel afin d’améliorer le rapport signal sur bruit. Pour cette fin, les méthodes existantes utilisant des critères conventionnels sont étudiées en parallèles avec une nouvelle méthodologie aveugle de filtrage. La dernière étape du processus de traitement consiste à diagnostiquer le défaut potentiel. Ainsi, des indicateurs statistiques sont calculés sur le signal obtenu après traitement et suivis dans le temps pour vérifier leurs variations. Dans de nombreux cas, la signature du défaut présente un comportement cyclostationaire. Par conséquent, cette thèse examine également différentes techniques d'analyse de la cyclostationarité. Enfin, les performances des différentes méthodes de traitement sont validées sur deux ensembles de données expérimentales de vibrations issues de boîtes de vitesses d’éoliennes

Blind filters based on envelope spectrum sparsity indicators for bearing and gear vibration-based condition monitoring

International audienc

Historique du survol de la Région de Bruxelles-Capitale

info:eu-repo/semantics/publishe

Cerebral venous thrombosis revealing Behçet's disease in a Moroccan patient: A case report and literature review.

Behçet's disease is an inflammatory disease, the origin of which still remains unclear, and it has multiple , one of them being thrombosis. In this report, we describe the case of a 24-year-old Moroccan patient who presented with headache persisting for more than 2 weeks, which was found to be caused by cerebral venous sinus thrombosis. His medical history of recurrent oral and genital ulcerations, epididymitis and one episode of pericarditis led to the diagnosis of Behçet's disease. We could observe an almost complete relief of symptoms with colchicine therapy, and anticoagulation with warfarin was started for secondary prevention of thrombosis

Multi-harmonic phase demodulation method for instantaneous angular speed estimation using harmonic weighting

International audienc

Are chilblains a skin expression of COVID-19 microangiopathy?

The unexpected outbreak of chilblains during the current coronavirus disease 2019 (COVID-19) pandemic has been causally linked to severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) infection (1). The pathophysiology of these chilblains observed in otherwise healthy patients is widely discussed