Machine vibration monitoring for diagnostics through hypothesis testing

Nowadays, the subject of machine diagnostics is gathering growing interest in the research field as switching from a programmed to a preventive maintenance regime based on the real health conditions (i.e., condition-based maintenance) can lead to great advantages both in terms of safety and costs. Nondestructive tests monitoring the state of health are fundamental for this purpose. An effective form of condition monitoring is that based on vibration (vibration monitoring), which exploits inexpensive accelerometers to perform machine diagnostics. In this work, statistics and hypothesis testing will be used to build a solid foundation for damage detection by recognition of patterns in a multivariate dataset which collects simple time features extracted from accelerometric measurements. In this regard, data from high-speed aeronautical bearings were analyzed. These were acquired on a test rig built by the Dynamic and Identification Research Group (DIRG) of the Department of Mechanical and Aerospace Engineering at Politecnico di Torino. The proposed strategy was to reduce the multivariate dataset to a single index which the health conditions can be determined. This dimensionality reduction was initially performed using Principal Component Analysis, which proved to be a lossy compression. Improvement was obtained via Fisher’s Linear Discriminant Analysis, which finds the direction with maximum distance between the damaged and healthy indices. This method is still ineffective in highlighting phenomena that develop in directions orthogonal to the discriminant. Finally, a lossless compression was achieved using the Mahalanobis distance-based Novelty Indices, which was also able to compensate for possible latent confounding factors. Further, considerations about the confidence, the sensitivity, the curse of dimensionality, and the minimum number of samples were also tackled for ensuring statistical significance. The results obtained here were very good not only in terms of reduced amounts of missed and false alarms, but also considering the speed of the algorithms, their simplicity, and the full independence from human interaction, which make them suitable for real time implementation and integration in condition-based maintenance (CBM) regimes

Towards efficient neurosurgery: Image analysis for interventional MRI

Interventional magnetic resonance imaging (iMRI) is being increasingly used for performing imageguided neurosurgical procedures. Intermittent imaging through iMRI can help a neurosurgeon visualise the target and eloquent brain areas during neurosurgery and lead to better patient outcome. MRI plays an important role in planning and performing neurosurgical procedures because it can provide highresolution anatomical images that can be used to discriminate between healthy and diseased tissue, as well as identify location and extent of functional areas. This is of significant clinical utility as it helps the surgeons maximise target resection and avoid damage to functionally important brain areas. There is clinical interest in propagating the pre-operative surgical information to the intra-operative image space as this allows the surgeons to utilise the pre-operatively generated surgical plans during surgery. The current state of the art neuronavigation systems achieve this by performing rigid registration of pre-operative and intra-operative images. As the brain undergoes non-linear deformations after craniotomy (brain shift), the rigidly registered pre-operative images do not accurately align anymore with the intra-operative images acquired during surgery. This limits the accuracy of these neuronavigation systems and hampers the surgeon’s ability to perform more aggressive interventions. In addition, intra-operative images are typically of lower quality with susceptibility artefacts inducing severe geometric and intensity distortions around areas of resection in echo planar MRI images, significantly reducing their utility in the intraoperative setting. This thesis focuses on development of novel methods for an image processing workflow that aims to maximise the utility of iMRI in neurosurgery. I present a fast, non-rigid registration algorithm that can leverage information from both structural and diffusion weighted MRI images to localise target lesions and a critical white matter tract, the optic radiation, during surgical management of temporal lobe epilepsy. A novel method for correcting susceptibility artefacts in echo planar MRI images is also developed, which combines fieldmap and image registration based correction techniques. The work developed in this thesis has been validated and successfully integrated into the surgical workflow at the National Hospital for Neurology and Neurosurgery in London and is being clinically used to inform surgical decisions

Wind turbine drive-train condition monitoring through tower vibrations measurement and processing

A new method for wind turbine drive-train condition monitoring is proposed: the innovative idea is that vibrations are measured at the tower. The critical point is extracting knowledge about the drive-train from tower measurements: this is achieved by measuring simultaneously at the highest possible number of nearby wind turbines. One wind turbine is selected as target and the others are used as reference. The data are analyzed in the time domain basing on statistical features (root mean square, peak, crest factor, skewness, kurtosis). The data set in the feature space reduces to a matrix, from which the observations at the target wind turbine should be distinguishable. The application of this algorithm is supported by univariate statistical tests and by Principal Component Analysis. A novelty index based on the Mahalanobis distance is finally used to detect the statistical novelty of the damaged wind turbine. This work is based on field measurement campaigns, performed by the authors in 2018 and 2019 at wind farms owned by the Renvico company

