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

Condition monitoring of wind turbine gearboxes through on-site measurement and vibration analysis techniques

Condition monitoring of gear-based mechanical systems undergoing non-stationary operation conditions is in general very challenging. In particular, this issue is remarkable as regards 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 gearbox damages is proposed: the main idea is that vibrations are measured at the tower, instead that at the gearbox. This implies that measurements can be performed without impacting on the wind turbine operation, as desirable by the point of view of wind turbine practitioners. A test case study is discussed: it deals with a wind farm owned by Renvico, featuring 6 wind turbines with 2 MW of rated power each. The vibration measurements at a wind turbine suspected to be damaged and at reference wind turbines are processed through a multivariate Novelty Detection algorithm in the feature space. The application of this algorithm is justified by univariate statistical tests on the time-domain features selected 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

Constructing mixed density functionals for describing dissociative chemisorption on metal surfaces : basic principles

Abstract: The production of a majority of chemicals involves heterogeneous catalysis at some stage, and the rates of many heterogeneously catalyzed processes are governed by transition states for dissociative chemisorption on metals. Accurate values of barrier heights for dissociative chemisorption on metals are therefore important to benchmarking electronic structure theory in general and density functionals in particular. Such accurate barriers can be obtained using the semiempirical specific reaction parameter (SRP) approach to density functional theory. However, this approach has thus far been rather ad hoc in its choice of the generic expression of the SRP functional to be used, and there is a need for better heuristic approaches to determining the mixing parameters contained in such expressions. Here we address these two issues. We investigate the ability of several mixed, parametrized density functional expressions combining exchange at the generalized gradient approximation (GGA) level with either GGA or nonlocal correlation to reproduce barrier heights for dissociative chemisorption on metal surfaces. For this, seven expressions of such mixed density functionals are tested on a database consisting of results for 16 systems taken from a recently published slightly larger database called SBH17. Three expressions are derived that exhibit high tunability and use correlation functionals that are either of the PBE GGA form or of one of two limiting nonlocal forms also describing the attractive van der Waals interaction in an approximate way. We also find that, for mixed density functionals incorporating GGA correlation, the optimum fraction of repulsive RPBE GGA exchange obtained with a specific GGA density functional is correlated with the charge-transfer parameter, which is equal to the difference in the work function of the metal surface and the electron affinity of the molecule. However, the correlation is generally not large and not large enough to obtain accurate guesses of the mixing parameter for the systems considered, suggesting that it does not give rise to a very effective search strategy

Supervisory Control and Data Acquisition Analysis for Wind Turbine Maintenance Management

Wind energy is growing to become a competitive energy source. An efficient wind turbine maintenance management is required for ensuring the reliability of the energy production and the costs reduction. Supervisory control and data acquisition system provide information about the condition of the wind turbine by signals of the different subsystems and alarm activations in case of failure or malfunction. Due to the volume and variety of the data, operators require advanced analytics to control the performance of the wind turbines and the identification and prediction of failures. The novelty proposed in this work is based on statistical analysis for analyzing supervisory control and data acquisition data to optimize the use of the data in neural networks. The first phase is the alarm analysis, quantifying the critical alarms regarding on the number and time of activation. A filtering algorithm is developed for considering only interest periods with enough range to make the study. The second phase is based on the initial data treatment, classifying alarms and signals identifying the interest time periods. Neural network is defined and trained for evaluating the signal trends, with the aim of detecting the alarm activations cause. This information will be used in the maintenance management plan for programming maintenance tasks

Digital Image Correlation Techniques for NDE and SHM

Monitoring and analyzing the integrity of structures, infrastructure, and machines is essential for economic, operational, and safety reasons. The assessment of structural integrity and dynamic conditions of those systems is important to ensure safe operation and achieve or even extend the design service life. Recent advancements in camera technology, optical sensors, and image processing algorithms have made optically based and noncontact measurement techniques such as photogrammetry and digital image correlation (DIC) appealing methods for nondestructive evaluation (NDE) and structural health monitoring (SHM). Conventional sensors (e.g., accelerometers, strain gages, string potentiometers, LVDTs) provide results only at a discrete number of points. Moreover, these sensors need wiring, can be time-consuming to install, may require additional instrumentations (e.g., power amplifiers, data acquisition), and are difficult to implement on large-sized structures without interfering with their functionality or may require instrumentation having a large number of data channels. On the contrary, optical techniques can provide accurate quantitative information about full-field displacement, strain, geometry, and the dynamics of a structure without contact or interfering with the structure’s functionality. This chapter presents a summary review of the efforts made in both academia and industry to leverage the use of DIC systems for NDE and SHM applications in the fields of civil, aerospace, and energy engineering systems. The chapter also summarizes the feasibility of the approaches and presents possible future directions of the measurement approach