Vestas V90-3MW Wind Turbine Gearbox Health Assessment Using a Vibration-Based Condition Monitoring System

Reliable monitoring for the early fault diagnosis of gearbox faults is of great concern for the wind industry.This paper presents a novel approach for health condition monitoring (CM) and fault diagnosis in wind turbine gearboxes using vibration analysis. This methodology is based on amachine learning algorithm that generates a baseline for the identification of deviations fromthe normal operation conditions of the turbine and the intrinsic characteristic-scale decomposition (ICD) method for fault type recognition. Outliers picked up during the baseline stage are decomposed by the ICD method to obtain the product components which reveal the fault information.The new methodology proposed for gear and bearing defect identification was validated by laboratory and field trials, comparing well with the methods reviewed in the literature

Destructive Testing of Open Rotor Propeller Blades for Extreme Operation Conditions

Due to the complexity of composite material, accurate manufacturing is very complicated and carry over a large number of uncertainties, the process of optimization and In this paper as part of design technology for rotor blades, the process of mechanical testing against severe working conditions using Acoustic Emission (AE) is discussed. As part of this process, the dynamic behaviour is analysed with validation and model updating of the structure and material properties, followed by structural tests under loads specific to the operational conditions. The results presented show how the appropriate AE technique has been implemented in order to obtain relevant data and focus on specific areas of interest for the design development, for correlation between modelling and experimental testing, the structure being tested to the ultimate load

A pattern recognition approach to acoustic emission data originating from fatigue of wind turbine blades

© 2017 by the authors. The identification of particular types of damage in wind turbine blades using acoustic emission (AE) techniques is a significant emerging field. In this work, a 45.7-m turbine blade was subjected to flap-wise fatigue loading for 21 days, during which AE was measured by internally mounted piezoelectric sensors. This paper focuses on using unsupervised pattern recognition methods to characterize different AE activities corresponding to different fracture mechanisms. A sequential feature selection method based on a k-means clustering algorithm is used to achieve a fine classification accuracy. The visualization of clusters in peak frequency−frequency centroid features is used to correlate the clustering results with failure modes. The positions of these clusters in time domain features, average frequency−MARSE, and average frequency−peak amplitude are also presented in this paper (where MARSE represents the Measured Area under Rectified Signal Envelope). The results show that these parameters are representative for the classification of the failure modes

The benefits of long range ultrasonic sensors for the efficient InLine inspection and corrosion monitoring of previously Non-piggable or hard to reach pipelines

One third of all pipelines worldwide are considered un-piggable by the widely used existing Smart pigs. The vast majority of buried oil pipelines in Europe carry hazardous fluids at high pressure and temperature. While the most common type of InLine Inspection (ILI) pigs use magnetic flux leakage (MFL) techniques. Several limitations of this approach have been identified such as its effectiveness in distinguishing acceptable anomalies from defects, or determining whether the indication is on the external surface or internal as well as the signal reading when the pipe is encased by steel conduits, which is often the case through road and rail crossings. The other common technique used for the purpose is that of Ultrasonic inspection pigs. In this case the process of covering the whole pipeline length with Ultrasonic scan inspection would be both exhaustively time-consuming as well as impractical in sheer data volume to analyse and interpret even at the age of the IOT. iPIM research program is bringing the idea of using Long Range Ultrasonic Guided Waves for a pigging system that is a permanent, reliable, manageable and energy efficient solution to pipeline monitoring. A permanent network of novel low profile Long Range Ultrasonic (LRU) sensors will incorporate on-board signal processing capabilities

Acoustic emission monitoring of fatigue crack growth in mooring chains

Offshore installations are subject to perpetual fatigue loading and are usually very hard to inspect. Close visual inspection from the turret is usually too hazardous for divers and is not possible with remotely operated vehicles (ROVs) because of the limited access. Conventional nondestructive techniques (NDTs) have been used in the past to carry out inspections of mooring chains, floating production storage and offloading systems (FPSOs), and other platforms. Although these have been successful at detecting and assessing fatigue cracks, the hazardous nature of the operations calls for remote techniques that could be applied continuously to identify damage initiation and progress. The aim of the present work is to study the capabilities of acoustic emission (AE) as a monitoring tool to detect fatigue crack initiation and propagation in mooring chains. A 72-day large-scale experiment was designed for this purpose. A detailed analysis of the different AE signal time domain features was not conclusive, possibly due to the high level of noise. However, the frequency content of the AE signals offers a promising indication of fatigue crack growth.TWI's JIP projec