Diagnosis of faulty wind turbine bearings using tower vibration measurements †

Condition monitoring of gear-based mechanical systems in non-stationary operation conditions is in general very challenging. This issue is particularly important for wind energy technology because most of the modern wind turbines are geared and gearbox damages account for at least the 20% of their unavailability time. In this work, a new method for the diagnosis of drive-train bearings damages is proposed: the general idea is that vibrations are measured at the tower instead of at the gearbox. This implies that measurements can be performed without impacting the wind turbine operation. The test case considered in this work is a wind farm owned by the Renvico company, featuring six wind turbines with 2 MW of rated power each. A measurement campaign has been conducted in winter 2019 and vibration measurements have been acquired at five wind turbines in the farm. The rationale for this choice is that, when the measurements have been acquired, three wind turbines were healthy, one wind turbine had recently recovered from a planetary bearing fault, and one wind turbine was undergoing a high speed shaft bearing fault. The healthy wind turbines are selected as references and the damaged and recovered are selected as targets: vibration measurements are processed through a multivariate Novelty Detection algorithm in the feature space, with the objective of distinguishing the target wind turbines with respect to the reference ones. The application of this algorithm is justified by univariate statistical tests on the selected time-domain features and by a visual inspection of the data set via Principal Component Analysis. Finally, a novelty index based on the Mahalanobis distance is used to detect the anomalous conditions at the damaged wind turbine. The main result of the study is that the statistical novelty of the damaged wind turbine data set arises clearly, and this supports that the proposed measurement and processing methods are promising for wind turbine condition monitoring

Generation of thermo-sensitive allele of the TPR like protein Nup211by PCR mutagenesis

Motivation: TPR proteins are conserved large coiled-coil proteins that localize at the nucleoplasmic side of the nuclear pore complex and participate in multiple aspects of DNA metabolism. The protein Nup211, fission yeast homolog of Mlp1/Mlp2/Tpr, participate in the mRNA export and is essential for vegetative growth. The aim of this work is to create a collection of thermo-sensitive alleles of nup211.Methods: To create the collection, we have generated a new strain with the nup211 gen tagged with GFP at the amino terminal extreme and confirmed by fluorescent microscopy that the protein Nup211 localized in the nuclear envelop. Then, we have carried out a Taq PCR-based Random Mutagenesis with reduced concentration of dATP. The PCR products were transformed into a wild type strain to generate conditional mutants. The transformants obtained whose growth was impaired at 36ºC were preselected as thermo-sensitive mutants. To confirm the growth deficiency of these clones, a drop assay was performed and the best candidates were selected. These thermo-sensitive mutants were cultivated at 25ºC as well as 36ºC and both cultures were subjected to various experiments in order to study any changes in the localization of Nup211.Results: Up to now, we have demonstrated by fluorescent microscopy that the thermo-sensitive mutants show a modified nuclear distribution of Nup211 and different cellular phenotype, suggesting that the differents clones might represent differents nup211 thermo-sensitive alleles. These alleles are going to be subjected to various experiments to clarify the role of the protein in the mRNA export

The MicroBioDiverSar Project: Exploring the Microbial Biodiversity in Ex Situ Collections of Sardinia

In the last decades, biodiversity preservation has gained growing attention and many strategies, laws and regulations have been enacted by governments with this purpose. The Micro-BioDiverSar (MBDS) project, the first one regarding microbiological resources, funded by the Italian Minister of Agricultural, Food and Forestry Policies (Mipaaf) through the Law 194/2015, was aimed at surveying, cataloguing, and managing the microbial resources and the related information of three Sardinian collections (Agris BNSS, Uniss, and Unica). While microorganisms were reordered and inventoried, a federated database, accessible via the web, was designed by the bioinformatician of Ospedale Policlinico San Martino of Genova, according to both international standards and laboratory needs. The resulting MBDS collection boasts a great richness of microbial resources. Indeed, over 21,000 isolates, belonging to over 200 species of bacteria, yeasts, and filamentous fungi isolated from different matrices, mainly food, of animal and vegetable origin, collected in over 50 years, were included in the database. Currently, about 2000 isolates, belonging to 150 species, are available online for both the scientific community and agri-food producers. The huge work done allowed one to know the consistency and the composition of most of the patrimony of the Sardinian microbial collections. Furthermore, the MBDS database has been proposed as a model for other Italian collections that, as the MBDS partners, are part of the Joint Research UnitMIRRI-IT Italian collections network, with the aim of overcoming fragmentation, facing sustainability challenges, and improving the quality of the management of the collections

Optic radiation tractography and vision in anterior temporal lobe resection.

Anterior temporal lobe resection (ATLR) is an effective treatment for refractory temporal lobe epilepsy but may result in a contralateral superior visual field deficit (VFD) that precludes driving in the seizure-free patient. Diffusion tensor imaging (DTI) tractography can delineate the optic radiation preoperatively and stratify risk. It would be advantageous to incorporate display of tracts into interventional magnetic resonance imaging (MRI) to guide surgery