Validated AE application for continuous monitoring of the structural condition of the supporting structure of offshore wind turbines

It is widely accepted that the use of acoustic emission technology offers unique advantages for the inspection and structural health monitoring of structures. Even though this method is quite evolved, there are still challenges in its application largely due to its complex behaviour and there are also other factors to consider such as numerous possible vibrational modes, dispersion, attenuation, noise and multiple reflections etc. There are also challenges in the use of this technology for offshore structures especially when the structures are totally submerged in water and when there are different materials used for construction. In order to address these issues and determine the applicability of these methods, a study has been carried out that uses finite element analysis and an experimental study to understand their behaviour in an offshore wind turbine monopole has also been conducted.,

RETRATO DE MUJER [Material gráfico]

Copia digital. Madrid : Ministerio de Educación, Cultura y Deporte, 201

A Critical Review on the Structural Health Monitoring Methods of the Composite Wind Turbine Blades

With increasing turbine size, monitoring of blades becomes increasingly im-portant, in order to prevent catastrophic damages and unnecessary mainte-nance, minimize the downtime and labor cost and improving the safety is-sues and reliability. The present work provides a review and classification of various structural health monitoring (SHM) methods as strain measurement utilizing optical fiber sensors and Fiber Bragg Gratings (FBG’s), active/ pas-sive acoustic emission method, vibration‒based method, thermal imaging method and ultrasonic methods, based on the recent investigations and prom-ising novel techniques. Since accuracy, comprehensiveness and cost-effectiveness are the fundamental parameters in selecting the SHM method, a systematically summarized investigation encompassing methods capabilities/ limitations and sensors types, is needed. Furthermore, the damages which are included in the present work are fiber breakage, matrix cracking, delamina-tion, fiber debonding, crack opening at leading/ trailing edge and ice accre-tion. Taking into account the types of the sensors relevant to different SHM methods, the advantages/ capabilities and disadvantages/ limitations of repre-sented methods are nominated and analyzed

Molecular study of the perforin gene in familial hematological malignancies

Perforin gene (PRF1) mutations have been identified in some patients diagnosed with the familial form of hemophagocytic lymphohistiocytosis (HLH) and in patients with lymphoma. The aim of the present study was to determine whether patients with a familial aggregation of hematological malignancies harbor germline perforin gene mutations. For this purpose, 81 unrelated families from Tunisia and France with aggregated hematological malignancies were investigated. The variants detected in the PRF1 coding region amounted to 3.7% (3/81). Two of the three variants identified were previously described: the p.Ala91Val pathogenic mutation and the p.Asn252Ser polymorphism. A new p.Ala 211Val missense substitution was identified in two related Tunisian patients. In order to assess the pathogenicity of this new variation, bioinformatic tools were used to predict its effects on the perforin protein structure and at the mRNA level. The segregation of the mutant allele was studied in the family of interest and a control population was screened. The fact that this variant was not found to occur in 200 control chromosomes suggests that it may be pathogenic. However, overexpression of mutated PRF1 in rat basophilic leukemia cells did not affect the lytic function of perforin differently from the wild type protein

An Ontological Approach to Inform HMI Designs for Minimizing Driver Distractions with ADAS

ADAS (Advanced Driver Assistance Systems) are in-vehicle systems designed to enhance driving safety and efficiency as well as comfort for drivers in the driving process. Recent studies have noticed that when Human Machine Interface (HMI) is not designed properly, an ADAS can cause distraction which would affect its usage and even lead to safety issues. Current understanding of these issues is limited to the context-dependent nature of such systems. This paper reports the development of a holistic conceptualisation of how drivers interact with ADAS and how such interaction could lead to potential distraction. This is done taking an ontological approach to contextualise the potential distraction, driving tasks and user interactions centred on the use of ADAS. Example scenarios are also given to demonstrate how the developed ontology can be used to deduce rules for identifying distraction from ADAS and informing future designs